Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery (http://machinelearningmastery.com/)

Dataset Used: Pima Indians Diabetes Database Data Set ML Model: Classification with numerical attributes Dataset Reference: https://www.kaggle.com/uciml/pima-indians-diabetes-database

For more information on this case study project, please consult Dr. Brownlee’s blog post at https://machinelearningmastery.com/standard-machine-learning-datasets/.

For more information on performance benchmarks, please consult: https://www.kaggle.com/uciml/pima-indians-diabetes-database

INTRODUCTION: The Pima Indians Diabetes Dataset involves predicting the onset of diabetes within 5 years in Pima Indians given medical details. It is a binary (2-class) classification problem. There are 768 observations with 8 input variables and 1 output variable. Missing values are believed to be encoded with zero values.

CONCLUSION: The baseline performance of predicting the class variable achieved an average accuracy of 75.85%. The top accuracy result achieved via Logistic Regression was 77.73% after a series of tuning trials. The ensemble algorithms, in this case, did not yield a better result than the non-ensemble algorithms to justify the additional processing required.

The purpose of this project is to analyze a dataset using various machine learning algorithms and to document the steps using a template. The project aims to touch on the following areas:

  1. Document a predictive modeling problem end-to-end.
  2. Explore data transformation options for improving model performance
  3. Explore non-ensemble and ensemble algorithms for improving model performance
  4. Explore algorithm tuning techniques for improving model performance

Working through machine learning problems from end-to-end requires a structured modeling approach. Working problems through a project template can encourage you to think about the problem more critically, to challenge your assumptions, and to get good at all parts of a modeling project.

Any predictive modeling machine learning project can be broken down into about 6 common tasks:

  1. Define Problem
  2. Summarize Data
  3. Prepare Data
  4. Evaluate Algorithms
  5. Improve Accuracy or Results
  6. Finalize Model and Present Results

1. Prepare Problem

1.a) Load libraries

library(mlbench)
library(caret)
## Warning: package 'caret' was built under R version 3.4.4
## Loading required package: lattice
## Loading required package: ggplot2
library(corrplot)
## corrplot 0.84 loaded

1.b) Load dataset

seedNum <- 888
data(PimaIndiansDiabetes)
dataset <- PimaIndiansDiabetes
# Rename the class column to a standard label
colnames(dataset)[9] <- "classVar"

1.c) Split-out validation dataset

Normally, we would create a training (variable name “dataset”) and a validation (variable name “validation”) dataset. Because this dataset is relatively small, we will opt to test the algorithms with the full set of data and not splitting.

# Not applicable for this iteration of the project.

2. Summarize Data

To gain a better understanding of the data that we have on-hand, we will leverage a number of descriptive statistics and data visualization techniques. The plan is to use the results to consider new questions, review assumptions, and validate hypotheses that we can investigate later with specialized models.

2.a) Descriptive statistics

2.a.i) Dimensions of the dataset.

dim(dataset)
## [1] 768   9

2.a.ii) Types of the attributes.

sapply(dataset, class)
##  pregnant   glucose  pressure   triceps   insulin      mass  pedigree 
## "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" 
##       age  classVar 
## "numeric"  "factor"

2.a.iii) Peek at the data itself.

head(dataset)
##   pregnant glucose pressure triceps insulin mass pedigree age classVar
## 1        6     148       72      35       0 33.6    0.627  50      pos
## 2        1      85       66      29       0 26.6    0.351  31      neg
## 3        8     183       64       0       0 23.3    0.672  32      pos
## 4        1      89       66      23      94 28.1    0.167  21      neg
## 5        0     137       40      35     168 43.1    2.288  33      pos
## 6        5     116       74       0       0 25.6    0.201  30      neg

2.a.iv) Summarize the levels of the class attribute.

x <- dataset[,1:8]
y <- dataset[,9]
cbind(freq=table(y), percentage=prop.table(table(y))*100)
##     freq percentage
## neg  500   65.10417
## pos  268   34.89583

2.a.v) Summarize correlations between input variables.

cor(x)
##             pregnant    glucose   pressure     triceps     insulin
## pregnant  1.00000000 0.12945867 0.14128198 -0.08167177 -0.07353461
## glucose   0.12945867 1.00000000 0.15258959  0.05732789  0.33135711
## pressure  0.14128198 0.15258959 1.00000000  0.20737054  0.08893338
## triceps  -0.08167177 0.05732789 0.20737054  1.00000000  0.43678257
## insulin  -0.07353461 0.33135711 0.08893338  0.43678257  1.00000000
## mass      0.01768309 0.22107107 0.28180529  0.39257320  0.19785906
## pedigree -0.03352267 0.13733730 0.04126495  0.18392757  0.18507093
## age       0.54434123 0.26351432 0.23952795 -0.11397026 -0.04216295
##                mass    pedigree         age
## pregnant 0.01768309 -0.03352267  0.54434123
## glucose  0.22107107  0.13733730  0.26351432
## pressure 0.28180529  0.04126495  0.23952795
## triceps  0.39257320  0.18392757 -0.11397026
## insulin  0.19785906  0.18507093 -0.04216295
## mass     1.00000000  0.14064695  0.03624187
## pedigree 0.14064695  1.00000000  0.03356131
## age      0.03624187  0.03356131  1.00000000

2.a.vi) Statistical summary of all attributes.

summary(dataset)
##     pregnant         glucose         pressure         triceps     
##  Min.   : 0.000   Min.   :  0.0   Min.   :  0.00   Min.   : 0.00  
##  1st Qu.: 1.000   1st Qu.: 99.0   1st Qu.: 62.00   1st Qu.: 0.00  
##  Median : 3.000   Median :117.0   Median : 72.00   Median :23.00  
##  Mean   : 3.845   Mean   :120.9   Mean   : 69.11   Mean   :20.54  
##  3rd Qu.: 6.000   3rd Qu.:140.2   3rd Qu.: 80.00   3rd Qu.:32.00  
##  Max.   :17.000   Max.   :199.0   Max.   :122.00   Max.   :99.00  
##     insulin           mass          pedigree           age       
##  Min.   :  0.0   Min.   : 0.00   Min.   :0.0780   Min.   :21.00  
##  1st Qu.:  0.0   1st Qu.:27.30   1st Qu.:0.2437   1st Qu.:24.00  
##  Median : 30.5   Median :32.00   Median :0.3725   Median :29.00  
##  Mean   : 79.8   Mean   :31.99   Mean   :0.4719   Mean   :33.24  
##  3rd Qu.:127.2   3rd Qu.:36.60   3rd Qu.:0.6262   3rd Qu.:41.00  
##  Max.   :846.0   Max.   :67.10   Max.   :2.4200   Max.   :81.00  
##  classVar 
##  neg:500  
##  pos:268  
##           
##           
##           
## 

2.b) Data visualizations

2.b.i) Univariate plots to better understand each attribute.

# boxplots for each attribute
par(mfrow=c(2,4))
for(i in 1:8) {
    boxplot(dataset[,i], main=names(dataset)[i])
}

# histograms each attribute
par(mfrow=c(2,4))
for(i in 1:8) {
    hist(dataset[,i], main=names(dataset)[i])
}

# density plot for each attribute
par(mfrow=c(2,4))
for(i in 1:8) {
    plot(density(dataset[,i]), main=names(dataset)[i])
}

2.b.ii) Multivariate plots to better understand the relationships between attributes

# scatterplot matrix colored by class
pairs(classVar~., data=dataset, col=dataset$classVar)

# box and whisker plots for each attribute by class
scales <- list(x=list(relation="free"), y=list(relation="free"))
featurePlot(x=x, y=y, plot="box", scales=scales)

# density plots for each attribute by class value
featurePlot(x=x, y=y, plot="density", scales=scales)

# correlation plot
correlations <- cor(x)
corrplot(correlations, method="circle")

3. Prepare Data

Some dataset may require additional preparation activities that will best exposes the structure of the problem and the relationships between the input attributes and the output variable. Some data-prep tasks might include:

3.a) Data Cleaning

# Not applicable for this iteration of the project.
# Mark missing values
# invalid <- 0
# dataset$pressure[dataset$pressure==invalid] <- NA
# dataset$mass[dataset$mass==invalid] <- NA
# dataset$glucose[dataset$glucose==invalid] <- NA
# # Impute missing values
# dataset$pressure <- with(dataset, impute(pressure, mean))
# dataset$mass <- with(dataset, impute(mass, mean))
# dataset$glucose <- with(dataset, impute(glucose, mean))

3.b) Feature Selection

# Not applicable for this iteration of the project.

3.c) Data Transforms

# Not applicable for this iteration of the project.

4. Evaluate Algorithms

After the data-prep, we next work on finding a workable model by evaluating a subset of machine learning algorithms that are good at exploiting the structure of the dataset. The typical evaluation tasks include:

For this project, we will evaluate four non-ensemble and three ensemble algorithms:

Non-Ensemble Algorithms: Logistic Regression, Linear Discriminant Analysis, Decision Trees (CART), Naive Bayes, k-Nearest Neighbors, and Support Vector Machine

Ensemble Algorithms: Bagged CART, Random Forest, Adaboost, Stochastic Gradient Boosting, and Neural Networks

The random number seed is reset before each run to ensure that the evaluation of each algorithm is performed using the same data splits. It ensures the results are directly comparable.

4.a) Test options and evaluation metric

# Run algorithms using 10-fold cross validation
control <- trainControl(method="repeatedcv", number=10, repeats=3)
metricTarget <- "Accuracy"

4.b) Generate Models using Non-Ensemble Algorithms

# Linear Regression (Regression)
# Algorithm not applicable for this project
#set.seed(seedNum)
#fit.lm <- train(classVar~., data=dataset, method="lm", metric=metricTarget, trControl=control)
# Logistic Regression (Regression/Classification)
set.seed(seedNum)
fit.glm <- train(classVar~., data=dataset, method="glm", metric=metricTarget, trControl=control)
# Linear/Quadratic Discriminant Analysis (Regression/Classification)
set.seed(seedNum)
fit.lda <- train(classVar~., data=dataset, method="lda", metric=metricTarget, trControl=control)
# Decision Tree - CART (Regression/Classification)
set.seed(seedNum)
fit.cart <- train(classVar~., data=dataset, method="rpart", metric=metricTarget, trControl=control)
# Naive Bayes (Classification)
set.seed(seedNum)
fit.nb <- train(classVar~., data=dataset, method="nb", metric=metricTarget, trControl=control)
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## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 1
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 2
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 3
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 4
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 5
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 6
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 7
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 8
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 9
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 10
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 11
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 12
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 13
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 14
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 15
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 16
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 17
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 18
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 19
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 20
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 21
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 22
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 23
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 24
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 25
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 26
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 27
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 28
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 29
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 30
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 31
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 32
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 33
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 34
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 35
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 36
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 37
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 38
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 39
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 40
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 41
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 42
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 43
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 44
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 45
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 46
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 47
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 48
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 49
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 50
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 51
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 52
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 53
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 54
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 55
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 56
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 57
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 58
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 59
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 60
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 61
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 62
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 63
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 64
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 65
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 66
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 67
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 68
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 69
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 70
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 71
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 72
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 73
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 74
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 75
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 76
## Warning in FUN(X[[i]], ...): Numerical 0 probability for all classes with
## observation 77
# k-Nearest Neighbors (Regression/Classification)
set.seed(seedNum)
fit.knn <- train(classVar~., data=dataset, method="knn", metric=metricTarget, trControl=control)
# Support Vector Machine (Classification)
set.seed(seedNum)
fit.svm <- train(classVar~., data=dataset, method="svmRadial", metric=metricTarget, trControl=control)

4.C) Generate Models using Ensemble Algorithms

In this section, we will explore the use and tuning of ensemble algorithms to see whether we can improve the results.

# Bagged CART (Regression/Classification)
set.seed(seedNum)
fit.bagcart <- train(classVar~., data=dataset, method="treebag", metric=metricTarget, trControl=control)
# Random Forest (Regression/Classification)
set.seed(seedNum)
fit.rf <- train(classVar~., data=dataset, method="rf", metric=metricTarget, trControl=control)
# AdaBoost (Classification)
set.seed(seedNum)
fit.ada <- train(classVar~., data=dataset, method="adaboost", metric=metricTarget, trControl=control)
# Stochastic Gradient Boosting (Regression/Classification)
set.seed(seedNum)
fit.gbm <- train(classVar~., data=dataset, method="gbm", metric=metricTarget, trControl=control)
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2581             nan     0.1000    0.0172
##      2        1.2321             nan     0.1000    0.0124
##      3        1.2023             nan     0.1000    0.0127
##      4        1.1823             nan     0.1000    0.0065
##      5        1.1643             nan     0.1000    0.0073
##      6        1.1462             nan     0.1000    0.0086
##      7        1.1306             nan     0.1000    0.0065
##      8        1.1186             nan     0.1000    0.0048
##      9        1.1019             nan     0.1000    0.0071
##     10        1.0876             nan     0.1000    0.0056
##     20        0.9970             nan     0.1000    0.0020
##     40        0.9149             nan     0.1000    0.0001
##     60        0.8730             nan     0.1000   -0.0007
##     80        0.8509             nan     0.1000   -0.0013
##    100        0.8340             nan     0.1000   -0.0004
##    120        0.8213             nan     0.1000   -0.0010
##    140        0.8106             nan     0.1000   -0.0007
##    150        0.8061             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2494             nan     0.1000    0.0213
##      2        1.2109             nan     0.1000    0.0177
##      3        1.1751             nan     0.1000    0.0159
##      4        1.1486             nan     0.1000    0.0076
##      5        1.1177             nan     0.1000    0.0116
##      6        1.0928             nan     0.1000    0.0090
##      7        1.0722             nan     0.1000    0.0059
##      8        1.0557             nan     0.1000    0.0061
##      9        1.0408             nan     0.1000    0.0057
##     10        1.0272             nan     0.1000    0.0044
##     20        0.9292             nan     0.1000    0.0002
##     40        0.8541             nan     0.1000   -0.0006
##     60        0.8189             nan     0.1000   -0.0023
##     80        0.7855             nan     0.1000   -0.0009
##    100        0.7579             nan     0.1000   -0.0030
##    120        0.7368             nan     0.1000   -0.0012
##    140        0.7188             nan     0.1000   -0.0008
##    150        0.7122             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2420             nan     0.1000    0.0225
##      2        1.1925             nan     0.1000    0.0198
##      3        1.1573             nan     0.1000    0.0170
##      4        1.1247             nan     0.1000    0.0132
##      5        1.0941             nan     0.1000    0.0133
##      6        1.0679             nan     0.1000    0.0083
##      7        1.0437             nan     0.1000    0.0087
##      8        1.0244             nan     0.1000    0.0093
##      9        1.0066             nan     0.1000    0.0054
##     10        0.9889             nan     0.1000    0.0052
##     20        0.8968             nan     0.1000   -0.0009
##     40        0.8101             nan     0.1000   -0.0019
##     60        0.7625             nan     0.1000   -0.0019
##     80        0.7237             nan     0.1000   -0.0022
##    100        0.6885             nan     0.1000   -0.0015
##    120        0.6635             nan     0.1000   -0.0021
##    140        0.6313             nan     0.1000   -0.0020
##    150        0.6169             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2579             nan     0.1000    0.0168
##      2        1.2242             nan     0.1000    0.0145
##      3        1.1989             nan     0.1000    0.0110
##      4        1.1813             nan     0.1000    0.0080
##      5        1.1628             nan     0.1000    0.0068
##      6        1.1420             nan     0.1000    0.0076
##      7        1.1233             nan     0.1000    0.0092
##      8        1.1107             nan     0.1000    0.0057
##      9        1.0988             nan     0.1000    0.0053
##     10        1.0865             nan     0.1000    0.0049
##     20        0.9945             nan     0.1000    0.0025
##     40        0.9182             nan     0.1000   -0.0009
##     60        0.8761             nan     0.1000    0.0001
##     80        0.8543             nan     0.1000    0.0001
##    100        0.8391             nan     0.1000   -0.0014
##    120        0.8277             nan     0.1000   -0.0007
##    140        0.8155             nan     0.1000   -0.0011
##    150        0.8121             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2386             nan     0.1000    0.0193
##      2        1.1956             nan     0.1000    0.0165
##      3        1.1644             nan     0.1000    0.0156
##      4        1.1370             nan     0.1000    0.0122
##      5        1.1127             nan     0.1000    0.0113
##      6        1.0927             nan     0.1000    0.0082
##      7        1.0749             nan     0.1000    0.0080
##      8        1.0603             nan     0.1000    0.0048
##      9        1.0428             nan     0.1000    0.0059
##     10        1.0292             nan     0.1000    0.0049
##     20        0.9292             nan     0.1000    0.0032
##     40        0.8524             nan     0.1000   -0.0011
##     60        0.8075             nan     0.1000   -0.0008
##     80        0.7828             nan     0.1000   -0.0013
##    100        0.7599             nan     0.1000   -0.0003
##    120        0.7398             nan     0.1000   -0.0003
##    140        0.7217             nan     0.1000   -0.0015
##    150        0.7112             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2416             nan     0.1000    0.0253
##      2        1.1932             nan     0.1000    0.0202
##      3        1.1527             nan     0.1000    0.0141
##      4        1.1236             nan     0.1000    0.0098
##      5        1.0938             nan     0.1000    0.0132
##      6        1.0687             nan     0.1000    0.0087
##      7        1.0474             nan     0.1000    0.0070
##      8        1.0269             nan     0.1000    0.0081
##      9        1.0082             nan     0.1000    0.0048
##     10        0.9946             nan     0.1000    0.0029
##     20        0.8936             nan     0.1000    0.0017
##     40        0.8087             nan     0.1000    0.0004
##     60        0.7596             nan     0.1000   -0.0010
##     80        0.7219             nan     0.1000   -0.0023
##    100        0.6832             nan     0.1000   -0.0009
##    120        0.6545             nan     0.1000   -0.0014
##    140        0.6284             nan     0.1000   -0.0023
##    150        0.6152             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2601             nan     0.1000    0.0137
##      2        1.2322             nan     0.1000    0.0134
##      3        1.2090             nan     0.1000    0.0104
##      4        1.1854             nan     0.1000    0.0075
##      5        1.1684             nan     0.1000    0.0060
##      6        1.1500             nan     0.1000    0.0084
##      7        1.1371             nan     0.1000    0.0053
##      8        1.1206             nan     0.1000    0.0065
##      9        1.1109             nan     0.1000    0.0026
##     10        1.0981             nan     0.1000    0.0060
##     20        1.0015             nan     0.1000    0.0018
##     40        0.9197             nan     0.1000    0.0001
##     60        0.8822             nan     0.1000    0.0002
##     80        0.8573             nan     0.1000   -0.0007
##    100        0.8409             nan     0.1000   -0.0009
##    120        0.8239             nan     0.1000   -0.0009
##    140        0.8121             nan     0.1000   -0.0011
##    150        0.8085             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2469             nan     0.1000    0.0194
##      2        1.2043             nan     0.1000    0.0193
##      3        1.1739             nan     0.1000    0.0138
##      4        1.1437             nan     0.1000    0.0124
##      5        1.1256             nan     0.1000    0.0082
##      6        1.1026             nan     0.1000    0.0082
##      7        1.0814             nan     0.1000    0.0076
##      8        1.0619             nan     0.1000    0.0101
##      9        1.0453             nan     0.1000    0.0039
##     10        1.0260             nan     0.1000    0.0046
##     20        0.9262             nan     0.1000    0.0024
##     40        0.8472             nan     0.1000   -0.0013
##     60        0.8031             nan     0.1000   -0.0008
##     80        0.7761             nan     0.1000   -0.0016
##    100        0.7520             nan     0.1000   -0.0007
##    120        0.7294             nan     0.1000   -0.0003
##    140        0.7078             nan     0.1000   -0.0006
##    150        0.6989             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2408             nan     0.1000    0.0207
##      2        1.1956             nan     0.1000    0.0212
##      3        1.1551             nan     0.1000    0.0169
##      4        1.1240             nan     0.1000    0.0127
##      5        1.0981             nan     0.1000    0.0103
##      6        1.0745             nan     0.1000    0.0085
##      7        1.0551             nan     0.1000    0.0055
##      8        1.0379             nan     0.1000    0.0056
##      9        1.0222             nan     0.1000    0.0057
##     10        1.0026             nan     0.1000    0.0065
##     20        0.8970             nan     0.1000    0.0018
##     40        0.8073             nan     0.1000   -0.0026
##     60        0.7525             nan     0.1000   -0.0014
##     80        0.7119             nan     0.1000    0.0002
##    100        0.6749             nan     0.1000   -0.0023
##    120        0.6440             nan     0.1000   -0.0013
##    140        0.6180             nan     0.1000   -0.0029
##    150        0.6040             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2559             nan     0.1000    0.0176
##      2        1.2250             nan     0.1000    0.0147
##      3        1.1979             nan     0.1000    0.0113
##      4        1.1779             nan     0.1000    0.0093
##      5        1.1603             nan     0.1000    0.0079
##      6        1.1424             nan     0.1000    0.0058
##      7        1.1290             nan     0.1000    0.0045
##      8        1.1159             nan     0.1000    0.0052
##      9        1.0995             nan     0.1000    0.0057
##     10        1.0889             nan     0.1000    0.0046
##     20        1.0022             nan     0.1000    0.0025
##     40        0.9286             nan     0.1000    0.0003
##     60        0.8892             nan     0.1000   -0.0011
##     80        0.8660             nan     0.1000    0.0002
##    100        0.8523             nan     0.1000   -0.0002
##    120        0.8394             nan     0.1000   -0.0004
##    140        0.8291             nan     0.1000   -0.0022
##    150        0.8250             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2433             nan     0.1000    0.0212
##      2        1.2033             nan     0.1000    0.0174
##      3        1.1677             nan     0.1000    0.0163
##      4        1.1430             nan     0.1000    0.0093
##      5        1.1221             nan     0.1000    0.0066
##      6        1.0996             nan     0.1000    0.0098
##      7        1.0826             nan     0.1000    0.0055
##      8        1.0667             nan     0.1000    0.0062
##      9        1.0521             nan     0.1000    0.0042
##     10        1.0338             nan     0.1000    0.0067
##     20        0.9392             nan     0.1000    0.0012
##     40        0.8617             nan     0.1000   -0.0002
##     60        0.8210             nan     0.1000   -0.0011
##     80        0.7911             nan     0.1000   -0.0005
##    100        0.7649             nan     0.1000   -0.0018
##    120        0.7444             nan     0.1000   -0.0009
##    140        0.7204             nan     0.1000   -0.0019
##    150        0.7098             nan     0.1000   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2384             nan     0.1000    0.0217
##      2        1.1937             nan     0.1000    0.0194
##      3        1.1542             nan     0.1000    0.0177
##      4        1.1259             nan     0.1000    0.0117
##      5        1.0963             nan     0.1000    0.0103
##      6        1.0706             nan     0.1000    0.0101
##      7        1.0456             nan     0.1000    0.0086
##      8        1.0264             nan     0.1000    0.0067
##      9        1.0088             nan     0.1000    0.0064
##     10        0.9948             nan     0.1000    0.0045
##     20        0.9041             nan     0.1000    0.0019
##     40        0.8190             nan     0.1000   -0.0022
##     60        0.7735             nan     0.1000   -0.0019
##     80        0.7375             nan     0.1000   -0.0022
##    100        0.7050             nan     0.1000   -0.0024
##    120        0.6681             nan     0.1000   -0.0020
##    140        0.6429             nan     0.1000   -0.0013
##    150        0.6270             nan     0.1000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2551             nan     0.1000    0.0152
##      2        1.2249             nan     0.1000    0.0138
##      3        1.2001             nan     0.1000    0.0119
##      4        1.1800             nan     0.1000    0.0081
##      5        1.1639             nan     0.1000    0.0067
##      6        1.1468             nan     0.1000    0.0067
##      7        1.1333             nan     0.1000    0.0065
##      8        1.1226             nan     0.1000    0.0047
##      9        1.1092             nan     0.1000    0.0046
##     10        1.0969             nan     0.1000    0.0045
##     20        1.0135             nan     0.1000    0.0008
##     40        0.9291             nan     0.1000    0.0008
##     60        0.8909             nan     0.1000    0.0005
##     80        0.8681             nan     0.1000    0.0005
##    100        0.8506             nan     0.1000   -0.0014
##    120        0.8382             nan     0.1000   -0.0012
##    140        0.8258             nan     0.1000   -0.0011
##    150        0.8210             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2561             nan     0.1000    0.0185
##      2        1.2167             nan     0.1000    0.0170
##      3        1.1805             nan     0.1000    0.0165
##      4        1.1498             nan     0.1000    0.0115
##      5        1.1265             nan     0.1000    0.0106
##      6        1.1051             nan     0.1000    0.0081
##      7        1.0864             nan     0.1000    0.0075
##      8        1.0683             nan     0.1000    0.0049
##      9        1.0549             nan     0.1000    0.0045
##     10        1.0419             nan     0.1000    0.0044
##     20        0.9490             nan     0.1000    0.0018
##     40        0.8657             nan     0.1000   -0.0016
##     60        0.8249             nan     0.1000   -0.0013
##     80        0.7936             nan     0.1000   -0.0014
##    100        0.7733             nan     0.1000   -0.0016
##    120        0.7505             nan     0.1000   -0.0015
##    140        0.7330             nan     0.1000   -0.0017
##    150        0.7210             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2389             nan     0.1000    0.0243
##      2        1.1936             nan     0.1000    0.0207
##      3        1.1550             nan     0.1000    0.0175
##      4        1.1238             nan     0.1000    0.0108
##      5        1.0955             nan     0.1000    0.0105
##      6        1.0711             nan     0.1000    0.0090
##      7        1.0490             nan     0.1000    0.0090
##      8        1.0260             nan     0.1000    0.0073
##      9        1.0059             nan     0.1000    0.0050
##     10        0.9939             nan     0.1000    0.0019
##     20        0.9000             nan     0.1000    0.0016
##     40        0.8123             nan     0.1000   -0.0028
##     60        0.7626             nan     0.1000   -0.0023
##     80        0.7265             nan     0.1000   -0.0015
##    100        0.6929             nan     0.1000   -0.0028
##    120        0.6581             nan     0.1000   -0.0012
##    140        0.6303             nan     0.1000   -0.0020
##    150        0.6160             nan     0.1000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2634             nan     0.1000    0.0170
##      2        1.2345             nan     0.1000    0.0131
##      3        1.2115             nan     0.1000    0.0117
##      4        1.1927             nan     0.1000    0.0072
##      5        1.1727             nan     0.1000    0.0096
##      6        1.1538             nan     0.1000    0.0070
##      7        1.1383             nan     0.1000    0.0070
##      8        1.1265             nan     0.1000    0.0042
##      9        1.1110             nan     0.1000    0.0063
##     10        1.0998             nan     0.1000    0.0041
##     20        1.0079             nan     0.1000    0.0005
##     40        0.9254             nan     0.1000    0.0001
##     60        0.8810             nan     0.1000    0.0001
##     80        0.8599             nan     0.1000   -0.0022
##    100        0.8433             nan     0.1000   -0.0004
##    120        0.8310             nan     0.1000    0.0000
##    140        0.8205             nan     0.1000   -0.0006
##    150        0.8179             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2500             nan     0.1000    0.0201
##      2        1.2185             nan     0.1000    0.0139
##      3        1.1866             nan     0.1000    0.0140
##      4        1.1586             nan     0.1000    0.0120
##      5        1.1331             nan     0.1000    0.0095
##      6        1.1120             nan     0.1000    0.0084
##      7        1.0904             nan     0.1000    0.0070
##      8        1.0702             nan     0.1000    0.0077
##      9        1.0566             nan     0.1000    0.0049
##     10        1.0406             nan     0.1000    0.0051
##     20        0.9421             nan     0.1000    0.0005
##     40        0.8561             nan     0.1000    0.0009
##     60        0.8131             nan     0.1000   -0.0012
##     80        0.7848             nan     0.1000   -0.0018
##    100        0.7580             nan     0.1000   -0.0017
##    120        0.7336             nan     0.1000   -0.0016
##    140        0.7155             nan     0.1000   -0.0010
##    150        0.7070             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2441             nan     0.1000    0.0240
##      2        1.2010             nan     0.1000    0.0193
##      3        1.1659             nan     0.1000    0.0167
##      4        1.1303             nan     0.1000    0.0160
##      5        1.1027             nan     0.1000    0.0112
##      6        1.0767             nan     0.1000    0.0087
##      7        1.0537             nan     0.1000    0.0090
##      8        1.0353             nan     0.1000    0.0058
##      9        1.0171             nan     0.1000    0.0082
##     10        1.0000             nan     0.1000    0.0065
##     20        0.8987             nan     0.1000    0.0021
##     40        0.8102             nan     0.1000   -0.0024
##     60        0.7616             nan     0.1000   -0.0009
##     80        0.7268             nan     0.1000   -0.0020
##    100        0.6938             nan     0.1000   -0.0016
##    120        0.6655             nan     0.1000   -0.0025
##    140        0.6379             nan     0.1000   -0.0019
##    150        0.6216             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2555             nan     0.1000    0.0180
##      2        1.2247             nan     0.1000    0.0108
##      3        1.1976             nan     0.1000    0.0116
##      4        1.1733             nan     0.1000    0.0072
##      5        1.1552             nan     0.1000    0.0063
##      6        1.1382             nan     0.1000    0.0077
##      7        1.1233             nan     0.1000    0.0067
##      8        1.1085             nan     0.1000    0.0060
##      9        1.0967             nan     0.1000    0.0037
##     10        1.0806             nan     0.1000    0.0055
##     20        0.9917             nan     0.1000    0.0018
##     40        0.9115             nan     0.1000    0.0004
##     60        0.8794             nan     0.1000   -0.0001
##     80        0.8567             nan     0.1000   -0.0006
##    100        0.8403             nan     0.1000   -0.0011
##    120        0.8279             nan     0.1000   -0.0017
##    140        0.8185             nan     0.1000   -0.0008
##    150        0.8146             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2420             nan     0.1000    0.0236
##      2        1.2064             nan     0.1000    0.0153
##      3        1.1727             nan     0.1000    0.0117
##      4        1.1459             nan     0.1000    0.0124
##      5        1.1231             nan     0.1000    0.0096
##      6        1.0999             nan     0.1000    0.0099
##      7        1.0802             nan     0.1000    0.0088
##      8        1.0629             nan     0.1000    0.0062
##      9        1.0440             nan     0.1000    0.0072
##     10        1.0295             nan     0.1000    0.0063
##     20        0.9278             nan     0.1000    0.0009
##     40        0.8547             nan     0.1000   -0.0008
##     60        0.8140             nan     0.1000   -0.0004
##     80        0.7926             nan     0.1000   -0.0022
##    100        0.7676             nan     0.1000   -0.0031
##    120        0.7493             nan     0.1000   -0.0005
##    140        0.7276             nan     0.1000   -0.0002
##    150        0.7181             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2438             nan     0.1000    0.0237
##      2        1.1944             nan     0.1000    0.0218
##      3        1.1596             nan     0.1000    0.0124
##      4        1.1211             nan     0.1000    0.0122
##      5        1.0940             nan     0.1000    0.0102
##      6        1.0658             nan     0.1000    0.0111
##      7        1.0435             nan     0.1000    0.0104
##      8        1.0255             nan     0.1000    0.0070
##      9        1.0089             nan     0.1000    0.0079
##     10        0.9943             nan     0.1000    0.0041
##     20        0.8931             nan     0.1000    0.0006
##     40        0.8093             nan     0.1000   -0.0012
##     60        0.7626             nan     0.1000   -0.0031
##     80        0.7294             nan     0.1000   -0.0024
##    100        0.6934             nan     0.1000   -0.0017
##    120        0.6592             nan     0.1000   -0.0022
##    140        0.6309             nan     0.1000   -0.0039
##    150        0.6183             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2569             nan     0.1000    0.0158
##      2        1.2317             nan     0.1000    0.0134
##      3        1.2111             nan     0.1000    0.0073
##      4        1.1865             nan     0.1000    0.0104
##      5        1.1659             nan     0.1000    0.0097
##      6        1.1478             nan     0.1000    0.0061
##      7        1.1321             nan     0.1000    0.0059
##      8        1.1211             nan     0.1000    0.0050
##      9        1.1078             nan     0.1000    0.0051
##     10        1.0929             nan     0.1000    0.0059
##     20        1.0067             nan     0.1000    0.0012
##     40        0.9297             nan     0.1000   -0.0002
##     60        0.8877             nan     0.1000    0.0000
##     80        0.8617             nan     0.1000    0.0006
##    100        0.8448             nan     0.1000   -0.0004
##    120        0.8299             nan     0.1000   -0.0010
##    140        0.8167             nan     0.1000   -0.0007
##    150        0.8131             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2409             nan     0.1000    0.0176
##      2        1.2049             nan     0.1000    0.0147
##      3        1.1712             nan     0.1000    0.0134
##      4        1.1424             nan     0.1000    0.0112
##      5        1.1180             nan     0.1000    0.0095
##      6        1.0933             nan     0.1000    0.0091
##      7        1.0789             nan     0.1000    0.0056
##      8        1.0611             nan     0.1000    0.0077
##      9        1.0464             nan     0.1000    0.0044
##     10        1.0321             nan     0.1000    0.0057
##     20        0.9403             nan     0.1000    0.0015
##     40        0.8504             nan     0.1000   -0.0010
##     60        0.8047             nan     0.1000   -0.0008
##     80        0.7796             nan     0.1000    0.0003
##    100        0.7523             nan     0.1000   -0.0007
##    120        0.7251             nan     0.1000   -0.0010
##    140        0.7055             nan     0.1000   -0.0014
##    150        0.6965             nan     0.1000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2404             nan     0.1000    0.0235
##      2        1.1976             nan     0.1000    0.0203
##      3        1.1589             nan     0.1000    0.0126
##      4        1.1249             nan     0.1000    0.0150
##      5        1.0990             nan     0.1000    0.0100
##      6        1.0735             nan     0.1000    0.0103
##      7        1.0509             nan     0.1000    0.0078
##      8        1.0344             nan     0.1000    0.0074
##      9        1.0224             nan     0.1000    0.0045
##     10        1.0042             nan     0.1000    0.0050
##     20        0.9074             nan     0.1000    0.0006
##     40        0.8126             nan     0.1000   -0.0021
##     60        0.7588             nan     0.1000   -0.0014
##     80        0.7136             nan     0.1000   -0.0010
##    100        0.6732             nan     0.1000   -0.0028
##    120        0.6410             nan     0.1000   -0.0013
##    140        0.6181             nan     0.1000   -0.0012
##    150        0.6086             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2653             nan     0.1000    0.0108
##      2        1.2316             nan     0.1000    0.0151
##      3        1.2073             nan     0.1000    0.0111
##      4        1.1864             nan     0.1000    0.0111
##      5        1.1669             nan     0.1000    0.0088
##      6        1.1514             nan     0.1000    0.0077
##      7        1.1360             nan     0.1000    0.0064
##      8        1.1191             nan     0.1000    0.0063
##      9        1.1043             nan     0.1000    0.0054
##     10        1.0900             nan     0.1000    0.0065
##     20        0.9980             nan     0.1000    0.0015
##     40        0.9155             nan     0.1000   -0.0002
##     60        0.8728             nan     0.1000    0.0004
##     80        0.8483             nan     0.1000   -0.0005
##    100        0.8271             nan     0.1000   -0.0004
##    120        0.8164             nan     0.1000   -0.0017
##    140        0.8060             nan     0.1000   -0.0011
##    150        0.8045             nan     0.1000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2523             nan     0.1000    0.0166
##      2        1.2136             nan     0.1000    0.0174
##      3        1.1763             nan     0.1000    0.0152
##      4        1.1432             nan     0.1000    0.0126
##      5        1.1203             nan     0.1000    0.0093
##      6        1.1002             nan     0.1000    0.0094
##      7        1.0797             nan     0.1000    0.0067
##      8        1.0607             nan     0.1000    0.0073
##      9        1.0442             nan     0.1000    0.0077
##     10        1.0289             nan     0.1000    0.0050
##     20        0.9321             nan     0.1000    0.0022
##     40        0.8512             nan     0.1000   -0.0009
##     60        0.8115             nan     0.1000   -0.0015
##     80        0.7780             nan     0.1000   -0.0008
##    100        0.7545             nan     0.1000   -0.0013
##    120        0.7383             nan     0.1000   -0.0010
##    140        0.7190             nan     0.1000   -0.0015
##    150        0.7102             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2423             nan     0.1000    0.0255
##      2        1.1935             nan     0.1000    0.0215
##      3        1.1508             nan     0.1000    0.0177
##      4        1.1191             nan     0.1000    0.0116
##      5        1.0929             nan     0.1000    0.0103
##      6        1.0651             nan     0.1000    0.0112
##      7        1.0426             nan     0.1000    0.0097
##      8        1.0199             nan     0.1000    0.0087
##      9        1.0013             nan     0.1000    0.0069
##     10        0.9856             nan     0.1000    0.0063
##     20        0.8900             nan     0.1000   -0.0003
##     40        0.8035             nan     0.1000   -0.0022
##     60        0.7508             nan     0.1000   -0.0018
##     80        0.7110             nan     0.1000   -0.0012
##    100        0.6809             nan     0.1000   -0.0025
##    120        0.6479             nan     0.1000   -0.0008
##    140        0.6167             nan     0.1000   -0.0021
##    150        0.6007             nan     0.1000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2474             nan     0.1000    0.0147
##      2        1.2275             nan     0.1000    0.0084
##      3        1.2027             nan     0.1000    0.0131
##      4        1.1775             nan     0.1000    0.0083
##      5        1.1600             nan     0.1000    0.0087
##      6        1.1440             nan     0.1000    0.0066
##      7        1.1294             nan     0.1000    0.0058
##      8        1.1175             nan     0.1000    0.0058
##      9        1.1039             nan     0.1000    0.0060
##     10        1.0901             nan     0.1000    0.0049
##     20        1.0025             nan     0.1000    0.0028
##     40        0.9190             nan     0.1000   -0.0003
##     60        0.8770             nan     0.1000    0.0001
##     80        0.8530             nan     0.1000   -0.0019
##    100        0.8346             nan     0.1000   -0.0005
##    120        0.8191             nan     0.1000   -0.0007
##    140        0.8080             nan     0.1000   -0.0019
##    150        0.8030             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2539             nan     0.1000    0.0188
##      2        1.2144             nan     0.1000    0.0172
##      3        1.1814             nan     0.1000    0.0175
##      4        1.1511             nan     0.1000    0.0109
##      5        1.1247             nan     0.1000    0.0111
##      6        1.0993             nan     0.1000    0.0101
##      7        1.0802             nan     0.1000    0.0073
##      8        1.0628             nan     0.1000    0.0057
##      9        1.0479             nan     0.1000    0.0041
##     10        1.0348             nan     0.1000    0.0039
##     20        0.9299             nan     0.1000    0.0025
##     40        0.8528             nan     0.1000   -0.0014
##     60        0.8069             nan     0.1000   -0.0021
##     80        0.7792             nan     0.1000   -0.0007
##    100        0.7590             nan     0.1000   -0.0016
##    120        0.7409             nan     0.1000   -0.0018
##    140        0.7176             nan     0.1000   -0.0010
##    150        0.7062             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2373             nan     0.1000    0.0226
##      2        1.1921             nan     0.1000    0.0208
##      3        1.1573             nan     0.1000    0.0170
##      4        1.1217             nan     0.1000    0.0148
##      5        1.0975             nan     0.1000    0.0114
##      6        1.0727             nan     0.1000    0.0095
##      7        1.0485             nan     0.1000    0.0121
##      8        1.0278             nan     0.1000    0.0083
##      9        1.0131             nan     0.1000    0.0056
##     10        1.0007             nan     0.1000    0.0032
##     20        0.8944             nan     0.1000    0.0020
##     40        0.8066             nan     0.1000   -0.0012
##     60        0.7588             nan     0.1000   -0.0014
##     80        0.7156             nan     0.1000   -0.0007
##    100        0.6808             nan     0.1000   -0.0017
##    120        0.6509             nan     0.1000   -0.0019
##    140        0.6246             nan     0.1000   -0.0018
##    150        0.6123             nan     0.1000   -0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2567             nan     0.1000    0.0146
##      2        1.2293             nan     0.1000    0.0144
##      3        1.2078             nan     0.1000    0.0086
##      4        1.1898             nan     0.1000    0.0086
##      5        1.1710             nan     0.1000    0.0079
##      6        1.1553             nan     0.1000    0.0061
##      7        1.1394             nan     0.1000    0.0059
##      8        1.1262             nan     0.1000    0.0045
##      9        1.1144             nan     0.1000    0.0057
##     10        1.0999             nan     0.1000    0.0066
##     20        1.0147             nan     0.1000    0.0005
##     40        0.9360             nan     0.1000    0.0008
##     60        0.8978             nan     0.1000   -0.0008
##     80        0.8724             nan     0.1000   -0.0008
##    100        0.8566             nan     0.1000    0.0001
##    120        0.8433             nan     0.1000   -0.0005
##    140        0.8344             nan     0.1000   -0.0010
##    150        0.8292             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2459             nan     0.1000    0.0194
##      2        1.2126             nan     0.1000    0.0128
##      3        1.1854             nan     0.1000    0.0116
##      4        1.1565             nan     0.1000    0.0123
##      5        1.1353             nan     0.1000    0.0095
##      6        1.1118             nan     0.1000    0.0102
##      7        1.0940             nan     0.1000    0.0081
##      8        1.0779             nan     0.1000    0.0056
##      9        1.0595             nan     0.1000    0.0058
##     10        1.0469             nan     0.1000    0.0040
##     20        0.9539             nan     0.1000    0.0011
##     40        0.8656             nan     0.1000   -0.0016
##     60        0.8233             nan     0.1000   -0.0012
##     80        0.7944             nan     0.1000   -0.0012
##    100        0.7691             nan     0.1000   -0.0024
##    120        0.7454             nan     0.1000   -0.0012
##    140        0.7257             nan     0.1000   -0.0006
##    150        0.7185             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2410             nan     0.1000    0.0251
##      2        1.2028             nan     0.1000    0.0172
##      3        1.1645             nan     0.1000    0.0144
##      4        1.1362             nan     0.1000    0.0074
##      5        1.1084             nan     0.1000    0.0092
##      6        1.0835             nan     0.1000    0.0081
##      7        1.0605             nan     0.1000    0.0084
##      8        1.0385             nan     0.1000    0.0073
##      9        1.0178             nan     0.1000    0.0034
##     10        1.0027             nan     0.1000    0.0064
##     20        0.9141             nan     0.1000   -0.0017
##     40        0.8235             nan     0.1000   -0.0026
##     60        0.7697             nan     0.1000   -0.0016
##     80        0.7270             nan     0.1000    0.0000
##    100        0.6957             nan     0.1000   -0.0003
##    120        0.6641             nan     0.1000   -0.0018
##    140        0.6360             nan     0.1000   -0.0022
##    150        0.6210             nan     0.1000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2565             nan     0.1000    0.0149
##      2        1.2253             nan     0.1000    0.0129
##      3        1.2032             nan     0.1000    0.0102
##      4        1.1819             nan     0.1000    0.0094
##      5        1.1641             nan     0.1000    0.0074
##      6        1.1461             nan     0.1000    0.0066
##      7        1.1314             nan     0.1000    0.0060
##      8        1.1186             nan     0.1000    0.0047
##      9        1.1065             nan     0.1000    0.0042
##     10        1.0953             nan     0.1000    0.0036
##     20        1.0094             nan     0.1000    0.0026
##     40        0.9277             nan     0.1000    0.0002
##     60        0.8883             nan     0.1000   -0.0000
##     80        0.8641             nan     0.1000   -0.0009
##    100        0.8555             nan     0.1000   -0.0015
##    120        0.8432             nan     0.1000   -0.0003
##    140        0.8347             nan     0.1000   -0.0008
##    150        0.8292             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2528             nan     0.1000    0.0197
##      2        1.2170             nan     0.1000    0.0142
##      3        1.1822             nan     0.1000    0.0159
##      4        1.1568             nan     0.1000    0.0117
##      5        1.1307             nan     0.1000    0.0115
##      6        1.1094             nan     0.1000    0.0101
##      7        1.0893             nan     0.1000    0.0071
##      8        1.0716             nan     0.1000    0.0054
##      9        1.0552             nan     0.1000    0.0036
##     10        1.0447             nan     0.1000    0.0020
##     20        0.9497             nan     0.1000   -0.0012
##     40        0.8713             nan     0.1000   -0.0014
##     60        0.8270             nan     0.1000   -0.0002
##     80        0.7985             nan     0.1000   -0.0025
##    100        0.7737             nan     0.1000   -0.0011
##    120        0.7557             nan     0.1000   -0.0021
##    140        0.7366             nan     0.1000   -0.0015
##    150        0.7259             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2435             nan     0.1000    0.0227
##      2        1.1991             nan     0.1000    0.0200
##      3        1.1706             nan     0.1000    0.0088
##      4        1.1358             nan     0.1000    0.0126
##      5        1.0985             nan     0.1000    0.0165
##      6        1.0724             nan     0.1000    0.0110
##      7        1.0494             nan     0.1000    0.0104
##      8        1.0312             nan     0.1000    0.0064
##      9        1.0162             nan     0.1000    0.0035
##     10        1.0033             nan     0.1000    0.0027
##     20        0.9103             nan     0.1000    0.0006
##     40        0.8219             nan     0.1000   -0.0016
##     60        0.7692             nan     0.1000   -0.0010
##     80        0.7373             nan     0.1000   -0.0016
##    100        0.7063             nan     0.1000   -0.0011
##    120        0.6777             nan     0.1000   -0.0023
##    140        0.6471             nan     0.1000   -0.0018
##    150        0.6345             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2565             nan     0.1000    0.0161
##      2        1.2298             nan     0.1000    0.0124
##      3        1.2074             nan     0.1000    0.0112
##      4        1.1851             nan     0.1000    0.0092
##      5        1.1672             nan     0.1000    0.0091
##      6        1.1497             nan     0.1000    0.0068
##      7        1.1321             nan     0.1000    0.0071
##      8        1.1142             nan     0.1000    0.0067
##      9        1.0999             nan     0.1000    0.0057
##     10        1.0870             nan     0.1000    0.0056
##     20        0.9986             nan     0.1000    0.0014
##     40        0.9143             nan     0.1000   -0.0004
##     60        0.8721             nan     0.1000   -0.0007
##     80        0.8473             nan     0.1000   -0.0002
##    100        0.8328             nan     0.1000   -0.0018
##    120        0.8159             nan     0.1000   -0.0009
##    140        0.8044             nan     0.1000   -0.0012
##    150        0.8003             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2496             nan     0.1000    0.0224
##      2        1.2118             nan     0.1000    0.0168
##      3        1.1751             nan     0.1000    0.0140
##      4        1.1450             nan     0.1000    0.0138
##      5        1.1196             nan     0.1000    0.0118
##      6        1.0998             nan     0.1000    0.0081
##      7        1.0789             nan     0.1000    0.0091
##      8        1.0596             nan     0.1000    0.0074
##      9        1.0442             nan     0.1000    0.0056
##     10        1.0262             nan     0.1000    0.0064
##     20        0.9275             nan     0.1000    0.0019
##     40        0.8472             nan     0.1000   -0.0001
##     60        0.8043             nan     0.1000   -0.0013
##     80        0.7734             nan     0.1000   -0.0008
##    100        0.7439             nan     0.1000   -0.0012
##    120        0.7274             nan     0.1000   -0.0016
##    140        0.7098             nan     0.1000   -0.0009
##    150        0.6990             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2340             nan     0.1000    0.0268
##      2        1.1925             nan     0.1000    0.0168
##      3        1.1535             nan     0.1000    0.0174
##      4        1.1195             nan     0.1000    0.0153
##      5        1.0943             nan     0.1000    0.0101
##      6        1.0674             nan     0.1000    0.0109
##      7        1.0489             nan     0.1000    0.0060
##      8        1.0295             nan     0.1000    0.0061
##      9        1.0117             nan     0.1000    0.0042
##     10        0.9960             nan     0.1000    0.0053
##     20        0.8962             nan     0.1000   -0.0003
##     40        0.8106             nan     0.1000   -0.0003
##     60        0.7533             nan     0.1000   -0.0006
##     80        0.7123             nan     0.1000   -0.0004
##    100        0.6848             nan     0.1000   -0.0018
##    120        0.6557             nan     0.1000   -0.0010
##    140        0.6266             nan     0.1000   -0.0013
##    150        0.6185             nan     0.1000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2569             nan     0.1000    0.0179
##      2        1.2314             nan     0.1000    0.0138
##      3        1.2076             nan     0.1000    0.0099
##      4        1.1868             nan     0.1000    0.0090
##      5        1.1709             nan     0.1000    0.0076
##      6        1.1556             nan     0.1000    0.0070
##      7        1.1405             nan     0.1000    0.0060
##      8        1.1285             nan     0.1000    0.0040
##      9        1.1161             nan     0.1000    0.0053
##     10        1.1046             nan     0.1000    0.0046
##     20        1.0150             nan     0.1000    0.0017
##     40        0.9340             nan     0.1000    0.0003
##     60        0.8970             nan     0.1000   -0.0017
##     80        0.8737             nan     0.1000   -0.0005
##    100        0.8591             nan     0.1000   -0.0008
##    120        0.8471             nan     0.1000   -0.0014
##    140        0.8370             nan     0.1000   -0.0011
##    150        0.8327             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2547             nan     0.1000    0.0167
##      2        1.2235             nan     0.1000    0.0149
##      3        1.1922             nan     0.1000    0.0158
##      4        1.1615             nan     0.1000    0.0142
##      5        1.1367             nan     0.1000    0.0115
##      6        1.1185             nan     0.1000    0.0091
##      7        1.0995             nan     0.1000    0.0080
##      8        1.0803             nan     0.1000    0.0069
##      9        1.0583             nan     0.1000    0.0077
##     10        1.0461             nan     0.1000    0.0034
##     20        0.9493             nan     0.1000    0.0018
##     40        0.8697             nan     0.1000   -0.0004
##     60        0.8317             nan     0.1000   -0.0013
##     80        0.8039             nan     0.1000   -0.0006
##    100        0.7756             nan     0.1000    0.0002
##    120        0.7532             nan     0.1000   -0.0016
##    140        0.7347             nan     0.1000   -0.0008
##    150        0.7245             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2482             nan     0.1000    0.0185
##      2        1.2034             nan     0.1000    0.0211
##      3        1.1621             nan     0.1000    0.0193
##      4        1.1312             nan     0.1000    0.0129
##      5        1.1042             nan     0.1000    0.0098
##      6        1.0843             nan     0.1000    0.0070
##      7        1.0644             nan     0.1000    0.0070
##      8        1.0455             nan     0.1000    0.0048
##      9        1.0257             nan     0.1000    0.0069
##     10        1.0089             nan     0.1000    0.0052
##     20        0.9100             nan     0.1000    0.0023
##     40        0.8269             nan     0.1000   -0.0010
##     60        0.7752             nan     0.1000   -0.0009
##     80        0.7410             nan     0.1000   -0.0026
##    100        0.7034             nan     0.1000   -0.0018
##    120        0.6752             nan     0.1000   -0.0018
##    140        0.6441             nan     0.1000   -0.0015
##    150        0.6306             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2647             nan     0.1000    0.0143
##      2        1.2356             nan     0.1000    0.0125
##      3        1.2122             nan     0.1000    0.0094
##      4        1.1882             nan     0.1000    0.0091
##      5        1.1704             nan     0.1000    0.0059
##      6        1.1552             nan     0.1000    0.0066
##      7        1.1405             nan     0.1000    0.0054
##      8        1.1240             nan     0.1000    0.0042
##      9        1.1074             nan     0.1000    0.0076
##     10        1.0903             nan     0.1000    0.0067
##     20        1.0011             nan     0.1000    0.0035
##     40        0.9197             nan     0.1000    0.0002
##     60        0.8791             nan     0.1000   -0.0005
##     80        0.8583             nan     0.1000   -0.0007
##    100        0.8455             nan     0.1000   -0.0013
##    120        0.8334             nan     0.1000   -0.0003
##    140        0.8219             nan     0.1000   -0.0015
##    150        0.8177             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2524             nan     0.1000    0.0172
##      2        1.2117             nan     0.1000    0.0162
##      3        1.1800             nan     0.1000    0.0122
##      4        1.1515             nan     0.1000    0.0147
##      5        1.1246             nan     0.1000    0.0119
##      6        1.1037             nan     0.1000    0.0095
##      7        1.0888             nan     0.1000    0.0069
##      8        1.0736             nan     0.1000    0.0046
##      9        1.0537             nan     0.1000    0.0086
##     10        1.0399             nan     0.1000    0.0032
##     20        0.9459             nan     0.1000    0.0011
##     40        0.8552             nan     0.1000    0.0003
##     60        0.8148             nan     0.1000   -0.0006
##     80        0.7892             nan     0.1000   -0.0013
##    100        0.7659             nan     0.1000   -0.0015
##    120        0.7448             nan     0.1000   -0.0016
##    140        0.7247             nan     0.1000   -0.0008
##    150        0.7123             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2421             nan     0.1000    0.0198
##      2        1.1957             nan     0.1000    0.0187
##      3        1.1566             nan     0.1000    0.0138
##      4        1.1275             nan     0.1000    0.0126
##      5        1.1033             nan     0.1000    0.0095
##      6        1.0752             nan     0.1000    0.0118
##      7        1.0567             nan     0.1000    0.0080
##      8        1.0399             nan     0.1000    0.0058
##      9        1.0199             nan     0.1000    0.0069
##     10        1.0029             nan     0.1000    0.0080
##     20        0.9047             nan     0.1000   -0.0006
##     40        0.8182             nan     0.1000   -0.0011
##     60        0.7713             nan     0.1000   -0.0009
##     80        0.7277             nan     0.1000   -0.0009
##    100        0.6913             nan     0.1000   -0.0009
##    120        0.6601             nan     0.1000   -0.0015
##    140        0.6270             nan     0.1000   -0.0007
##    150        0.6118             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2598             nan     0.1000    0.0136
##      2        1.2263             nan     0.1000    0.0146
##      3        1.2062             nan     0.1000    0.0068
##      4        1.1811             nan     0.1000    0.0112
##      5        1.1623             nan     0.1000    0.0094
##      6        1.1433             nan     0.1000    0.0063
##      7        1.1273             nan     0.1000    0.0072
##      8        1.1117             nan     0.1000    0.0070
##      9        1.0976             nan     0.1000    0.0045
##     10        1.0847             nan     0.1000    0.0052
##     20        1.0010             nan     0.1000    0.0023
##     40        0.9228             nan     0.1000   -0.0003
##     60        0.8815             nan     0.1000   -0.0004
##     80        0.8622             nan     0.1000   -0.0010
##    100        0.8451             nan     0.1000   -0.0002
##    120        0.8347             nan     0.1000   -0.0012
##    140        0.8243             nan     0.1000   -0.0012
##    150        0.8210             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2489             nan     0.1000    0.0170
##      2        1.2106             nan     0.1000    0.0158
##      3        1.1766             nan     0.1000    0.0168
##      4        1.1471             nan     0.1000    0.0137
##      5        1.1233             nan     0.1000    0.0092
##      6        1.0983             nan     0.1000    0.0109
##      7        1.0805             nan     0.1000    0.0077
##      8        1.0669             nan     0.1000    0.0029
##      9        1.0524             nan     0.1000    0.0072
##     10        1.0370             nan     0.1000    0.0058
##     20        0.9384             nan     0.1000    0.0010
##     40        0.8557             nan     0.1000   -0.0000
##     60        0.8089             nan     0.1000   -0.0007
##     80        0.7780             nan     0.1000   -0.0011
##    100        0.7549             nan     0.1000   -0.0009
##    120        0.7297             nan     0.1000   -0.0006
##    140        0.7132             nan     0.1000   -0.0013
##    150        0.7005             nan     0.1000   -0.0028
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2438             nan     0.1000    0.0153
##      2        1.2031             nan     0.1000    0.0182
##      3        1.1625             nan     0.1000    0.0192
##      4        1.1272             nan     0.1000    0.0124
##      5        1.0979             nan     0.1000    0.0124
##      6        1.0710             nan     0.1000    0.0100
##      7        1.0479             nan     0.1000    0.0104
##      8        1.0309             nan     0.1000    0.0036
##      9        1.0158             nan     0.1000    0.0037
##     10        1.0016             nan     0.1000    0.0043
##     20        0.9062             nan     0.1000   -0.0003
##     40        0.8135             nan     0.1000   -0.0018
##     60        0.7723             nan     0.1000   -0.0036
##     80        0.7273             nan     0.1000   -0.0015
##    100        0.6936             nan     0.1000   -0.0003
##    120        0.6626             nan     0.1000   -0.0009
##    140        0.6304             nan     0.1000   -0.0019
##    150        0.6159             nan     0.1000   -0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2540             nan     0.1000    0.0178
##      2        1.2226             nan     0.1000    0.0116
##      3        1.1942             nan     0.1000    0.0131
##      4        1.1732             nan     0.1000    0.0089
##      5        1.1540             nan     0.1000    0.0070
##      6        1.1365             nan     0.1000    0.0080
##      7        1.1244             nan     0.1000    0.0040
##      8        1.1070             nan     0.1000    0.0056
##      9        1.0952             nan     0.1000    0.0045
##     10        1.0821             nan     0.1000    0.0052
##     20        0.9942             nan     0.1000    0.0019
##     40        0.9187             nan     0.1000    0.0004
##     60        0.8749             nan     0.1000    0.0005
##     80        0.8541             nan     0.1000   -0.0001
##    100        0.8365             nan     0.1000   -0.0004
##    120        0.8195             nan     0.1000   -0.0012
##    140        0.8111             nan     0.1000   -0.0005
##    150        0.8030             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2460             nan     0.1000    0.0183
##      2        1.2037             nan     0.1000    0.0176
##      3        1.1657             nan     0.1000    0.0162
##      4        1.1370             nan     0.1000    0.0139
##      5        1.1130             nan     0.1000    0.0093
##      6        1.0967             nan     0.1000    0.0059
##      7        1.0776             nan     0.1000    0.0066
##      8        1.0601             nan     0.1000    0.0066
##      9        1.0444             nan     0.1000    0.0061
##     10        1.0309             nan     0.1000    0.0053
##     20        0.9265             nan     0.1000    0.0022
##     40        0.8453             nan     0.1000   -0.0011
##     60        0.7986             nan     0.1000   -0.0010
##     80        0.7657             nan     0.1000   -0.0008
##    100        0.7371             nan     0.1000   -0.0006
##    120        0.7130             nan     0.1000   -0.0010
##    140        0.6948             nan     0.1000   -0.0011
##    150        0.6834             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2381             nan     0.1000    0.0237
##      2        1.1874             nan     0.1000    0.0197
##      3        1.1485             nan     0.1000    0.0175
##      4        1.1194             nan     0.1000    0.0115
##      5        1.0921             nan     0.1000    0.0132
##      6        1.0689             nan     0.1000    0.0094
##      7        1.0468             nan     0.1000    0.0090
##      8        1.0262             nan     0.1000    0.0093
##      9        1.0092             nan     0.1000    0.0054
##     10        0.9895             nan     0.1000    0.0064
##     20        0.8868             nan     0.1000    0.0015
##     40        0.7945             nan     0.1000    0.0008
##     60        0.7494             nan     0.1000   -0.0009
##     80        0.7092             nan     0.1000   -0.0015
##    100        0.6768             nan     0.1000   -0.0009
##    120        0.6447             nan     0.1000    0.0001
##    140        0.6136             nan     0.1000   -0.0017
##    150        0.6037             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2568             nan     0.1000    0.0157
##      2        1.2284             nan     0.1000    0.0124
##      3        1.2060             nan     0.1000    0.0102
##      4        1.1842             nan     0.1000    0.0097
##      5        1.1682             nan     0.1000    0.0067
##      6        1.1517             nan     0.1000    0.0065
##      7        1.1365             nan     0.1000    0.0062
##      8        1.1202             nan     0.1000    0.0061
##      9        1.1073             nan     0.1000    0.0047
##     10        1.0956             nan     0.1000    0.0045
##     20        1.0071             nan     0.1000    0.0015
##     40        0.9334             nan     0.1000    0.0000
##     60        0.8911             nan     0.1000   -0.0001
##     80        0.8666             nan     0.1000   -0.0015
##    100        0.8539             nan     0.1000   -0.0011
##    120        0.8383             nan     0.1000   -0.0016
##    140        0.8302             nan     0.1000   -0.0006
##    150        0.8261             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2490             nan     0.1000    0.0203
##      2        1.2111             nan     0.1000    0.0173
##      3        1.1795             nan     0.1000    0.0124
##      4        1.1484             nan     0.1000    0.0118
##      5        1.1289             nan     0.1000    0.0083
##      6        1.1116             nan     0.1000    0.0072
##      7        1.0910             nan     0.1000    0.0083
##      8        1.0717             nan     0.1000    0.0088
##      9        1.0574             nan     0.1000    0.0041
##     10        1.0444             nan     0.1000    0.0050
##     20        0.9512             nan     0.1000    0.0004
##     40        0.8628             nan     0.1000    0.0008
##     60        0.8294             nan     0.1000   -0.0018
##     80        0.8019             nan     0.1000   -0.0015
##    100        0.7769             nan     0.1000   -0.0018
##    120        0.7498             nan     0.1000   -0.0016
##    140        0.7341             nan     0.1000   -0.0011
##    150        0.7254             nan     0.1000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2449             nan     0.1000    0.0234
##      2        1.2032             nan     0.1000    0.0181
##      3        1.1665             nan     0.1000    0.0181
##      4        1.1318             nan     0.1000    0.0124
##      5        1.1019             nan     0.1000    0.0110
##      6        1.0784             nan     0.1000    0.0101
##      7        1.0545             nan     0.1000    0.0069
##      8        1.0340             nan     0.1000    0.0081
##      9        1.0164             nan     0.1000    0.0053
##     10        1.0031             nan     0.1000    0.0034
##     20        0.9088             nan     0.1000    0.0024
##     40        0.8251             nan     0.1000    0.0001
##     60        0.7756             nan     0.1000   -0.0008
##     80        0.7363             nan     0.1000   -0.0023
##    100        0.7028             nan     0.1000   -0.0016
##    120        0.6737             nan     0.1000   -0.0011
##    140        0.6470             nan     0.1000   -0.0026
##    150        0.6345             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2588             nan     0.1000    0.0177
##      2        1.2248             nan     0.1000    0.0136
##      3        1.1999             nan     0.1000    0.0122
##      4        1.1797             nan     0.1000    0.0093
##      5        1.1613             nan     0.1000    0.0074
##      6        1.1470             nan     0.1000    0.0056
##      7        1.1313             nan     0.1000    0.0063
##      8        1.1144             nan     0.1000    0.0069
##      9        1.1018             nan     0.1000    0.0048
##     10        1.0876             nan     0.1000    0.0063
##     20        0.9940             nan     0.1000    0.0033
##     40        0.9091             nan     0.1000    0.0001
##     60        0.8679             nan     0.1000    0.0001
##     80        0.8398             nan     0.1000   -0.0006
##    100        0.8254             nan     0.1000   -0.0009
##    120        0.8144             nan     0.1000   -0.0016
##    140        0.8048             nan     0.1000   -0.0021
##    150        0.8009             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2441             nan     0.1000    0.0214
##      2        1.2002             nan     0.1000    0.0190
##      3        1.1639             nan     0.1000    0.0124
##      4        1.1319             nan     0.1000    0.0110
##      5        1.1061             nan     0.1000    0.0114
##      6        1.0881             nan     0.1000    0.0068
##      7        1.0692             nan     0.1000    0.0063
##      8        1.0523             nan     0.1000    0.0058
##      9        1.0347             nan     0.1000    0.0064
##     10        1.0200             nan     0.1000    0.0021
##     20        0.9260             nan     0.1000   -0.0003
##     40        0.8373             nan     0.1000   -0.0005
##     60        0.7956             nan     0.1000   -0.0016
##     80        0.7689             nan     0.1000   -0.0022
##    100        0.7460             nan     0.1000   -0.0012
##    120        0.7225             nan     0.1000   -0.0016
##    140        0.7027             nan     0.1000   -0.0018
##    150        0.6935             nan     0.1000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2459             nan     0.1000    0.0227
##      2        1.2002             nan     0.1000    0.0185
##      3        1.1601             nan     0.1000    0.0167
##      4        1.1257             nan     0.1000    0.0126
##      5        1.0974             nan     0.1000    0.0114
##      6        1.0755             nan     0.1000    0.0084
##      7        1.0465             nan     0.1000    0.0110
##      8        1.0299             nan     0.1000    0.0069
##      9        1.0094             nan     0.1000    0.0048
##     10        0.9974             nan     0.1000    0.0011
##     20        0.8901             nan     0.1000    0.0002
##     40        0.7924             nan     0.1000   -0.0019
##     60        0.7461             nan     0.1000   -0.0005
##     80        0.7033             nan     0.1000   -0.0013
##    100        0.6671             nan     0.1000   -0.0031
##    120        0.6377             nan     0.1000   -0.0006
##    140        0.6078             nan     0.1000   -0.0003
##    150        0.5949             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2604             nan     0.1000    0.0161
##      2        1.2289             nan     0.1000    0.0128
##      3        1.2009             nan     0.1000    0.0134
##      4        1.1795             nan     0.1000    0.0063
##      5        1.1579             nan     0.1000    0.0108
##      6        1.1401             nan     0.1000    0.0080
##      7        1.1220             nan     0.1000    0.0068
##      8        1.1067             nan     0.1000    0.0063
##      9        1.0937             nan     0.1000    0.0047
##     10        1.0813             nan     0.1000    0.0045
##     20        0.9828             nan     0.1000    0.0027
##     40        0.8983             nan     0.1000    0.0001
##     60        0.8576             nan     0.1000   -0.0015
##     80        0.8361             nan     0.1000   -0.0007
##    100        0.8167             nan     0.1000   -0.0001
##    120        0.8007             nan     0.1000   -0.0008
##    140        0.7906             nan     0.1000   -0.0012
##    150        0.7835             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2428             nan     0.1000    0.0222
##      2        1.2043             nan     0.1000    0.0181
##      3        1.1663             nan     0.1000    0.0149
##      4        1.1338             nan     0.1000    0.0132
##      5        1.1120             nan     0.1000    0.0093
##      6        1.0884             nan     0.1000    0.0100
##      7        1.0692             nan     0.1000    0.0098
##      8        1.0495             nan     0.1000    0.0062
##      9        1.0296             nan     0.1000    0.0074
##     10        1.0149             nan     0.1000    0.0046
##     20        0.9164             nan     0.1000    0.0016
##     40        0.8337             nan     0.1000    0.0011
##     60        0.7947             nan     0.1000   -0.0005
##     80        0.7612             nan     0.1000   -0.0008
##    100        0.7336             nan     0.1000   -0.0009
##    120        0.7130             nan     0.1000   -0.0023
##    140        0.6927             nan     0.1000   -0.0016
##    150        0.6845             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2347             nan     0.1000    0.0264
##      2        1.1913             nan     0.1000    0.0193
##      3        1.1514             nan     0.1000    0.0187
##      4        1.1191             nan     0.1000    0.0104
##      5        1.0881             nan     0.1000    0.0108
##      6        1.0644             nan     0.1000    0.0103
##      7        1.0391             nan     0.1000    0.0097
##      8        1.0171             nan     0.1000    0.0083
##      9        1.0007             nan     0.1000    0.0042
##     10        0.9861             nan     0.1000    0.0058
##     20        0.8745             nan     0.1000    0.0014
##     40        0.7905             nan     0.1000   -0.0012
##     60        0.7395             nan     0.1000   -0.0011
##     80        0.7009             nan     0.1000   -0.0018
##    100        0.6634             nan     0.1000   -0.0013
##    120        0.6375             nan     0.1000   -0.0012
##    140        0.6088             nan     0.1000   -0.0006
##    150        0.5930             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2554             nan     0.1000    0.0175
##      2        1.2230             nan     0.1000    0.0092
##      3        1.2007             nan     0.1000    0.0105
##      4        1.1803             nan     0.1000    0.0091
##      5        1.1614             nan     0.1000    0.0066
##      6        1.1417             nan     0.1000    0.0084
##      7        1.1269             nan     0.1000    0.0065
##      8        1.1115             nan     0.1000    0.0060
##      9        1.0966             nan     0.1000    0.0052
##     10        1.0823             nan     0.1000    0.0043
##     20        0.9995             nan     0.1000    0.0032
##     40        0.9185             nan     0.1000    0.0013
##     60        0.8829             nan     0.1000   -0.0003
##     80        0.8567             nan     0.1000   -0.0000
##    100        0.8418             nan     0.1000   -0.0012
##    120        0.8267             nan     0.1000   -0.0009
##    140        0.8208             nan     0.1000   -0.0006
##    150        0.8122             nan     0.1000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2499             nan     0.1000    0.0165
##      2        1.2134             nan     0.1000    0.0170
##      3        1.1822             nan     0.1000    0.0146
##      4        1.1521             nan     0.1000    0.0119
##      5        1.1249             nan     0.1000    0.0087
##      6        1.0998             nan     0.1000    0.0118
##      7        1.0842             nan     0.1000    0.0050
##      8        1.0679             nan     0.1000    0.0068
##      9        1.0499             nan     0.1000    0.0069
##     10        1.0373             nan     0.1000    0.0054
##     20        0.9385             nan     0.1000    0.0013
##     40        0.8618             nan     0.1000   -0.0023
##     60        0.8185             nan     0.1000   -0.0011
##     80        0.7899             nan     0.1000   -0.0021
##    100        0.7590             nan     0.1000   -0.0008
##    120        0.7346             nan     0.1000   -0.0007
##    140        0.7157             nan     0.1000   -0.0006
##    150        0.7058             nan     0.1000   -0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2427             nan     0.1000    0.0203
##      2        1.1931             nan     0.1000    0.0191
##      3        1.1512             nan     0.1000    0.0165
##      4        1.1238             nan     0.1000    0.0118
##      5        1.0978             nan     0.1000    0.0121
##      6        1.0669             nan     0.1000    0.0119
##      7        1.0459             nan     0.1000    0.0087
##      8        1.0277             nan     0.1000    0.0073
##      9        1.0092             nan     0.1000    0.0071
##     10        0.9929             nan     0.1000    0.0053
##     20        0.8939             nan     0.1000    0.0003
##     40        0.8082             nan     0.1000   -0.0014
##     60        0.7541             nan     0.1000   -0.0014
##     80        0.7211             nan     0.1000   -0.0014
##    100        0.6899             nan     0.1000   -0.0018
##    120        0.6546             nan     0.1000   -0.0033
##    140        0.6261             nan     0.1000   -0.0013
##    150        0.6117             nan     0.1000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2609             nan     0.1000    0.0160
##      2        1.2360             nan     0.1000    0.0138
##      3        1.2128             nan     0.1000    0.0094
##      4        1.1879             nan     0.1000    0.0113
##      5        1.1665             nan     0.1000    0.0088
##      6        1.1517             nan     0.1000    0.0059
##      7        1.1345             nan     0.1000    0.0059
##      8        1.1206             nan     0.1000    0.0036
##      9        1.1039             nan     0.1000    0.0062
##     10        1.0921             nan     0.1000    0.0038
##     20        1.0068             nan     0.1000    0.0018
##     40        0.9243             nan     0.1000    0.0001
##     60        0.8870             nan     0.1000   -0.0007
##     80        0.8606             nan     0.1000   -0.0004
##    100        0.8464             nan     0.1000   -0.0001
##    120        0.8356             nan     0.1000   -0.0008
##    140        0.8277             nan     0.1000   -0.0014
##    150        0.8254             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2473             nan     0.1000    0.0217
##      2        1.2092             nan     0.1000    0.0163
##      3        1.1816             nan     0.1000    0.0139
##      4        1.1541             nan     0.1000    0.0127
##      5        1.1286             nan     0.1000    0.0119
##      6        1.1090             nan     0.1000    0.0074
##      7        1.0927             nan     0.1000    0.0054
##      8        1.0754             nan     0.1000    0.0071
##      9        1.0549             nan     0.1000    0.0075
##     10        1.0427             nan     0.1000    0.0050
##     20        0.9414             nan     0.1000    0.0018
##     40        0.8625             nan     0.1000   -0.0002
##     60        0.8275             nan     0.1000   -0.0045
##     80        0.7967             nan     0.1000   -0.0014
##    100        0.7785             nan     0.1000   -0.0007
##    120        0.7561             nan     0.1000   -0.0011
##    140        0.7344             nan     0.1000   -0.0012
##    150        0.7238             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2354             nan     0.1000    0.0233
##      2        1.1950             nan     0.1000    0.0182
##      3        1.1602             nan     0.1000    0.0141
##      4        1.1309             nan     0.1000    0.0113
##      5        1.1052             nan     0.1000    0.0091
##      6        1.0792             nan     0.1000    0.0100
##      7        1.0573             nan     0.1000    0.0087
##      8        1.0398             nan     0.1000    0.0053
##      9        1.0210             nan     0.1000    0.0053
##     10        1.0046             nan     0.1000    0.0065
##     20        0.9033             nan     0.1000    0.0021
##     40        0.8193             nan     0.1000   -0.0006
##     60        0.7709             nan     0.1000   -0.0038
##     80        0.7365             nan     0.1000   -0.0016
##    100        0.6960             nan     0.1000   -0.0014
##    120        0.6668             nan     0.1000   -0.0023
##    140        0.6387             nan     0.1000   -0.0020
##    150        0.6214             nan     0.1000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2584             nan     0.1000    0.0145
##      2        1.2297             nan     0.1000    0.0141
##      3        1.2071             nan     0.1000    0.0096
##      4        1.1879             nan     0.1000    0.0078
##      5        1.1681             nan     0.1000    0.0089
##      6        1.1499             nan     0.1000    0.0064
##      7        1.1344             nan     0.1000    0.0057
##      8        1.1200             nan     0.1000    0.0053
##      9        1.1075             nan     0.1000    0.0062
##     10        1.0949             nan     0.1000    0.0052
##     20        1.0053             nan     0.1000    0.0013
##     40        0.9269             nan     0.1000   -0.0002
##     60        0.8859             nan     0.1000   -0.0002
##     80        0.8615             nan     0.1000   -0.0005
##    100        0.8422             nan     0.1000   -0.0006
##    120        0.8289             nan     0.1000   -0.0009
##    140        0.8181             nan     0.1000   -0.0006
##    150        0.8140             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2497             nan     0.1000    0.0176
##      2        1.2109             nan     0.1000    0.0157
##      3        1.1813             nan     0.1000    0.0132
##      4        1.1551             nan     0.1000    0.0129
##      5        1.1346             nan     0.1000    0.0090
##      6        1.1114             nan     0.1000    0.0095
##      7        1.0898             nan     0.1000    0.0086
##      8        1.0711             nan     0.1000    0.0085
##      9        1.0510             nan     0.1000    0.0066
##     10        1.0385             nan     0.1000    0.0056
##     20        0.9494             nan     0.1000    0.0022
##     40        0.8727             nan     0.1000   -0.0006
##     60        0.8271             nan     0.1000   -0.0021
##     80        0.7911             nan     0.1000   -0.0012
##    100        0.7657             nan     0.1000   -0.0026
##    120        0.7394             nan     0.1000   -0.0017
##    140        0.7186             nan     0.1000   -0.0007
##    150        0.7083             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2352             nan     0.1000    0.0274
##      2        1.1929             nan     0.1000    0.0180
##      3        1.1543             nan     0.1000    0.0181
##      4        1.1228             nan     0.1000    0.0110
##      5        1.0932             nan     0.1000    0.0120
##      6        1.0685             nan     0.1000    0.0132
##      7        1.0509             nan     0.1000    0.0049
##      8        1.0324             nan     0.1000    0.0061
##      9        1.0157             nan     0.1000    0.0052
##     10        0.9979             nan     0.1000    0.0057
##     20        0.9058             nan     0.1000    0.0004
##     40        0.8109             nan     0.1000   -0.0006
##     60        0.7665             nan     0.1000   -0.0003
##     80        0.7258             nan     0.1000   -0.0018
##    100        0.6947             nan     0.1000   -0.0019
##    120        0.6651             nan     0.1000   -0.0015
##    140        0.6372             nan     0.1000   -0.0020
##    150        0.6228             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2632             nan     0.1000    0.0153
##      2        1.2291             nan     0.1000    0.0138
##      3        1.2039             nan     0.1000    0.0102
##      4        1.1797             nan     0.1000    0.0098
##      5        1.1606             nan     0.1000    0.0065
##      6        1.1408             nan     0.1000    0.0071
##      7        1.1242             nan     0.1000    0.0055
##      8        1.1091             nan     0.1000    0.0076
##      9        1.0934             nan     0.1000    0.0059
##     10        1.0803             nan     0.1000    0.0056
##     20        0.9898             nan     0.1000    0.0014
##     40        0.9092             nan     0.1000    0.0005
##     60        0.8706             nan     0.1000   -0.0005
##     80        0.8433             nan     0.1000    0.0001
##    100        0.8263             nan     0.1000   -0.0010
##    120        0.8154             nan     0.1000   -0.0004
##    140        0.8049             nan     0.1000   -0.0015
##    150        0.8009             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2442             nan     0.1000    0.0178
##      2        1.2036             nan     0.1000    0.0159
##      3        1.1688             nan     0.1000    0.0143
##      4        1.1408             nan     0.1000    0.0117
##      5        1.1191             nan     0.1000    0.0072
##      6        1.0957             nan     0.1000    0.0087
##      7        1.0807             nan     0.1000    0.0057
##      8        1.0592             nan     0.1000    0.0103
##      9        1.0413             nan     0.1000    0.0066
##     10        1.0272             nan     0.1000    0.0050
##     20        0.9290             nan     0.1000    0.0019
##     40        0.8438             nan     0.1000   -0.0004
##     60        0.7987             nan     0.1000   -0.0002
##     80        0.7696             nan     0.1000   -0.0015
##    100        0.7467             nan     0.1000   -0.0013
##    120        0.7288             nan     0.1000   -0.0007
##    140        0.7114             nan     0.1000   -0.0015
##    150        0.7034             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2453             nan     0.1000    0.0218
##      2        1.2026             nan     0.1000    0.0183
##      3        1.1589             nan     0.1000    0.0187
##      4        1.1282             nan     0.1000    0.0113
##      5        1.1056             nan     0.1000    0.0084
##      6        1.0764             nan     0.1000    0.0114
##      7        1.0534             nan     0.1000    0.0094
##      8        1.0347             nan     0.1000    0.0090
##      9        1.0154             nan     0.1000    0.0079
##     10        0.9975             nan     0.1000    0.0063
##     20        0.8923             nan     0.1000    0.0001
##     40        0.7994             nan     0.1000   -0.0015
##     60        0.7485             nan     0.1000   -0.0027
##     80        0.7041             nan     0.1000   -0.0018
##    100        0.6705             nan     0.1000   -0.0014
##    120        0.6424             nan     0.1000   -0.0018
##    140        0.6166             nan     0.1000   -0.0024
##    150        0.6013             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2577             nan     0.1000    0.0166
##      2        1.2251             nan     0.1000    0.0146
##      3        1.1989             nan     0.1000    0.0121
##      4        1.1787             nan     0.1000    0.0088
##      5        1.1598             nan     0.1000    0.0077
##      6        1.1427             nan     0.1000    0.0067
##      7        1.1293             nan     0.1000    0.0035
##      8        1.1161             nan     0.1000    0.0066
##      9        1.1009             nan     0.1000    0.0068
##     10        1.0871             nan     0.1000    0.0052
##     20        0.9982             nan     0.1000    0.0031
##     40        0.9145             nan     0.1000    0.0006
##     60        0.8752             nan     0.1000    0.0007
##     80        0.8474             nan     0.1000   -0.0007
##    100        0.8331             nan     0.1000   -0.0010
##    120        0.8182             nan     0.1000   -0.0011
##    140        0.8075             nan     0.1000   -0.0010
##    150        0.8031             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2487             nan     0.1000    0.0222
##      2        1.2127             nan     0.1000    0.0168
##      3        1.1787             nan     0.1000    0.0158
##      4        1.1493             nan     0.1000    0.0154
##      5        1.1244             nan     0.1000    0.0063
##      6        1.1020             nan     0.1000    0.0106
##      7        1.0858             nan     0.1000    0.0066
##      8        1.0669             nan     0.1000    0.0059
##      9        1.0511             nan     0.1000    0.0058
##     10        1.0343             nan     0.1000    0.0053
##     20        0.9298             nan     0.1000    0.0026
##     40        0.8477             nan     0.1000   -0.0004
##     60        0.8006             nan     0.1000   -0.0001
##     80        0.7749             nan     0.1000   -0.0011
##    100        0.7498             nan     0.1000   -0.0005
##    120        0.7248             nan     0.1000   -0.0018
##    140        0.7050             nan     0.1000   -0.0013
##    150        0.6919             nan     0.1000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2355             nan     0.1000    0.0262
##      2        1.1927             nan     0.1000    0.0184
##      3        1.1544             nan     0.1000    0.0163
##      4        1.1206             nan     0.1000    0.0136
##      5        1.0935             nan     0.1000    0.0119
##      6        1.0675             nan     0.1000    0.0103
##      7        1.0455             nan     0.1000    0.0073
##      8        1.0273             nan     0.1000    0.0074
##      9        1.0044             nan     0.1000    0.0098
##     10        0.9871             nan     0.1000    0.0053
##     20        0.8860             nan     0.1000   -0.0001
##     40        0.7999             nan     0.1000   -0.0016
##     60        0.7498             nan     0.1000   -0.0016
##     80        0.7115             nan     0.1000   -0.0015
##    100        0.6750             nan     0.1000   -0.0009
##    120        0.6420             nan     0.1000   -0.0010
##    140        0.6109             nan     0.1000   -0.0024
##    150        0.5986             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2541             nan     0.1000    0.0150
##      2        1.2267             nan     0.1000    0.0133
##      3        1.2056             nan     0.1000    0.0100
##      4        1.1825             nan     0.1000    0.0105
##      5        1.1635             nan     0.1000    0.0080
##      6        1.1442             nan     0.1000    0.0056
##      7        1.1286             nan     0.1000    0.0072
##      8        1.1148             nan     0.1000    0.0055
##      9        1.1038             nan     0.1000    0.0048
##     10        1.0923             nan     0.1000    0.0050
##     20        1.0047             nan     0.1000    0.0011
##     40        0.9263             nan     0.1000   -0.0002
##     60        0.8889             nan     0.1000   -0.0007
##     80        0.8650             nan     0.1000   -0.0006
##    100        0.8489             nan     0.1000   -0.0002
##    120        0.8347             nan     0.1000   -0.0005
##    140        0.8242             nan     0.1000   -0.0012
##    150        0.8194             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2483             nan     0.1000    0.0229
##      2        1.2047             nan     0.1000    0.0205
##      3        1.1755             nan     0.1000    0.0120
##      4        1.1431             nan     0.1000    0.0114
##      5        1.1171             nan     0.1000    0.0096
##      6        1.0990             nan     0.1000    0.0079
##      7        1.0792             nan     0.1000    0.0078
##      8        1.0631             nan     0.1000    0.0057
##      9        1.0499             nan     0.1000    0.0046
##     10        1.0365             nan     0.1000    0.0055
##     20        0.9468             nan     0.1000    0.0002
##     40        0.8664             nan     0.1000    0.0001
##     60        0.8288             nan     0.1000   -0.0009
##     80        0.7989             nan     0.1000   -0.0014
##    100        0.7728             nan     0.1000   -0.0016
##    120        0.7508             nan     0.1000   -0.0012
##    140        0.7276             nan     0.1000   -0.0015
##    150        0.7199             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2428             nan     0.1000    0.0226
##      2        1.2011             nan     0.1000    0.0184
##      3        1.1666             nan     0.1000    0.0138
##      4        1.1364             nan     0.1000    0.0115
##      5        1.1081             nan     0.1000    0.0109
##      6        1.0847             nan     0.1000    0.0094
##      7        1.0651             nan     0.1000    0.0071
##      8        1.0467             nan     0.1000    0.0055
##      9        1.0260             nan     0.1000    0.0050
##     10        1.0090             nan     0.1000    0.0075
##     20        0.9064             nan     0.1000    0.0005
##     40        0.8099             nan     0.1000   -0.0006
##     60        0.7637             nan     0.1000   -0.0011
##     80        0.7272             nan     0.1000   -0.0017
##    100        0.6889             nan     0.1000   -0.0007
##    120        0.6601             nan     0.1000   -0.0018
##    140        0.6341             nan     0.1000   -0.0024
##    150        0.6200             nan     0.1000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2543             nan     0.1000    0.0165
##      2        1.2200             nan     0.1000    0.0147
##      3        1.1914             nan     0.1000    0.0110
##      4        1.1682             nan     0.1000    0.0094
##      5        1.1503             nan     0.1000    0.0080
##      6        1.1336             nan     0.1000    0.0060
##      7        1.1199             nan     0.1000    0.0053
##      8        1.1077             nan     0.1000    0.0047
##      9        1.0922             nan     0.1000    0.0072
##     10        1.0819             nan     0.1000    0.0022
##     20        0.9873             nan     0.1000    0.0010
##     40        0.9071             nan     0.1000    0.0001
##     60        0.8689             nan     0.1000    0.0001
##     80        0.8461             nan     0.1000   -0.0006
##    100        0.8292             nan     0.1000   -0.0004
##    120        0.8165             nan     0.1000   -0.0012
##    140        0.8091             nan     0.1000   -0.0019
##    150        0.8048             nan     0.1000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2466             nan     0.1000    0.0182
##      2        1.2045             nan     0.1000    0.0162
##      3        1.1716             nan     0.1000    0.0159
##      4        1.1404             nan     0.1000    0.0128
##      5        1.1170             nan     0.1000    0.0115
##      6        1.0949             nan     0.1000    0.0087
##      7        1.0745             nan     0.1000    0.0078
##      8        1.0561             nan     0.1000    0.0067
##      9        1.0408             nan     0.1000    0.0040
##     10        1.0282             nan     0.1000    0.0047
##     20        0.9328             nan     0.1000    0.0006
##     40        0.8473             nan     0.1000   -0.0000
##     60        0.8062             nan     0.1000   -0.0008
##     80        0.7816             nan     0.1000   -0.0011
##    100        0.7605             nan     0.1000   -0.0017
##    120        0.7376             nan     0.1000   -0.0019
##    140        0.7149             nan     0.1000   -0.0017
##    150        0.7037             nan     0.1000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2465             nan     0.1000    0.0202
##      2        1.2021             nan     0.1000    0.0202
##      3        1.1638             nan     0.1000    0.0167
##      4        1.1256             nan     0.1000    0.0131
##      5        1.1000             nan     0.1000    0.0096
##      6        1.0795             nan     0.1000    0.0049
##      7        1.0587             nan     0.1000    0.0063
##      8        1.0389             nan     0.1000    0.0073
##      9        1.0211             nan     0.1000    0.0050
##     10        1.0011             nan     0.1000    0.0071
##     20        0.8908             nan     0.1000    0.0010
##     40        0.8010             nan     0.1000   -0.0015
##     60        0.7423             nan     0.1000   -0.0013
##     80        0.7044             nan     0.1000   -0.0015
##    100        0.6747             nan     0.1000   -0.0028
##    120        0.6432             nan     0.1000   -0.0017
##    140        0.6159             nan     0.1000   -0.0016
##    150        0.6012             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2524             nan     0.1000    0.0152
##      2        1.2269             nan     0.1000    0.0120
##      3        1.2038             nan     0.1000    0.0123
##      4        1.1816             nan     0.1000    0.0104
##      5        1.1655             nan     0.1000    0.0078
##      6        1.1475             nan     0.1000    0.0071
##      7        1.1301             nan     0.1000    0.0065
##      8        1.1169             nan     0.1000    0.0040
##      9        1.1045             nan     0.1000    0.0048
##     10        1.0940             nan     0.1000    0.0032
##     20        1.0107             nan     0.1000   -0.0016
##     40        0.9237             nan     0.1000    0.0004
##     60        0.8881             nan     0.1000   -0.0010
##     80        0.8638             nan     0.1000   -0.0006
##    100        0.8479             nan     0.1000   -0.0007
##    120        0.8382             nan     0.1000   -0.0010
##    140        0.8282             nan     0.1000   -0.0005
##    150        0.8218             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2498             nan     0.1000    0.0200
##      2        1.2133             nan     0.1000    0.0170
##      3        1.1781             nan     0.1000    0.0152
##      4        1.1579             nan     0.1000    0.0078
##      5        1.1336             nan     0.1000    0.0105
##      6        1.1101             nan     0.1000    0.0090
##      7        1.0874             nan     0.1000    0.0103
##      8        1.0699             nan     0.1000    0.0078
##      9        1.0569             nan     0.1000    0.0048
##     10        1.0407             nan     0.1000    0.0067
##     20        0.9364             nan     0.1000    0.0011
##     40        0.8583             nan     0.1000   -0.0024
##     60        0.8166             nan     0.1000   -0.0021
##     80        0.7959             nan     0.1000   -0.0014
##    100        0.7593             nan     0.1000   -0.0014
##    120        0.7349             nan     0.1000   -0.0009
##    140        0.7151             nan     0.1000   -0.0004
##    150        0.7058             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2459             nan     0.1000    0.0213
##      2        1.1948             nan     0.1000    0.0226
##      3        1.1580             nan     0.1000    0.0173
##      4        1.1297             nan     0.1000    0.0131
##      5        1.1038             nan     0.1000    0.0112
##      6        1.0832             nan     0.1000    0.0089
##      7        1.0627             nan     0.1000    0.0070
##      8        1.0443             nan     0.1000    0.0067
##      9        1.0210             nan     0.1000    0.0094
##     10        1.0037             nan     0.1000    0.0064
##     20        0.8941             nan     0.1000    0.0013
##     40        0.8006             nan     0.1000   -0.0007
##     60        0.7469             nan     0.1000   -0.0012
##     80        0.7090             nan     0.1000   -0.0018
##    100        0.6785             nan     0.1000   -0.0020
##    120        0.6506             nan     0.1000   -0.0020
##    140        0.6248             nan     0.1000   -0.0014
##    150        0.6128             nan     0.1000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2555             nan     0.1000    0.0161
##      2        1.2264             nan     0.1000    0.0134
##      3        1.2041             nan     0.1000    0.0071
##      4        1.1804             nan     0.1000    0.0094
##      5        1.1614             nan     0.1000    0.0085
##      6        1.1426             nan     0.1000    0.0078
##      7        1.1248             nan     0.1000    0.0065
##      8        1.1095             nan     0.1000    0.0062
##      9        1.0957             nan     0.1000    0.0063
##     10        1.0845             nan     0.1000    0.0038
##     20        0.9973             nan     0.1000    0.0017
##     40        0.9239             nan     0.1000    0.0012
##     60        0.8870             nan     0.1000   -0.0001
##     80        0.8572             nan     0.1000   -0.0006
##    100        0.8423             nan     0.1000   -0.0011
##    120        0.8274             nan     0.1000   -0.0007
##    140        0.8190             nan     0.1000   -0.0016
##    150        0.8157             nan     0.1000    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2521             nan     0.1000    0.0197
##      2        1.2117             nan     0.1000    0.0163
##      3        1.1741             nan     0.1000    0.0135
##      4        1.1453             nan     0.1000    0.0080
##      5        1.1181             nan     0.1000    0.0127
##      6        1.0946             nan     0.1000    0.0098
##      7        1.0755             nan     0.1000    0.0077
##      8        1.0574             nan     0.1000    0.0077
##      9        1.0429             nan     0.1000    0.0057
##     10        1.0278             nan     0.1000    0.0047
##     20        0.9322             nan     0.1000    0.0015
##     40        0.8564             nan     0.1000   -0.0004
##     60        0.8128             nan     0.1000   -0.0008
##     80        0.7847             nan     0.1000   -0.0014
##    100        0.7648             nan     0.1000   -0.0021
##    120        0.7441             nan     0.1000   -0.0017
##    140        0.7235             nan     0.1000   -0.0014
##    150        0.7158             nan     0.1000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2425             nan     0.1000    0.0246
##      2        1.1979             nan     0.1000    0.0231
##      3        1.1614             nan     0.1000    0.0156
##      4        1.1285             nan     0.1000    0.0158
##      5        1.1010             nan     0.1000    0.0128
##      6        1.0776             nan     0.1000    0.0076
##      7        1.0510             nan     0.1000    0.0119
##      8        1.0329             nan     0.1000    0.0050
##      9        1.0141             nan     0.1000    0.0064
##     10        0.9963             nan     0.1000    0.0072
##     20        0.8984             nan     0.1000    0.0014
##     40        0.8148             nan     0.1000   -0.0011
##     60        0.7619             nan     0.1000   -0.0009
##     80        0.7237             nan     0.1000   -0.0019
##    100        0.6905             nan     0.1000   -0.0008
##    120        0.6609             nan     0.1000   -0.0025
##    140        0.6288             nan     0.1000   -0.0012
##    150        0.6177             nan     0.1000   -0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2584             nan     0.1000    0.0179
##      2        1.2286             nan     0.1000    0.0141
##      3        1.2005             nan     0.1000    0.0123
##      4        1.1801             nan     0.1000    0.0103
##      5        1.1590             nan     0.1000    0.0075
##      6        1.1439             nan     0.1000    0.0069
##      7        1.1286             nan     0.1000    0.0067
##      8        1.1151             nan     0.1000    0.0061
##      9        1.1025             nan     0.1000    0.0051
##     10        1.0876             nan     0.1000    0.0058
##     20        0.9961             nan     0.1000    0.0027
##     40        0.9150             nan     0.1000   -0.0002
##     60        0.8759             nan     0.1000    0.0002
##     80        0.8486             nan     0.1000   -0.0002
##    100        0.8321             nan     0.1000   -0.0004
##    120        0.8209             nan     0.1000   -0.0013
##    140        0.8102             nan     0.1000   -0.0011
##    150        0.8017             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2485             nan     0.1000    0.0178
##      2        1.2154             nan     0.1000    0.0147
##      3        1.1811             nan     0.1000    0.0140
##      4        1.1545             nan     0.1000    0.0101
##      5        1.1215             nan     0.1000    0.0138
##      6        1.0986             nan     0.1000    0.0089
##      7        1.0845             nan     0.1000    0.0048
##      8        1.0706             nan     0.1000    0.0041
##      9        1.0513             nan     0.1000    0.0075
##     10        1.0367             nan     0.1000    0.0052
##     20        0.9394             nan     0.1000    0.0012
##     40        0.8575             nan     0.1000   -0.0012
##     60        0.8079             nan     0.1000   -0.0007
##     80        0.7761             nan     0.1000   -0.0020
##    100        0.7515             nan     0.1000   -0.0013
##    120        0.7268             nan     0.1000   -0.0008
##    140        0.7082             nan     0.1000   -0.0005
##    150        0.6973             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2413             nan     0.1000    0.0218
##      2        1.1977             nan     0.1000    0.0181
##      3        1.1531             nan     0.1000    0.0178
##      4        1.1249             nan     0.1000    0.0086
##      5        1.0966             nan     0.1000    0.0117
##      6        1.0743             nan     0.1000    0.0070
##      7        1.0525             nan     0.1000    0.0096
##      8        1.0348             nan     0.1000    0.0067
##      9        1.0146             nan     0.1000    0.0056
##     10        0.9970             nan     0.1000    0.0048
##     20        0.8945             nan     0.1000    0.0002
##     40        0.8058             nan     0.1000   -0.0008
##     60        0.7520             nan     0.1000   -0.0023
##     80        0.7180             nan     0.1000   -0.0020
##    100        0.6813             nan     0.1000   -0.0021
##    120        0.6508             nan     0.1000   -0.0019
##    140        0.6255             nan     0.1000   -0.0030
##    150        0.6129             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2590             nan     0.1000    0.0159
##      2        1.2306             nan     0.1000    0.0143
##      3        1.2059             nan     0.1000    0.0124
##      4        1.1854             nan     0.1000    0.0096
##      5        1.1666             nan     0.1000    0.0083
##      6        1.1503             nan     0.1000    0.0067
##      7        1.1330             nan     0.1000    0.0068
##      8        1.1167             nan     0.1000    0.0063
##      9        1.1023             nan     0.1000    0.0052
##     10        1.0897             nan     0.1000    0.0046
##     20        1.0097             nan     0.1000    0.0012
##     40        0.9248             nan     0.1000    0.0009
##     50        0.9059             nan     0.1000   -0.0003
# Neural Network (Regression/Classification)
set.seed(seedNum)
fit.nnet <- train(classVar~., data=dataset, method="nnet", metric=metricTarget, trControl=control)
## # weights:  11
## initial  value 450.372635 
## iter  10 value 429.578924
## iter  20 value 419.197576
## iter  30 value 414.246101
## iter  40 value 410.558579
## iter  50 value 410.503009
## iter  60 value 410.502560
## final  value 410.502221 
## converged
## # weights:  31
## initial  value 696.076489 
## iter  10 value 443.195613
## iter  20 value 429.180927
## iter  30 value 414.389302
## iter  40 value 402.897431
## iter  50 value 336.932200
## iter  60 value 329.881527
## iter  70 value 320.392704
## iter  80 value 319.107903
## final  value 319.001506 
## converged
## # weights:  51
## initial  value 537.788813 
## iter  10 value 431.174234
## iter  20 value 420.707494
## iter  30 value 410.046855
## iter  40 value 401.101865
## iter  50 value 391.799571
## iter  60 value 378.901790
## iter  70 value 370.738479
## iter  80 value 367.838536
## iter  90 value 366.041697
## iter 100 value 366.016847
## final  value 366.016847 
## stopped after 100 iterations
## # weights:  11
## initial  value 474.874962 
## iter  10 value 415.416545
## iter  20 value 405.109574
## iter  30 value 397.787977
## iter  40 value 361.688742
## iter  50 value 335.359452
## iter  60 value 327.973078
## iter  70 value 327.948475
## final  value 327.945863 
## converged
## # weights:  31
## initial  value 580.188871 
## iter  10 value 444.541557
## iter  20 value 442.999851
## iter  30 value 436.793082
## iter  40 value 423.894469
## iter  50 value 417.083658
## iter  60 value 411.870446
## iter  70 value 407.633013
## iter  80 value 387.685927
## iter  90 value 375.488335
## iter 100 value 334.051607
## final  value 334.051607 
## stopped after 100 iterations
## # weights:  51
## initial  value 462.975170 
## iter  10 value 437.386889
## iter  20 value 420.423014
## iter  30 value 406.224649
## iter  40 value 397.516148
## iter  50 value 390.374211
## iter  60 value 385.480793
## iter  70 value 381.477597
## iter  80 value 378.167628
## iter  90 value 369.813808
## iter 100 value 366.018396
## final  value 366.018396 
## stopped after 100 iterations
## # weights:  11
## initial  value 463.515306 
## iter  10 value 427.242179
## iter  20 value 421.396228
## iter  30 value 420.564806
## iter  40 value 420.486550
## iter  50 value 420.337944
## iter  60 value 420.335729
## iter  70 value 420.334329
## iter  70 value 420.334328
## iter  70 value 420.334328
## final  value 420.334328 
## converged
## # weights:  31
## initial  value 658.846597 
## final  value 447.912497 
## converged
## # weights:  51
## initial  value 573.491987 
## iter  10 value 428.910980
## iter  20 value 410.350760
## iter  30 value 398.221498
## iter  40 value 394.876130
## iter  50 value 393.677236
## iter  60 value 392.612204
## iter  70 value 391.366739
## iter  80 value 388.793965
## iter  90 value 388.704387
## iter 100 value 388.693578
## final  value 388.693578 
## stopped after 100 iterations
## # weights:  11
## initial  value 448.979188 
## iter  10 value 437.765827
## iter  20 value 429.446108
## iter  30 value 415.432297
## iter  40 value 413.889404
## iter  50 value 410.911600
## iter  60 value 369.210002
## iter  70 value 350.945359
## iter  80 value 343.550709
## iter  90 value 343.386826
## iter  90 value 343.386824
## iter  90 value 343.386824
## final  value 343.386824 
## converged
## # weights:  31
## initial  value 498.349294 
## iter  10 value 435.078555
## iter  20 value 431.168435
## iter  30 value 428.038860
## iter  40 value 426.558850
## iter  50 value 426.208760
## iter  60 value 425.986498
## iter  70 value 425.980704
## iter  80 value 425.978223
## iter  90 value 425.970695
## iter 100 value 425.968029
## final  value 425.968029 
## stopped after 100 iterations
## # weights:  51
## initial  value 537.086533 
## iter  10 value 421.653374
## iter  20 value 402.413294
## iter  30 value 393.458974
## iter  40 value 383.526757
## iter  50 value 378.930981
## iter  60 value 372.088746
## iter  70 value 366.821913
## iter  80 value 356.882600
## iter  90 value 340.643090
## iter 100 value 322.527060
## final  value 322.527060 
## stopped after 100 iterations
## # weights:  11
## initial  value 585.591995 
## iter  10 value 426.836790
## iter  20 value 404.594005
## iter  30 value 402.682350
## iter  40 value 401.184122
## iter  50 value 400.555135
## iter  60 value 400.383352
## final  value 400.383327 
## converged
## # weights:  31
## initial  value 439.805407 
## iter  10 value 432.128374
## iter  20 value 418.377441
## iter  30 value 400.942525
## iter  40 value 395.571036
## iter  50 value 394.297539
## iter  60 value 392.093827
## iter  70 value 386.984343
## iter  80 value 377.278217
## iter  90 value 355.391423
## iter 100 value 327.494825
## final  value 327.494825 
## stopped after 100 iterations
## # weights:  51
## initial  value 448.281594 
## iter  10 value 426.919165
## iter  20 value 414.972421
## iter  30 value 407.380738
## iter  40 value 399.813945
## iter  50 value 390.371223
## iter  60 value 372.010344
## iter  70 value 365.845718
## iter  80 value 351.341629
## iter  90 value 332.498372
## iter 100 value 315.599847
## final  value 315.599847 
## stopped after 100 iterations
## # weights:  11
## initial  value 447.203247 
## iter  10 value 414.258513
## iter  20 value 406.682718
## iter  30 value 400.183083
## iter  40 value 399.993728
## iter  50 value 399.922427
## iter  60 value 399.898810
## iter  70 value 399.890088
## iter  80 value 399.873932
## iter  90 value 399.870820
## iter 100 value 399.868554
## final  value 399.868554 
## stopped after 100 iterations
## # weights:  31
## initial  value 451.381609 
## iter  10 value 427.470101
## iter  20 value 424.901879
## iter  30 value 423.901323
## iter  40 value 423.894170
## iter  50 value 423.890581
## iter  60 value 423.883169
## iter  70 value 423.881276
## final  value 423.881099 
## converged
## # weights:  51
## initial  value 469.961864 
## iter  10 value 434.063588
## iter  20 value 419.529918
## iter  30 value 413.202631
## iter  40 value 399.640868
## iter  50 value 386.061239
## iter  60 value 380.043701
## iter  70 value 374.773466
## iter  80 value 371.969350
## iter  90 value 369.062747
## iter 100 value 368.340243
## final  value 368.340243 
## stopped after 100 iterations
## # weights:  11
## initial  value 494.598261 
## iter  10 value 435.809815
## iter  20 value 434.549735
## iter  30 value 434.366636
## iter  40 value 433.587320
## iter  50 value 433.121333
## final  value 433.074344 
## converged
## # weights:  31
## initial  value 450.159384 
## iter  10 value 441.658749
## iter  20 value 416.926771
## iter  30 value 403.681945
## iter  40 value 399.864349
## iter  50 value 396.231265
## iter  60 value 395.685216
## final  value 395.684507 
## converged
## # weights:  51
## initial  value 468.407545 
## iter  10 value 414.317319
## iter  20 value 395.713545
## iter  30 value 390.341958
## iter  40 value 385.856942
## iter  50 value 384.376778
## iter  60 value 384.067556
## iter  70 value 384.065825
## iter  70 value 384.065822
## iter  70 value 384.065822
## final  value 384.065822 
## converged
## # weights:  11
## initial  value 447.753343 
## iter  10 value 446.034426
## iter  20 value 442.992093
## iter  30 value 423.019945
## iter  40 value 408.503328
## iter  50 value 398.240708
## iter  60 value 380.965700
## iter  70 value 339.563956
## iter  80 value 331.431908
## iter  90 value 331.141279
## final  value 331.141263 
## converged
## # weights:  31
## initial  value 531.088624 
## iter  10 value 431.369996
## iter  20 value 423.362819
## iter  30 value 417.888596
## iter  40 value 401.624298
## iter  50 value 390.291827
## iter  60 value 388.421720
## iter  70 value 386.421099
## iter  80 value 372.731222
## iter  90 value 337.165314
## iter 100 value 330.467932
## final  value 330.467932 
## stopped after 100 iterations
## # weights:  51
## initial  value 443.879888 
## iter  10 value 419.566234
## iter  20 value 399.948393
## iter  30 value 391.476302
## iter  40 value 383.070671
## iter  50 value 376.679949
## iter  60 value 370.352223
## iter  70 value 368.091476
## iter  80 value 342.508058
## iter  90 value 332.563880
## iter 100 value 324.383281
## final  value 324.383281 
## stopped after 100 iterations
## # weights:  11
## initial  value 455.298125 
## final  value 446.857343 
## converged
## # weights:  31
## initial  value 447.839525 
## iter  10 value 434.737668
## iter  20 value 430.958443
## iter  30 value 429.501840
## iter  40 value 427.995451
## iter  50 value 427.432051
## iter  60 value 427.199123
## iter  70 value 427.193574
## iter  80 value 427.191469
## final  value 427.190852 
## converged
## # weights:  51
## initial  value 667.567011 
## iter  10 value 435.535166
## iter  20 value 433.778838
## iter  30 value 432.719716
## iter  40 value 432.101323
## iter  50 value 431.742465
## iter  60 value 431.737903
## final  value 431.737462 
## converged
## # weights:  11
## initial  value 516.219146 
## iter  10 value 442.960851
## iter  20 value 442.959269
## final  value 442.958963 
## converged
## # weights:  31
## initial  value 602.249111 
## iter  10 value 425.510539
## iter  20 value 416.290554
## iter  30 value 406.274502
## iter  40 value 403.292301
## iter  50 value 401.709112
## iter  60 value 400.591596
## iter  70 value 400.226452
## iter  80 value 400.115268
## iter  90 value 399.988481
## iter 100 value 399.961042
## final  value 399.961042 
## stopped after 100 iterations
## # weights:  51
## initial  value 496.104416 
## iter  10 value 439.667637
## iter  20 value 416.154832
## iter  30 value 406.333181
## iter  40 value 402.467088
## iter  50 value 400.723577
## iter  60 value 398.295640
## iter  70 value 398.218493
## final  value 398.217724 
## converged
## # weights:  11
## initial  value 446.997311 
## iter  10 value 446.887800
## iter  20 value 443.109264
## iter  30 value 437.552946
## iter  40 value 435.034675
## iter  50 value 430.905534
## iter  60 value 411.928219
## iter  70 value 381.458489
## iter  80 value 354.938856
## iter  90 value 335.384839
## iter 100 value 334.366971
## final  value 334.366971 
## stopped after 100 iterations
## # weights:  31
## initial  value 487.876753 
## iter  10 value 439.727113
## iter  20 value 428.345227
## iter  30 value 411.216634
## iter  40 value 405.374688
## iter  50 value 399.889927
## iter  60 value 396.886603
## iter  70 value 394.477907
## iter  80 value 393.784061
## iter  90 value 393.726217
## final  value 393.726118 
## converged
## # weights:  51
## initial  value 517.601986 
## iter  10 value 442.971227
## iter  20 value 439.963412
## iter  30 value 412.164683
## iter  40 value 406.301323
## iter  50 value 399.871393
## iter  60 value 390.530467
## iter  70 value 379.167085
## iter  80 value 377.471698
## iter  90 value 376.667439
## iter 100 value 375.881324
## final  value 375.881324 
## stopped after 100 iterations
## # weights:  11
## initial  value 574.510563 
## final  value 446.858115 
## converged
## # weights:  31
## initial  value 521.579695 
## iter  10 value 437.440255
## iter  20 value 412.311742
## iter  30 value 403.612857
## iter  40 value 400.992339
## iter  50 value 397.404764
## iter  60 value 395.680363
## iter  70 value 395.311713
## iter  80 value 395.249012
## iter  90 value 395.217560
## final  value 395.217031 
## converged
## # weights:  51
## initial  value 460.970942 
## iter  10 value 435.565552
## iter  20 value 428.016327
## iter  30 value 413.224057
## iter  40 value 408.538772
## iter  50 value 404.035598
## iter  60 value 395.069801
## iter  70 value 390.899146
## iter  80 value 389.612943
## iter  90 value 389.492884
## iter 100 value 389.432736
## final  value 389.432736 
## stopped after 100 iterations
## # weights:  11
## initial  value 573.459127 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 562.043614 
## iter  10 value 422.040399
## iter  20 value 412.175022
## iter  30 value 409.331329
## iter  40 value 407.610617
## iter  50 value 406.396207
## iter  60 value 406.379489
## final  value 406.379472 
## converged
## # weights:  51
## initial  value 599.415134 
## iter  10 value 441.233896
## iter  20 value 419.062354
## iter  30 value 406.780740
## iter  40 value 399.786308
## iter  50 value 394.193878
## iter  60 value 390.723181
## iter  70 value 389.481212
## iter  80 value 389.474536
## final  value 389.474473 
## converged
## # weights:  11
## initial  value 628.820103 
## iter  10 value 446.951468
## iter  20 value 446.878787
## final  value 446.878778 
## converged
## # weights:  31
## initial  value 627.569241 
## iter  10 value 427.858508
## iter  20 value 408.760674
## iter  30 value 399.465074
## iter  40 value 395.961811
## iter  50 value 393.776756
## iter  60 value 390.449336
## iter  70 value 382.246387
## iter  80 value 375.435109
## iter  90 value 359.512909
## iter 100 value 328.579717
## final  value 328.579717 
## stopped after 100 iterations
## # weights:  51
## initial  value 503.099976 
## iter  10 value 411.359690
## iter  20 value 404.859410
## iter  30 value 400.587735
## iter  40 value 393.534764
## iter  50 value 384.282179
## iter  60 value 368.396374
## iter  70 value 338.023054
## iter  80 value 312.376747
## iter  90 value 304.198908
## iter 100 value 302.404460
## final  value 302.404460 
## stopped after 100 iterations
## # weights:  11
## initial  value 484.911087 
## final  value 446.858531 
## converged
## # weights:  31
## initial  value 580.045213 
## iter  10 value 434.357777
## iter  20 value 415.414485
## iter  30 value 411.998427
## iter  40 value 410.692307
## iter  50 value 409.796708
## iter  60 value 409.075959
## iter  70 value 409.032930
## iter  80 value 409.030804
## iter  90 value 409.029647
## iter 100 value 409.028020
## final  value 409.028020 
## stopped after 100 iterations
## # weights:  51
## initial  value 467.553820 
## iter  10 value 442.274440
## iter  20 value 437.850698
## iter  30 value 436.185381
## iter  40 value 433.976041
## iter  50 value 433.443633
## iter  60 value 432.218293
## iter  70 value 431.343197
## iter  80 value 431.340975
## iter  90 value 431.338288
## iter 100 value 431.336909
## final  value 431.336909 
## stopped after 100 iterations
## # weights:  11
## initial  value 466.715338 
## final  value 447.909142 
## converged
## # weights:  31
## initial  value 475.874861 
## iter  10 value 444.625012
## iter  20 value 440.280929
## iter  30 value 438.265583
## iter  40 value 436.025869
## iter  50 value 435.360734
## iter  60 value 433.330680
## iter  70 value 431.084045
## iter  80 value 429.879467
## iter  90 value 429.747182
## iter 100 value 429.623943
## final  value 429.623943 
## stopped after 100 iterations
## # weights:  51
## initial  value 618.964361 
## iter  10 value 436.433937
## iter  20 value 424.909625
## iter  30 value 420.866527
## iter  40 value 418.382272
## iter  50 value 415.736292
## iter  60 value 415.653663
## iter  70 value 413.461849
## iter  80 value 407.501043
## iter  90 value 406.455312
## iter 100 value 405.622613
## final  value 405.622613 
## stopped after 100 iterations
## # weights:  11
## initial  value 451.094048 
## iter  10 value 428.156354
## iter  20 value 416.470969
## iter  30 value 407.188620
## iter  40 value 398.098204
## iter  50 value 348.170564
## iter  60 value 333.184841
## iter  70 value 332.394212
## final  value 332.198881 
## converged
## # weights:  31
## initial  value 451.414322 
## iter  10 value 442.219230
## iter  20 value 426.477692
## iter  30 value 407.586832
## iter  40 value 402.590325
## iter  50 value 400.107791
## iter  60 value 395.124633
## iter  70 value 388.758356
## iter  80 value 343.792132
## iter  90 value 333.782685
## iter 100 value 331.590921
## final  value 331.590921 
## stopped after 100 iterations
## # weights:  51
## initial  value 444.894176 
## iter  10 value 425.367969
## iter  20 value 412.309724
## iter  30 value 406.361828
## iter  40 value 402.723077
## iter  50 value 397.406150
## iter  60 value 386.832364
## iter  70 value 383.534991
## iter  80 value 380.359955
## iter  90 value 374.391166
## iter 100 value 365.576474
## final  value 365.576474 
## stopped after 100 iterations
## # weights:  11
## initial  value 563.537255 
## final  value 447.910003 
## converged
## # weights:  31
## initial  value 478.993611 
## iter  10 value 440.365930
## iter  20 value 434.208869
## iter  30 value 418.127351
## iter  40 value 415.567926
## iter  50 value 413.774435
## iter  60 value 412.070590
## iter  70 value 411.148621
## iter  80 value 411.059204
## iter  90 value 411.042442
## iter 100 value 411.038740
## final  value 411.038740 
## stopped after 100 iterations
## # weights:  51
## initial  value 641.121767 
## iter  10 value 443.976979
## iter  20 value 422.231761
## iter  30 value 412.548307
## iter  40 value 402.609872
## iter  50 value 398.914578
## iter  60 value 393.589141
## iter  70 value 384.785050
## iter  80 value 384.389989
## iter  90 value 384.350036
## iter 100 value 384.297192
## final  value 384.297192 
## stopped after 100 iterations
## # weights:  11
## initial  value 480.977029 
## iter  10 value 422.158768
## iter  20 value 411.700831
## iter  30 value 409.893623
## iter  40 value 409.208196
## iter  50 value 408.658306
## iter  60 value 408.461882
## iter  70 value 408.449684
## iter  80 value 408.411639
## iter  90 value 408.410972
## final  value 408.410490 
## converged
## # weights:  31
## initial  value 471.643815 
## iter  10 value 419.246383
## iter  20 value 413.664633
## iter  30 value 406.611070
## iter  40 value 403.678381
## iter  50 value 403.635321
## iter  60 value 403.626169
## final  value 403.625853 
## converged
## # weights:  51
## initial  value 590.839477 
## iter  10 value 418.399131
## iter  20 value 408.510350
## iter  30 value 406.089106
## iter  40 value 404.824287
## iter  50 value 403.916131
## iter  60 value 400.265298
## iter  70 value 396.390769
## iter  80 value 396.241560
## final  value 396.240534 
## converged
## # weights:  11
## initial  value 491.898400 
## iter  10 value 446.741942
## iter  20 value 445.826471
## final  value 445.814964 
## converged
## # weights:  31
## initial  value 453.734002 
## iter  10 value 437.201686
## iter  20 value 421.541254
## iter  30 value 417.023127
## iter  40 value 410.345982
## iter  50 value 407.601877
## iter  60 value 406.679385
## iter  70 value 404.179806
## iter  80 value 391.382839
## iter  90 value 380.258011
## iter 100 value 355.698204
## final  value 355.698204 
## stopped after 100 iterations
## # weights:  51
## initial  value 464.835480 
## iter  10 value 424.909164
## iter  20 value 414.440831
## iter  30 value 406.282613
## iter  40 value 402.644779
## iter  50 value 398.427970
## iter  60 value 383.128857
## iter  70 value 360.279460
## iter  80 value 337.213390
## iter  90 value 329.036685
## iter 100 value 323.602435
## final  value 323.602435 
## stopped after 100 iterations
## # weights:  11
## initial  value 458.419062 
## iter  10 value 446.601035
## iter  20 value 443.441334
## iter  30 value 442.725666
## iter  40 value 442.260621
## iter  50 value 441.989520
## iter  60 value 441.824816
## iter  70 value 441.655783
## iter  80 value 441.648377
## iter  90 value 441.645542
## final  value 441.645479 
## converged
## # weights:  31
## initial  value 460.905022 
## iter  10 value 437.742374
## iter  20 value 432.808548
## iter  30 value 412.256028
## iter  40 value 398.125115
## iter  50 value 386.913269
## iter  60 value 383.247254
## iter  70 value 367.713080
## iter  80 value 322.420582
## iter  90 value 318.937740
## iter 100 value 317.902314
## final  value 317.902314 
## stopped after 100 iterations
## # weights:  51
## initial  value 467.660589 
## iter  10 value 424.732207
## iter  20 value 411.427191
## iter  30 value 406.807679
## iter  40 value 404.838230
## iter  50 value 404.319675
## iter  60 value 401.944785
## iter  70 value 400.053851
## iter  80 value 398.865895
## iter  90 value 398.266756
## iter 100 value 397.153081
## final  value 397.153081 
## stopped after 100 iterations
## # weights:  11
## initial  value 577.378613 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 452.518258 
## iter  10 value 431.318236
## iter  20 value 427.612105
## iter  30 value 426.204235
## iter  40 value 422.484227
## iter  50 value 422.348338
## final  value 422.348118 
## converged
## # weights:  51
## initial  value 535.664098 
## iter  10 value 444.243462
## iter  20 value 443.756305
## iter  30 value 443.390042
## iter  40 value 442.518831
## iter  50 value 442.306483
## final  value 442.306140 
## converged
## # weights:  11
## initial  value 486.822174 
## iter  10 value 446.733562
## iter  20 value 441.989445
## iter  30 value 427.132391
## iter  40 value 404.529964
## iter  50 value 395.429034
## iter  60 value 374.811852
## iter  70 value 335.081283
## iter  80 value 332.016353
## iter  90 value 330.869186
## final  value 330.869181 
## converged
## # weights:  31
## initial  value 599.433720 
## iter  10 value 447.584507
## iter  20 value 439.503502
## iter  30 value 436.296544
## iter  40 value 434.342483
## iter  50 value 434.342298
## iter  50 value 434.342298
## final  value 434.342298 
## converged
## # weights:  51
## initial  value 589.646506 
## iter  10 value 429.588136
## iter  20 value 420.325906
## iter  30 value 412.968766
## iter  40 value 405.560083
## iter  50 value 400.559442
## iter  60 value 394.583266
## iter  70 value 392.214778
## iter  80 value 386.997609
## iter  90 value 385.111631
## iter 100 value 376.804453
## final  value 376.804453 
## stopped after 100 iterations
## # weights:  11
## initial  value 460.802737 
## final  value 446.857272 
## converged
## # weights:  31
## initial  value 676.335086 
## iter  10 value 422.081752
## iter  20 value 410.034575
## iter  30 value 405.040400
## iter  40 value 402.155451
## iter  50 value 402.019489
## iter  60 value 402.012538
## iter  70 value 401.986179
## iter  80 value 401.985538
## iter  90 value 401.983176
## iter 100 value 401.979751
## final  value 401.979751 
## stopped after 100 iterations
## # weights:  51
## initial  value 1128.109346 
## iter  10 value 439.007265
## iter  20 value 419.401882
## iter  30 value 413.258485
## iter  40 value 408.782058
## iter  50 value 405.276633
## iter  60 value 402.799846
## iter  70 value 402.454540
## iter  80 value 402.419298
## iter  90 value 402.377765
## iter 100 value 401.892924
## final  value 401.892924 
## stopped after 100 iterations
## # weights:  11
## initial  value 446.935475 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 434.140888 
## iter  10 value 417.216631
## iter  20 value 409.956980
## iter  30 value 409.254094
## iter  40 value 408.478141
## iter  50 value 407.052731
## iter  60 value 406.929047
## iter  70 value 406.886876
## iter  80 value 406.855495
## final  value 406.855434 
## converged
## # weights:  51
## initial  value 602.689488 
## iter  10 value 419.588582
## iter  20 value 409.503167
## iter  30 value 403.707869
## iter  40 value 399.591862
## iter  50 value 396.514058
## iter  60 value 394.781865
## iter  70 value 394.706363
## final  value 394.705851 
## converged
## # weights:  11
## initial  value 547.841667 
## iter  10 value 441.205730
## iter  20 value 439.278433
## final  value 439.276420 
## converged
## # weights:  31
## initial  value 556.142418 
## iter  10 value 422.750331
## iter  20 value 409.828797
## iter  30 value 399.881550
## iter  40 value 395.900841
## iter  50 value 392.334954
## iter  60 value 382.142442
## iter  70 value 377.966241
## iter  80 value 372.805171
## iter  90 value 369.625732
## iter 100 value 359.860231
## final  value 359.860231 
## stopped after 100 iterations
## # weights:  51
## initial  value 495.824258 
## iter  10 value 441.408643
## iter  20 value 423.360694
## iter  30 value 414.116456
## iter  40 value 412.225079
## iter  50 value 405.246032
## iter  60 value 376.439055
## iter  70 value 325.562005
## iter  80 value 314.801554
## iter  90 value 311.426185
## iter 100 value 308.867863
## final  value 308.867863 
## stopped after 100 iterations
## # weights:  11
## initial  value 445.676552 
## iter  10 value 411.115078
## iter  20 value 401.400128
## iter  30 value 391.710119
## iter  40 value 368.711207
## iter  50 value 329.011353
## iter  60 value 321.740793
## iter  70 value 321.484384
## final  value 321.278080 
## converged
## # weights:  31
## initial  value 520.768003 
## iter  10 value 421.265364
## iter  20 value 406.850439
## iter  30 value 399.365753
## iter  40 value 397.261059
## iter  50 value 395.125755
## iter  60 value 387.925303
## iter  70 value 359.516378
## iter  80 value 346.891810
## iter  90 value 332.891899
## iter 100 value 323.235427
## final  value 323.235427 
## stopped after 100 iterations
## # weights:  51
## initial  value 493.010393 
## iter  10 value 415.532988
## iter  20 value 398.882486
## iter  30 value 395.973785
## iter  40 value 391.016035
## iter  50 value 385.795669
## iter  60 value 385.491131
## iter  70 value 385.317056
## iter  80 value 385.231805
## iter  90 value 385.196919
## iter 100 value 385.182324
## final  value 385.182324 
## stopped after 100 iterations
## # weights:  11
## initial  value 564.807554 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 476.185417 
## iter  10 value 412.433410
## iter  20 value 404.930123
## iter  30 value 376.253348
## iter  40 value 357.253756
## iter  50 value 349.381325
## iter  60 value 347.361900
## iter  70 value 342.378703
## iter  80 value 341.943964
## final  value 341.943351 
## converged
## # weights:  51
## initial  value 509.785840 
## iter  10 value 424.870080
## iter  20 value 413.039749
## iter  30 value 408.303668
## iter  40 value 404.609016
## iter  50 value 403.795122
## iter  60 value 403.699079
## iter  70 value 403.695538
## iter  80 value 403.692680
## iter  90 value 403.312871
## iter 100 value 403.262287
## final  value 403.262287 
## stopped after 100 iterations
## # weights:  11
## initial  value 446.882049 
## iter  10 value 424.995511
## iter  20 value 407.678846
## iter  30 value 403.833350
## iter  40 value 403.578361
## iter  40 value 403.578360
## iter  40 value 403.578357
## final  value 403.578357 
## converged
## # weights:  31
## initial  value 449.425858 
## iter  10 value 418.393600
## iter  20 value 416.810251
## iter  30 value 414.854074
## iter  40 value 408.598103
## iter  50 value 401.782214
## iter  60 value 397.489480
## iter  70 value 393.062545
## iter  80 value 389.499404
## iter  90 value 381.114157
## iter 100 value 377.326674
## final  value 377.326674 
## stopped after 100 iterations
## # weights:  51
## initial  value 469.933455 
## iter  10 value 439.689603
## iter  20 value 433.661086
## iter  30 value 418.862152
## iter  40 value 408.532863
## iter  50 value 402.148132
## iter  60 value 392.684538
## iter  70 value 385.776039
## iter  80 value 380.484468
## iter  90 value 375.482847
## iter 100 value 367.415260
## final  value 367.415260 
## stopped after 100 iterations
## # weights:  11
## initial  value 647.216779 
## final  value 446.857388 
## converged
## # weights:  31
## initial  value 445.815400 
## iter  10 value 439.364268
## iter  20 value 434.646836
## iter  30 value 432.625018
## iter  40 value 432.497451
## iter  50 value 432.382076
## iter  60 value 432.217708
## iter  70 value 432.204927
## iter  80 value 432.191835
## iter  90 value 432.010760
## iter 100 value 430.899317
## final  value 430.899317 
## stopped after 100 iterations
## # weights:  51
## initial  value 462.374574 
## iter  10 value 420.361284
## iter  20 value 411.253131
## iter  30 value 403.658619
## iter  40 value 398.120713
## iter  50 value 397.405879
## iter  60 value 396.493996
## iter  70 value 394.414129
## iter  80 value 394.320330
## iter  90 value 391.787695
## iter 100 value 351.595278
## final  value 351.595278 
## stopped after 100 iterations
## # weights:  11
## initial  value 449.134723 
## iter  10 value 431.862376
## iter  20 value 429.390531
## iter  30 value 429.017720
## iter  40 value 428.876044
## iter  50 value 428.849601
## final  value 428.849533 
## converged
## # weights:  31
## initial  value 588.754557 
## final  value 446.857060 
## converged
## # weights:  51
## initial  value 498.696566 
## iter  10 value 432.907258
## iter  20 value 428.763288
## iter  30 value 427.914253
## iter  40 value 427.910138
## final  value 427.910131 
## converged
## # weights:  11
## initial  value 448.167677 
## iter  10 value 437.407236
## iter  20 value 433.631897
## final  value 433.593718 
## converged
## # weights:  31
## initial  value 615.302890 
## iter  10 value 440.458180
## iter  20 value 436.298607
## iter  30 value 430.897582
## iter  40 value 417.870314
## iter  50 value 407.461300
## iter  60 value 399.104432
## iter  70 value 396.551350
## iter  80 value 395.199345
## iter  90 value 394.893502
## iter 100 value 394.098010
## final  value 394.098010 
## stopped after 100 iterations
## # weights:  51
## initial  value 456.289150 
## iter  10 value 433.406423
## iter  20 value 425.493744
## iter  30 value 420.083649
## iter  40 value 401.115955
## iter  50 value 390.725497
## iter  60 value 380.011160
## iter  70 value 374.719049
## iter  80 value 371.544131
## iter  90 value 370.525471
## iter 100 value 370.361162
## final  value 370.361162 
## stopped after 100 iterations
## # weights:  11
## initial  value 635.646402 
## iter  10 value 437.998303
## iter  20 value 433.521047
## iter  30 value 428.799503
## iter  40 value 426.811104
## iter  50 value 426.437028
## iter  60 value 426.433276
## iter  70 value 426.428989
## final  value 426.427596 
## converged
## # weights:  31
## initial  value 526.150599 
## iter  10 value 437.202210
## iter  20 value 432.819014
## iter  30 value 431.129597
## iter  40 value 430.794408
## iter  50 value 430.453486
## iter  60 value 430.358371
## iter  70 value 430.352096
## iter  80 value 430.298884
## iter  90 value 430.244518
## iter 100 value 430.240774
## final  value 430.240774 
## stopped after 100 iterations
## # weights:  51
## initial  value 454.294193 
## iter  10 value 434.517110
## iter  20 value 413.822121
## iter  30 value 402.596515
## iter  40 value 398.848942
## iter  50 value 397.261061
## iter  60 value 397.143457
## iter  70 value 397.068482
## iter  80 value 397.061520
## iter  90 value 397.058893
## iter 100 value 396.304866
## final  value 396.304866 
## stopped after 100 iterations
## # weights:  11
## initial  value 461.121062 
## final  value 447.909139 
## converged
## # weights:  31
## initial  value 456.299140 
## iter  10 value 432.033162
## iter  20 value 420.508779
## iter  30 value 402.281876
## iter  40 value 399.371852
## iter  50 value 389.296112
## iter  60 value 341.302316
## iter  70 value 325.486146
## iter  80 value 324.607917
## iter  90 value 324.577432
## iter 100 value 324.565841
## final  value 324.565841 
## stopped after 100 iterations
## # weights:  51
## initial  value 538.277493 
## iter  10 value 424.480466
## iter  20 value 420.504335
## iter  30 value 418.045963
## iter  40 value 414.347871
## iter  50 value 413.929046
## final  value 413.928070 
## converged
## # weights:  11
## initial  value 457.071252 
## iter  10 value 440.372320
## iter  20 value 434.615589
## iter  30 value 407.431388
## iter  40 value 393.836491
## iter  50 value 380.677542
## iter  60 value 336.350914
## iter  70 value 332.899716
## iter  80 value 332.434386
## iter  80 value 332.434386
## iter  80 value 332.434386
## final  value 332.434386 
## converged
## # weights:  31
## initial  value 505.881688 
## iter  10 value 444.727309
## iter  20 value 433.318124
## iter  30 value 427.925773
## iter  40 value 425.951028
## iter  50 value 424.912831
## iter  60 value 423.041916
## iter  70 value 410.143905
## iter  80 value 402.637775
## iter  90 value 398.544203
## iter 100 value 395.554427
## final  value 395.554427 
## stopped after 100 iterations
## # weights:  51
## initial  value 460.552216 
## iter  10 value 440.553654
## iter  20 value 415.864433
## iter  30 value 409.819683
## iter  40 value 407.345656
## iter  50 value 401.230720
## iter  60 value 399.030123
## iter  70 value 396.226967
## iter  80 value 393.616381
## iter  90 value 386.817756
## iter 100 value 381.486720
## final  value 381.486720 
## stopped after 100 iterations
## # weights:  11
## initial  value 552.839437 
## final  value 447.912460 
## converged
## # weights:  31
## initial  value 525.984989 
## iter  10 value 447.906713
## iter  20 value 447.770171
## iter  30 value 444.944533
## iter  40 value 442.730420
## iter  50 value 441.219689
## iter  60 value 440.244919
## iter  70 value 440.218397
## iter  80 value 440.212735
## iter  90 value 440.146670
## iter 100 value 440.011450
## final  value 440.011450 
## stopped after 100 iterations
## # weights:  51
## initial  value 455.287782 
## iter  10 value 430.578157
## iter  20 value 409.307625
## iter  30 value 380.885706
## iter  40 value 340.506126
## iter  50 value 332.614107
## iter  60 value 332.395192
## iter  70 value 331.618810
## iter  80 value 328.617279
## iter  90 value 323.366714
## iter 100 value 318.104271
## final  value 318.104271 
## stopped after 100 iterations
## # weights:  11
## initial  value 489.960666 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 553.910903 
## iter  10 value 427.773129
## iter  20 value 413.715243
## iter  30 value 405.659487
## iter  40 value 397.137770
## iter  50 value 390.446917
## iter  60 value 383.319967
## iter  70 value 379.682795
## iter  80 value 360.266931
## iter  90 value 324.596439
## iter 100 value 318.226521
## final  value 318.226521 
## stopped after 100 iterations
## # weights:  51
## initial  value 546.371646 
## iter  10 value 437.483436
## iter  20 value 427.224728
## iter  30 value 419.966214
## iter  40 value 416.039244
## iter  50 value 414.677370
## iter  60 value 412.034555
## iter  70 value 409.602025
## iter  80 value 409.588600
## final  value 409.588580 
## converged
## # weights:  11
## initial  value 514.990097 
## iter  10 value 446.757920
## iter  20 value 442.561765
## iter  30 value 434.193877
## iter  40 value 421.581274
## iter  50 value 407.765903
## iter  60 value 398.981998
## iter  70 value 378.372570
## iter  80 value 336.242478
## iter  90 value 334.279033
## iter 100 value 334.276434
## final  value 334.276434 
## stopped after 100 iterations
## # weights:  31
## initial  value 454.806271 
## iter  10 value 444.379615
## iter  20 value 414.345997
## iter  30 value 401.457757
## iter  40 value 390.121688
## iter  50 value 349.916301
## iter  60 value 332.814104
## iter  70 value 329.835615
## iter  80 value 327.366844
## iter  90 value 326.520265
## iter 100 value 326.397399
## final  value 326.397399 
## stopped after 100 iterations
## # weights:  51
## initial  value 507.126408 
## iter  10 value 433.238977
## iter  20 value 424.662740
## iter  30 value 419.718494
## iter  40 value 417.309994
## iter  50 value 399.789324
## iter  60 value 369.791384
## iter  70 value 358.909154
## iter  80 value 330.797105
## iter  90 value 327.282684
## iter 100 value 325.210773
## final  value 325.210773 
## stopped after 100 iterations
## # weights:  11
## initial  value 527.491263 
## final  value 446.857427 
## converged
## # weights:  31
## initial  value 447.973661 
## iter  10 value 417.386725
## iter  20 value 397.775358
## iter  30 value 371.328055
## iter  40 value 343.327511
## iter  50 value 338.428305
## iter  60 value 336.642849
## iter  70 value 332.558265
## iter  80 value 326.647081
## iter  90 value 325.493327
## iter 100 value 325.489592
## final  value 325.489592 
## stopped after 100 iterations
## # weights:  51
## initial  value 452.358955 
## iter  10 value 411.695971
## iter  20 value 402.276221
## iter  30 value 400.552309
## iter  40 value 398.453088
## iter  50 value 393.245498
## iter  60 value 392.288695
## iter  70 value 391.417210
## iter  80 value 379.793311
## iter  90 value 368.384773
## iter 100 value 364.775931
## final  value 364.775931 
## stopped after 100 iterations
## # weights:  11
## initial  value 456.209236 
## iter  10 value 443.021271
## iter  20 value 437.490142
## iter  30 value 432.877753
## iter  40 value 432.007813
## iter  50 value 428.256693
## iter  60 value 424.323969
## iter  70 value 414.060254
## iter  80 value 406.954386
## iter  90 value 403.602743
## iter 100 value 403.386588
## final  value 403.386588 
## stopped after 100 iterations
## # weights:  31
## initial  value 486.338409 
## iter  10 value 439.533469
## iter  20 value 430.219693
## iter  30 value 427.451944
## iter  40 value 425.150778
## iter  50 value 424.376376
## iter  60 value 423.468036
## iter  70 value 421.439934
## iter  80 value 421.087006
## iter  90 value 421.001957
## iter 100 value 420.993581
## final  value 420.993581 
## stopped after 100 iterations
## # weights:  51
## initial  value 543.767135 
## iter  10 value 434.695108
## iter  20 value 427.222854
## iter  30 value 418.701552
## iter  40 value 411.934396
## iter  50 value 403.244647
## iter  60 value 399.896960
## iter  70 value 398.884436
## iter  80 value 398.682980
## iter  90 value 398.283540
## iter 100 value 398.050536
## final  value 398.050536 
## stopped after 100 iterations
## # weights:  11
## initial  value 445.620060 
## iter  10 value 440.539898
## iter  20 value 439.621541
## final  value 439.619191 
## converged
## # weights:  31
## initial  value 486.576370 
## iter  10 value 437.353567
## iter  20 value 428.241429
## iter  30 value 425.806029
## iter  40 value 425.618887
## iter  50 value 425.601008
## iter  60 value 425.565412
## iter  70 value 425.561212
## iter  80 value 423.469466
## iter  90 value 411.118013
## iter 100 value 402.888850
## final  value 402.888850 
## stopped after 100 iterations
## # weights:  51
## initial  value 461.143479 
## iter  10 value 430.868810
## iter  20 value 420.017951
## iter  30 value 407.071822
## iter  40 value 401.674029
## iter  50 value 395.702821
## iter  60 value 386.430152
## iter  70 value 373.086043
## iter  80 value 364.638418
## iter  90 value 352.761469
## iter 100 value 345.570275
## final  value 345.570275 
## stopped after 100 iterations
## # weights:  11
## initial  value 446.908544 
## final  value 446.857365 
## converged
## # weights:  31
## initial  value 462.730993 
## iter  10 value 446.187014
## iter  20 value 435.433213
## iter  30 value 420.841472
## iter  40 value 413.354382
## iter  50 value 411.198418
## iter  60 value 410.897011
## iter  70 value 410.321368
## iter  80 value 409.858476
## iter  90 value 409.292027
## iter 100 value 409.273661
## final  value 409.273661 
## stopped after 100 iterations
## # weights:  51
## initial  value 449.961050 
## iter  10 value 431.813390
## iter  20 value 414.487333
## iter  30 value 406.908323
## iter  40 value 403.981187
## iter  50 value 400.872070
## iter  60 value 398.032174
## iter  70 value 390.495166
## iter  80 value 388.984924
## iter  90 value 387.962914
## iter 100 value 386.717743
## final  value 386.717743 
## stopped after 100 iterations
## # weights:  11
## initial  value 477.564533 
## iter  10 value 422.220104
## iter  20 value 413.233349
## iter  30 value 410.833133
## iter  40 value 409.888699
## iter  50 value 409.206653
## iter  60 value 396.054622
## iter  70 value 378.246075
## iter  80 value 350.206814
## iter  90 value 342.480698
## iter 100 value 342.322511
## final  value 342.322511 
## stopped after 100 iterations
## # weights:  31
## initial  value 568.478439 
## iter  10 value 434.968124
## iter  20 value 421.396046
## iter  30 value 417.013054
## iter  40 value 415.836157
## iter  50 value 415.009031
## iter  60 value 414.408188
## iter  70 value 414.050181
## iter  80 value 414.046683
## iter  80 value 414.046680
## iter  80 value 414.046680
## final  value 414.046680 
## converged
## # weights:  51
## initial  value 455.345919 
## iter  10 value 432.938911
## iter  20 value 415.016338
## iter  30 value 404.421711
## iter  40 value 400.193604
## iter  50 value 394.655432
## iter  60 value 388.246708
## iter  70 value 385.224408
## iter  80 value 384.397633
## iter  90 value 384.392126
## iter  90 value 384.392123
## iter  90 value 384.392123
## final  value 384.392123 
## converged
## # weights:  11
## initial  value 499.035460 
## iter  10 value 446.878799
## final  value 446.878791 
## converged
## # weights:  31
## initial  value 447.494397 
## iter  10 value 428.836419
## iter  20 value 419.681901
## iter  30 value 416.233531
## iter  40 value 413.170960
## iter  50 value 402.772720
## iter  60 value 392.924812
## iter  70 value 381.319947
## iter  80 value 350.461964
## iter  90 value 339.292856
## iter 100 value 324.115391
## final  value 324.115391 
## stopped after 100 iterations
## # weights:  51
## initial  value 450.304258 
## iter  10 value 425.197305
## iter  20 value 409.754161
## iter  30 value 403.474012
## iter  40 value 399.134387
## iter  50 value 394.675617
## iter  60 value 389.870707
## iter  70 value 384.274732
## iter  80 value 378.826063
## iter  90 value 375.760494
## iter 100 value 372.107635
## final  value 372.107635 
## stopped after 100 iterations
## # weights:  11
## initial  value 459.102093 
## final  value 446.857374 
## converged
## # weights:  31
## initial  value 475.248953 
## iter  10 value 444.199220
## iter  20 value 402.479471
## iter  30 value 396.740859
## iter  40 value 391.374141
## iter  50 value 388.934559
## iter  60 value 388.871138
## iter  70 value 388.862847
## iter  80 value 388.855867
## iter  90 value 388.817980
## iter 100 value 388.813221
## final  value 388.813221 
## stopped after 100 iterations
## # weights:  51
## initial  value 439.904213 
## iter  10 value 422.370429
## iter  20 value 417.993745
## iter  30 value 415.354166
## iter  40 value 415.177741
## iter  50 value 415.131812
## iter  60 value 415.121128
## final  value 415.120511 
## converged
## # weights:  11
## initial  value 454.001973 
## iter  10 value 435.215934
## iter  20 value 414.878044
## iter  30 value 404.202174
## iter  40 value 386.390966
## iter  50 value 338.196924
## iter  60 value 327.143398
## iter  70 value 324.156589
## iter  80 value 322.394431
## iter  90 value 322.353439
## final  value 322.326416 
## converged
## # weights:  31
## initial  value 481.496343 
## iter  10 value 438.239253
## iter  20 value 417.587424
## iter  30 value 406.099842
## iter  40 value 398.541862
## iter  50 value 395.993818
## iter  60 value 395.780127
## final  value 395.779559 
## converged
## # weights:  51
## initial  value 528.698625 
## iter  10 value 436.825011
## iter  20 value 427.877535
## iter  30 value 404.346203
## iter  40 value 394.548274
## iter  50 value 390.589152
## iter  60 value 388.094114
## iter  70 value 387.135054
## iter  80 value 386.554688
## iter  90 value 386.475814
## final  value 386.475689 
## converged
## # weights:  11
## initial  value 457.874091 
## iter  10 value 427.450246
## iter  20 value 413.795650
## iter  30 value 407.926119
## iter  40 value 404.843392
## iter  50 value 404.396915
## final  value 404.396665 
## converged
## # weights:  31
## initial  value 573.997701 
## iter  10 value 445.501282
## iter  20 value 421.036846
## iter  30 value 403.161105
## iter  40 value 395.182678
## iter  50 value 392.555403
## iter  60 value 386.712153
## iter  70 value 383.053923
## iter  80 value 377.850907
## iter  90 value 346.022278
## iter 100 value 327.588493
## final  value 327.588493 
## stopped after 100 iterations
## # weights:  51
## initial  value 466.729650 
## iter  10 value 425.501082
## iter  20 value 406.590343
## iter  30 value 401.299329
## iter  40 value 397.187789
## iter  50 value 395.817748
## iter  60 value 394.628952
## iter  70 value 392.839763
## iter  80 value 391.995960
## iter  90 value 391.107315
## iter 100 value 390.638459
## final  value 390.638459 
## stopped after 100 iterations
## # weights:  11
## initial  value 543.737899 
## iter  10 value 447.962752
## iter  20 value 444.057941
## iter  30 value 439.095700
## iter  40 value 437.954811
## iter  50 value 437.862117
## iter  60 value 437.857691
## final  value 437.856981 
## converged
## # weights:  31
## initial  value 608.872532 
## iter  10 value 415.268149
## iter  20 value 407.651745
## iter  30 value 406.143680
## iter  40 value 405.937460
## iter  50 value 405.567536
## iter  60 value 405.251293
## iter  70 value 404.674855
## iter  80 value 404.614900
## iter  90 value 404.502993
## iter 100 value 404.268035
## final  value 404.268035 
## stopped after 100 iterations
## # weights:  51
## initial  value 453.476155 
## iter  10 value 422.780021
## iter  20 value 400.492700
## iter  30 value 395.756509
## iter  40 value 391.339081
## iter  50 value 388.608145
## iter  60 value 385.302579
## iter  70 value 384.729260
## iter  80 value 384.695060
## iter  90 value 384.661706
## iter 100 value 384.559362
## final  value 384.559362 
## stopped after 100 iterations
## # weights:  11
## initial  value 476.585804 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 454.826080 
## iter  10 value 420.787641
## iter  20 value 416.658503
## iter  30 value 414.713045
## iter  40 value 413.869125
## iter  50 value 413.864435
## final  value 413.864422 
## converged
## # weights:  51
## initial  value 678.294219 
## iter  10 value 442.727977
## iter  20 value 418.599343
## iter  30 value 409.108523
## iter  40 value 403.751315
## iter  50 value 403.236725
## iter  60 value 402.612372
## iter  70 value 402.549307
## iter  80 value 402.241762
## iter  90 value 402.202243
## iter 100 value 402.193590
## final  value 402.193590 
## stopped after 100 iterations
## # weights:  11
## initial  value 467.286492 
## iter  10 value 446.881557
## final  value 446.878833 
## converged
## # weights:  31
## initial  value 464.262648 
## iter  10 value 436.223022
## iter  20 value 434.446471
## iter  30 value 428.795401
## iter  40 value 415.295783
## iter  50 value 409.948132
## iter  60 value 404.545421
## iter  70 value 399.942890
## iter  80 value 393.155392
## iter  90 value 388.295170
## iter 100 value 386.229701
## final  value 386.229701 
## stopped after 100 iterations
## # weights:  51
## initial  value 702.289551 
## iter  10 value 438.332221
## iter  20 value 423.690886
## iter  30 value 408.244299
## iter  40 value 393.372658
## iter  50 value 384.072820
## iter  60 value 348.608982
## iter  70 value 318.397230
## iter  80 value 312.754607
## iter  90 value 311.297388
## iter 100 value 309.790371
## final  value 309.790371 
## stopped after 100 iterations
## # weights:  11
## initial  value 562.266369 
## final  value 446.857291 
## converged
## # weights:  31
## initial  value 490.948957 
## iter  10 value 446.464027
## iter  20 value 444.451956
## iter  30 value 442.461973
## iter  40 value 439.083171
## iter  50 value 437.813643
## iter  60 value 437.054313
## iter  70 value 430.384779
## iter  80 value 398.797638
## iter  90 value 395.170619
## iter 100 value 390.234709
## final  value 390.234709 
## stopped after 100 iterations
## # weights:  51
## initial  value 465.090636 
## iter  10 value 442.827393
## iter  20 value 435.809124
## iter  30 value 433.094306
## iter  40 value 429.681159
## iter  50 value 425.903123
## iter  60 value 425.117011
## iter  70 value 424.110733
## iter  80 value 422.210979
## iter  90 value 421.812351
## iter 100 value 421.669531
## final  value 421.669531 
## stopped after 100 iterations
## # weights:  11
## initial  value 452.552357 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 478.657695 
## iter  10 value 420.769903
## iter  20 value 400.083878
## iter  30 value 395.475051
## iter  40 value 392.955818
## iter  50 value 388.167647
## iter  60 value 387.723334
## final  value 387.720810 
## converged
## # weights:  51
## initial  value 450.917360 
## iter  10 value 433.832703
## iter  20 value 427.983661
## iter  30 value 424.609560
## iter  40 value 423.371479
## iter  50 value 422.384319
## iter  60 value 418.197114
## iter  70 value 415.515471
## iter  80 value 411.698403
## iter  90 value 410.535368
## iter 100 value 408.819631
## final  value 408.819631 
## stopped after 100 iterations
## # weights:  11
## initial  value 452.636095 
## iter  10 value 438.005324
## iter  20 value 417.105688
## iter  30 value 408.604363
## iter  40 value 394.272432
## iter  50 value 374.943210
## iter  60 value 333.107829
## iter  70 value 331.751832
## final  value 331.482149 
## converged
## # weights:  31
## initial  value 465.977865 
## iter  10 value 439.582253
## iter  20 value 422.167819
## iter  30 value 412.038368
## iter  40 value 402.196694
## iter  50 value 395.158049
## iter  60 value 392.987193
## iter  70 value 363.185338
## iter  80 value 337.614268
## iter  90 value 329.864700
## iter 100 value 328.193624
## final  value 328.193624 
## stopped after 100 iterations
## # weights:  51
## initial  value 450.847468 
## iter  10 value 437.470938
## iter  20 value 405.766112
## iter  30 value 397.447081
## iter  40 value 395.372675
## iter  50 value 394.940452
## iter  60 value 392.839184
## iter  70 value 389.622424
## iter  80 value 388.698056
## iter  90 value 382.876808
## iter 100 value 374.045692
## final  value 374.045692 
## stopped after 100 iterations
## # weights:  11
## initial  value 487.265955 
## iter  10 value 422.962941
## iter  20 value 407.581332
## iter  30 value 404.260895
## iter  40 value 403.157148
## iter  50 value 403.151120
## iter  60 value 403.142613
## final  value 403.142539 
## converged
## # weights:  31
## initial  value 465.394310 
## iter  10 value 436.500560
## iter  20 value 431.857781
## iter  30 value 428.249183
## iter  40 value 425.290470
## iter  50 value 422.956365
## iter  60 value 420.118862
## iter  70 value 415.352161
## iter  80 value 415.174873
## iter  90 value 415.099669
## iter 100 value 414.743994
## final  value 414.743994 
## stopped after 100 iterations
## # weights:  51
## initial  value 496.847822 
## iter  10 value 435.016819
## iter  20 value 408.833239
## iter  30 value 401.837636
## iter  40 value 395.682089
## iter  50 value 395.024185
## iter  60 value 395.001111
## iter  70 value 394.979899
## iter  80 value 394.916009
## iter  90 value 394.636086
## iter 100 value 394.565549
## final  value 394.565549 
## stopped after 100 iterations
## # weights:  11
## initial  value 464.688032 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 456.154042 
## iter  10 value 445.223229
## final  value 445.187095 
## converged
## # weights:  51
## initial  value 440.047189 
## iter  10 value 417.790854
## iter  20 value 403.783173
## iter  30 value 391.933791
## iter  40 value 390.266271
## iter  50 value 389.761695
## iter  60 value 388.343123
## iter  70 value 387.304659
## iter  80 value 386.492129
## iter  90 value 386.386183
## iter 100 value 385.923804
## final  value 385.923804 
## stopped after 100 iterations
## # weights:  11
## initial  value 606.391007 
## iter  10 value 447.389173
## iter  20 value 446.878869
## final  value 446.878838 
## converged
## # weights:  31
## initial  value 516.593165 
## iter  10 value 437.038390
## iter  20 value 416.562842
## iter  30 value 403.743680
## iter  40 value 387.435176
## iter  50 value 344.505984
## iter  60 value 328.980478
## iter  70 value 326.257450
## iter  80 value 323.934960
## iter  90 value 322.938464
## iter 100 value 320.622679
## final  value 320.622679 
## stopped after 100 iterations
## # weights:  51
## initial  value 454.006140 
## iter  10 value 416.576239
## iter  20 value 402.830097
## iter  30 value 393.258849
## iter  40 value 389.847486
## iter  50 value 386.710987
## iter  60 value 383.547971
## iter  70 value 383.287889
## iter  80 value 383.268783
## iter  90 value 383.262485
## iter 100 value 383.183043
## final  value 383.183043 
## stopped after 100 iterations
## # weights:  11
## initial  value 484.208447 
## final  value 446.857573 
## converged
## # weights:  31
## initial  value 444.535324 
## iter  10 value 435.806020
## iter  20 value 435.144289
## iter  30 value 433.124155
## iter  40 value 432.562273
## iter  50 value 432.533178
## iter  60 value 432.517172
## iter  70 value 431.118366
## iter  80 value 430.750022
## iter  90 value 430.042147
## iter 100 value 430.036024
## final  value 430.036024 
## stopped after 100 iterations
## # weights:  51
## initial  value 462.972259 
## iter  10 value 444.262829
## iter  20 value 429.778941
## iter  30 value 424.418539
## iter  40 value 421.807859
## iter  50 value 419.694458
## iter  60 value 415.291456
## iter  70 value 412.542552
## iter  80 value 411.586407
## iter  90 value 411.440140
## iter 100 value 410.919483
## final  value 410.919483 
## stopped after 100 iterations
## # weights:  11
## initial  value 456.048011 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 569.448624 
## iter  10 value 417.145096
## iter  20 value 412.965376
## iter  30 value 408.629019
## iter  40 value 408.186694
## iter  50 value 408.157705
## iter  60 value 408.155384
## iter  70 value 408.154844
## iter  80 value 406.180445
## iter  90 value 405.621870
## iter 100 value 405.150588
## final  value 405.150588 
## stopped after 100 iterations
## # weights:  51
## initial  value 474.002013 
## iter  10 value 434.370773
## iter  20 value 431.256838
## iter  30 value 429.014622
## iter  40 value 428.131674
## iter  50 value 427.718457
## iter  60 value 427.668682
## iter  70 value 427.554374
## iter  80 value 427.221019
## iter  90 value 426.911298
## iter 100 value 426.113541
## final  value 426.113541 
## stopped after 100 iterations
## # weights:  11
## initial  value 485.935997 
## iter  10 value 443.989412
## iter  20 value 441.963057
## iter  30 value 440.338875
## iter  40 value 438.291399
## iter  50 value 438.068452
## iter  50 value 438.068450
## iter  50 value 438.068450
## final  value 438.068450 
## converged
## # weights:  31
## initial  value 538.693546 
## iter  10 value 446.627045
## iter  20 value 435.497348
## iter  30 value 431.211185
## iter  40 value 430.587129
## iter  50 value 429.757381
## iter  60 value 425.690021
## iter  70 value 422.700883
## iter  80 value 407.405296
## iter  90 value 404.478244
## iter 100 value 398.068451
## final  value 398.068451 
## stopped after 100 iterations
## # weights:  51
## initial  value 564.126672 
## iter  10 value 440.015939
## iter  20 value 405.365786
## iter  30 value 387.896634
## iter  40 value 378.971796
## iter  50 value 369.805080
## iter  60 value 343.773686
## iter  70 value 320.125625
## iter  80 value 305.738138
## iter  90 value 302.688825
## iter 100 value 301.497193
## final  value 301.497193 
## stopped after 100 iterations
## # weights:  11
## initial  value 495.701664 
## final  value 446.857488 
## converged
## # weights:  31
## initial  value 532.140028 
## iter  10 value 424.507691
## iter  20 value 412.845049
## iter  30 value 404.910643
## iter  40 value 400.371159
## iter  50 value 396.415167
## iter  60 value 394.852574
## iter  70 value 394.470643
## iter  80 value 394.332870
## iter  90 value 394.249695
## iter 100 value 394.229520
## final  value 394.229520 
## stopped after 100 iterations
## # weights:  51
## initial  value 448.264892 
## iter  10 value 421.819460
## iter  20 value 411.713082
## iter  30 value 401.684783
## iter  40 value 391.848673
## iter  50 value 390.620440
## iter  60 value 387.721876
## iter  70 value 370.797968
## iter  80 value 348.709407
## iter  90 value 324.603791
## iter 100 value 312.439402
## final  value 312.439402 
## stopped after 100 iterations
## # weights:  11
## initial  value 459.774512 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 541.474120 
## final  value 446.857145 
## converged
## # weights:  51
## initial  value 477.724411 
## iter  10 value 441.149485
## iter  20 value 436.286332
## iter  30 value 432.769200
## iter  40 value 429.447487
## iter  50 value 427.944807
## iter  60 value 426.031184
## iter  70 value 424.310482
## iter  80 value 424.276779
## final  value 424.276659 
## converged
## # weights:  11
## initial  value 498.161014 
## iter  10 value 447.052721
## iter  20 value 446.868841
## iter  30 value 443.034077
## iter  40 value 425.495237
## iter  50 value 423.978115
## iter  60 value 415.703746
## iter  70 value 403.082553
## iter  80 value 391.279447
## iter  90 value 381.952827
## iter 100 value 333.469356
## final  value 333.469356 
## stopped after 100 iterations
## # weights:  31
## initial  value 539.231081 
## iter  10 value 437.313557
## iter  20 value 426.218396
## iter  30 value 397.909834
## iter  40 value 393.521978
## iter  50 value 392.574144
## iter  60 value 389.507300
## iter  70 value 345.887224
## iter  80 value 332.649076
## iter  90 value 326.613663
## iter 100 value 324.542229
## final  value 324.542229 
## stopped after 100 iterations
## # weights:  51
## initial  value 511.104946 
## iter  10 value 422.325352
## iter  20 value 414.689376
## iter  30 value 411.112942
## iter  40 value 407.988673
## iter  50 value 406.025467
## iter  60 value 402.553710
## iter  70 value 398.934556
## iter  80 value 381.780125
## iter  90 value 341.454855
## iter 100 value 330.793378
## final  value 330.793378 
## stopped after 100 iterations
## # weights:  11
## initial  value 658.358101 
## final  value 446.858469 
## converged
## # weights:  31
## initial  value 591.475497 
## final  value 446.858968 
## converged
## # weights:  51
## initial  value 459.271369 
## iter  10 value 440.347235
## iter  20 value 437.701104
## iter  30 value 434.688333
## iter  40 value 434.188170
## iter  50 value 434.046179
## iter  60 value 433.748635
## iter  70 value 433.547757
## iter  80 value 433.281058
## iter  90 value 433.032289
## iter 100 value 432.977753
## final  value 432.977753 
## stopped after 100 iterations
## # weights:  11
## initial  value 554.722872 
## iter  10 value 440.277230
## iter  20 value 436.645780
## iter  30 value 401.113768
## iter  40 value 397.987989
## iter  50 value 397.033569
## iter  60 value 395.621067
## iter  70 value 395.518415
## iter  80 value 395.367664
## iter  90 value 395.359969
## iter 100 value 395.314813
## final  value 395.314813 
## stopped after 100 iterations
## # weights:  31
## initial  value 496.012530 
## final  value 446.857145 
## converged
## # weights:  51
## initial  value 673.033702 
## iter  10 value 425.747675
## iter  20 value 412.674634
## iter  30 value 407.521331
## iter  40 value 403.961267
## iter  50 value 402.943203
## iter  60 value 401.439247
## iter  70 value 401.301053
## iter  80 value 401.298206
## final  value 401.298163 
## converged
## # weights:  11
## initial  value 469.775956 
## iter  10 value 440.539094
## iter  20 value 439.697681
## iter  30 value 439.324320
## iter  40 value 438.963140
## iter  50 value 438.959474
## final  value 438.959193 
## converged
## # weights:  31
## initial  value 447.385333 
## iter  10 value 441.014097
## iter  20 value 431.550136
## iter  30 value 411.971332
## iter  40 value 402.362157
## iter  50 value 394.881706
## iter  60 value 390.139954
## iter  70 value 388.759127
## iter  80 value 388.557606
## iter  90 value 388.545150
## final  value 388.545126 
## converged
## # weights:  51
## initial  value 465.038649 
## iter  10 value 423.415906
## iter  20 value 409.255400
## iter  30 value 404.365760
## iter  40 value 400.582426
## iter  50 value 388.562783
## iter  60 value 348.913317
## iter  70 value 329.752398
## iter  80 value 323.922120
## iter  90 value 322.513936
## iter 100 value 321.174934
## final  value 321.174934 
## stopped after 100 iterations
## # weights:  11
## initial  value 624.373844 
## final  value 446.858026 
## converged
## # weights:  31
## initial  value 535.326939 
## iter  10 value 429.302122
## iter  20 value 410.434806
## iter  30 value 398.636549
## iter  40 value 359.014970
## iter  50 value 335.578869
## iter  60 value 326.908527
## iter  70 value 325.347763
## iter  80 value 325.309123
## final  value 325.309085 
## converged
## # weights:  51
## initial  value 442.903145 
## iter  10 value 432.436052
## iter  20 value 416.040213
## iter  30 value 404.442381
## iter  40 value 401.766597
## iter  50 value 400.656876
## iter  60 value 398.752306
## iter  70 value 398.378101
## iter  80 value 398.347556
## iter  90 value 398.190127
## iter 100 value 398.036393
## final  value 398.036393 
## stopped after 100 iterations
## # weights:  11
## initial  value 459.112139 
## final  value 447.881630 
## converged
## # weights:  31
## initial  value 449.872601 
## iter  10 value 415.397249
## iter  20 value 402.248836
## iter  30 value 396.558350
## iter  40 value 393.076709
## iter  50 value 388.391024
## iter  60 value 385.865132
## iter  70 value 385.442540
## iter  80 value 385.414093
## iter  90 value 385.389347
## iter 100 value 385.347873
## final  value 385.347873 
## stopped after 100 iterations
## # weights:  51
## initial  value 1134.992217 
## iter  10 value 442.007313
## iter  20 value 420.561882
## iter  30 value 399.604544
## iter  40 value 357.465722
## iter  50 value 337.141793
## iter  60 value 331.855382
## iter  70 value 321.832228
## iter  80 value 315.505043
## iter  90 value 314.781943
## iter 100 value 314.377739
## final  value 314.377739 
## stopped after 100 iterations
## # weights:  11
## initial  value 475.491955 
## iter  10 value 446.569596
## iter  20 value 436.304723
## iter  30 value 434.569168
## iter  40 value 433.901855
## final  value 433.823217 
## converged
## # weights:  31
## initial  value 555.249569 
## iter  10 value 444.097792
## iter  20 value 438.426083
## iter  30 value 434.550408
## iter  40 value 422.173462
## iter  50 value 415.027984
## iter  60 value 407.612235
## iter  70 value 402.878635
## iter  80 value 386.034851
## iter  90 value 341.654512
## iter 100 value 330.427787
## final  value 330.427787 
## stopped after 100 iterations
## # weights:  51
## initial  value 643.193026 
## iter  10 value 443.575942
## iter  20 value 434.767336
## iter  30 value 433.586693
## iter  40 value 431.953388
## iter  50 value 425.116927
## iter  60 value 399.699384
## iter  70 value 395.796295
## iter  80 value 393.977090
## iter  90 value 386.495732
## iter 100 value 378.717112
## final  value 378.717112 
## stopped after 100 iterations
## # weights:  11
## initial  value 540.235612 
## final  value 447.909585 
## converged
## # weights:  31
## initial  value 550.692098 
## iter  10 value 443.166609
## iter  20 value 421.340234
## iter  30 value 416.638191
## iter  40 value 406.838168
## iter  50 value 402.781649
## iter  60 value 401.411389
## iter  70 value 400.175004
## iter  80 value 399.246279
## iter  90 value 398.965430
## iter 100 value 398.854116
## final  value 398.854116 
## stopped after 100 iterations
## # weights:  51
## initial  value 641.882022 
## iter  10 value 445.052280
## iter  20 value 426.088445
## iter  30 value 420.298855
## iter  40 value 416.673170
## iter  50 value 408.900289
## iter  60 value 398.777614
## iter  70 value 390.749681
## iter  80 value 379.686043
## iter  90 value 363.768080
## iter 100 value 329.111540
## final  value 329.111540 
## stopped after 100 iterations
## # weights:  11
## initial  value 446.177754 
## iter  10 value 444.377753
## iter  20 value 444.354596
## iter  30 value 443.954092
## iter  40 value 442.627054
## iter  50 value 442.310084
## iter  60 value 442.304845
## iter  70 value 442.303736
## final  value 442.303724 
## converged
## # weights:  31
## initial  value 494.552656 
## iter  10 value 446.828719
## final  value 446.828624 
## converged
## # weights:  51
## initial  value 555.841066 
## iter  10 value 436.696639
## iter  20 value 428.164958
## iter  30 value 411.000627
## iter  40 value 396.395187
## iter  50 value 390.638263
## iter  60 value 384.969145
## iter  70 value 370.619867
## iter  80 value 348.941147
## iter  90 value 313.551261
## iter 100 value 306.646970
## final  value 306.646970 
## stopped after 100 iterations
## # weights:  11
## initial  value 535.076216 
## iter  10 value 446.366497
## iter  20 value 416.682302
## iter  30 value 408.652574
## iter  40 value 405.488625
## iter  50 value 404.460551
## iter  60 value 404.091122
## final  value 404.090770 
## converged
## # weights:  31
## initial  value 501.763220 
## iter  10 value 446.178992
## iter  20 value 420.705166
## iter  30 value 416.504611
## iter  40 value 413.612710
## iter  50 value 406.598226
## iter  60 value 403.031670
## iter  70 value 402.571185
## iter  80 value 401.801427
## iter  90 value 398.835359
## iter 100 value 396.123323
## final  value 396.123323 
## stopped after 100 iterations
## # weights:  51
## initial  value 449.542920 
## iter  10 value 436.442649
## iter  20 value 408.622732
## iter  30 value 396.444474
## iter  40 value 390.124517
## iter  50 value 382.046442
## iter  60 value 372.562920
## iter  70 value 354.133001
## iter  80 value 324.453356
## iter  90 value 319.810100
## iter 100 value 317.394809
## final  value 317.394809 
## stopped after 100 iterations
## # weights:  11
## initial  value 532.324761 
## final  value 446.857374 
## converged
## # weights:  31
## initial  value 448.759268 
## iter  10 value 426.013282
## iter  20 value 404.653299
## iter  30 value 401.710218
## iter  40 value 400.086657
## iter  50 value 399.526248
## iter  60 value 399.486017
## iter  70 value 399.483906
## iter  80 value 399.480581
## iter  90 value 399.471281
## iter 100 value 399.103754
## final  value 399.103754 
## stopped after 100 iterations
## # weights:  51
## initial  value 487.777340 
## iter  10 value 441.831011
## iter  20 value 434.676480
## iter  30 value 421.388513
## iter  40 value 413.091438
## iter  50 value 410.692558
## iter  60 value 408.400131
## iter  70 value 408.289758
## iter  80 value 408.228912
## iter  90 value 408.198774
## iter 100 value 408.071933
## final  value 408.071933 
## stopped after 100 iterations
## # weights:  11
## initial  value 519.792668 
## final  value 446.828630 
## converged
## # weights:  31
## initial  value 682.648859 
## iter  10 value 416.805654
## iter  20 value 402.768701
## iter  30 value 395.892374
## iter  40 value 393.316849
## iter  50 value 388.623001
## iter  60 value 387.778952
## final  value 387.778674 
## converged
## # weights:  51
## initial  value 566.596641 
## iter  10 value 439.574605
## iter  20 value 429.964534
## iter  30 value 412.393988
## iter  40 value 405.305310
## iter  50 value 403.705389
## iter  60 value 402.686638
## iter  70 value 402.131417
## iter  80 value 401.738095
## iter  90 value 401.734376
## final  value 401.734358 
## converged
## # weights:  11
## initial  value 473.790457 
## iter  10 value 421.648944
## iter  20 value 412.036381
## iter  30 value 408.931913
## iter  40 value 404.550877
## iter  50 value 397.590095
## iter  60 value 381.229070
## iter  70 value 326.710269
## iter  80 value 326.268094
## final  value 326.266097 
## converged
## # weights:  31
## initial  value 517.408829 
## iter  10 value 447.342837
## iter  20 value 441.527879
## iter  30 value 429.551885
## iter  40 value 425.952341
## iter  50 value 421.728292
## iter  60 value 410.970175
## iter  70 value 407.733773
## iter  80 value 402.994430
## iter  90 value 402.461254
## iter 100 value 402.345737
## final  value 402.345737 
## stopped after 100 iterations
## # weights:  51
## initial  value 643.265461 
## iter  10 value 440.147524
## iter  20 value 425.211798
## iter  30 value 410.009959
## iter  40 value 404.897665
## iter  50 value 389.516955
## iter  60 value 382.614755
## iter  70 value 379.522445
## iter  80 value 376.156444
## iter  90 value 373.954590
## iter 100 value 372.080673
## final  value 372.080673 
## stopped after 100 iterations
## # weights:  11
## initial  value 549.192609 
## final  value 446.857598 
## converged
## # weights:  31
## initial  value 614.406217 
## final  value 446.864450 
## converged
## # weights:  51
## initial  value 690.229509 
## iter  10 value 416.571314
## iter  20 value 405.424306
## iter  30 value 395.132715
## iter  40 value 389.815547
## iter  50 value 387.764854
## iter  60 value 386.357945
## iter  70 value 385.047906
## iter  80 value 383.802090
## iter  90 value 383.425483
## iter 100 value 382.904115
## final  value 382.904115 
## stopped after 100 iterations
## # weights:  11
## initial  value 499.997227 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 449.119388 
## iter  10 value 419.665474
## iter  20 value 411.523717
## iter  30 value 403.132887
## iter  40 value 396.090106
## iter  50 value 382.677891
## iter  60 value 363.423878
## iter  70 value 324.969009
## iter  80 value 321.616448
## iter  90 value 320.942113
## iter 100 value 319.652457
## final  value 319.652457 
## stopped after 100 iterations
## # weights:  51
## initial  value 623.254589 
## iter  10 value 427.873871
## iter  20 value 414.373592
## iter  30 value 401.848699
## iter  40 value 396.608841
## iter  50 value 392.647074
## iter  60 value 389.496007
## iter  70 value 389.290480
## iter  80 value 389.289790
## final  value 389.289771 
## converged
## # weights:  11
## initial  value 557.995031 
## iter  10 value 446.575462
## iter  20 value 433.480346
## iter  30 value 430.999365
## iter  40 value 411.445026
## iter  50 value 399.899309
## iter  60 value 368.112096
## iter  70 value 340.080099
## iter  80 value 335.152424
## final  value 335.079898 
## converged
## # weights:  31
## initial  value 453.693178 
## iter  10 value 444.485259
## iter  20 value 426.259967
## iter  30 value 409.529476
## iter  40 value 399.327401
## iter  50 value 397.346932
## iter  60 value 395.060197
## iter  70 value 363.757500
## iter  80 value 329.341975
## iter  90 value 318.454557
## iter 100 value 317.499581
## final  value 317.499581 
## stopped after 100 iterations
## # weights:  51
## initial  value 496.217999 
## iter  10 value 435.519889
## iter  20 value 411.807971
## iter  30 value 408.468738
## iter  40 value 405.028830
## iter  50 value 402.851197
## iter  60 value 402.237180
## iter  70 value 402.143375
## iter  80 value 401.635282
## iter  90 value 399.953388
## iter 100 value 398.777024
## final  value 398.777024 
## stopped after 100 iterations
## # weights:  11
## initial  value 458.814442 
## final  value 446.857350 
## converged
## # weights:  31
## initial  value 485.025617 
## iter  10 value 439.623086
## iter  20 value 433.476855
## iter  30 value 427.382463
## iter  40 value 425.834579
## iter  50 value 425.222716
## iter  60 value 425.106969
## iter  70 value 425.078756
## iter  80 value 425.055083
## iter  90 value 425.050509
## iter 100 value 424.982952
## final  value 424.982952 
## stopped after 100 iterations
## # weights:  51
## initial  value 558.333909 
## iter  10 value 439.478371
## iter  20 value 423.670805
## iter  30 value 418.333372
## iter  40 value 403.373555
## iter  50 value 400.095846
## iter  60 value 396.936655
## iter  70 value 393.536622
## iter  80 value 393.437357
## iter  90 value 393.297667
## iter 100 value 393.207083
## final  value 393.207083 
## stopped after 100 iterations
## # weights:  11
## initial  value 668.497215 
## final  value 447.909139 
## converged
## # weights:  31
## initial  value 504.051381 
## iter  10 value 438.598650
## iter  20 value 434.770575
## iter  30 value 434.331064
## iter  40 value 434.009767
## iter  50 value 433.974774
## final  value 433.974739 
## converged
## # weights:  51
## initial  value 448.530411 
## iter  10 value 430.624767
## iter  20 value 417.774101
## iter  30 value 410.037937
## iter  40 value 404.731737
## iter  50 value 399.936761
## iter  60 value 396.772073
## iter  70 value 393.854854
## iter  80 value 393.486851
## iter  90 value 393.478751
## final  value 393.478734 
## converged
## # weights:  11
## initial  value 494.385269 
## iter  10 value 447.898019
## iter  20 value 445.194668
## iter  30 value 444.088098
## iter  40 value 444.039987
## iter  50 value 442.834287
## iter  60 value 442.580912
## final  value 442.580752 
## converged
## # weights:  31
## initial  value 447.728395 
## iter  10 value 429.464227
## iter  20 value 405.731184
## iter  30 value 397.148132
## iter  40 value 392.773137
## iter  50 value 377.715696
## iter  60 value 368.022723
## iter  70 value 344.009591
## iter  80 value 315.445346
## iter  90 value 312.435730
## iter 100 value 311.680550
## final  value 311.680550 
## stopped after 100 iterations
## # weights:  51
## initial  value 533.341945 
## iter  10 value 427.609824
## iter  20 value 409.949270
## iter  30 value 403.550186
## iter  40 value 375.870917
## iter  50 value 366.651114
## iter  60 value 331.066296
## iter  70 value 313.698214
## iter  80 value 310.368930
## iter  90 value 309.822650
## iter 100 value 307.384866
## final  value 307.384866 
## stopped after 100 iterations
## # weights:  11
## initial  value 485.019636 
## final  value 447.909311 
## converged
## # weights:  31
## initial  value 482.721885 
## iter  10 value 447.047106
## iter  20 value 441.875805
## iter  30 value 440.110016
## iter  40 value 439.768580
## iter  50 value 432.268364
## iter  60 value 421.203276
## iter  70 value 415.953294
## iter  80 value 413.343089
## iter  90 value 408.187414
## iter 100 value 407.349250
## final  value 407.349250 
## stopped after 100 iterations
## # weights:  51
## initial  value 451.271378 
## iter  10 value 432.947398
## iter  20 value 411.707825
## iter  30 value 394.761513
## iter  40 value 389.429602
## iter  50 value 387.706599
## iter  60 value 387.279822
## iter  70 value 385.825526
## iter  80 value 385.078597
## iter  90 value 384.867761
## iter 100 value 384.796752
## final  value 384.796752 
## stopped after 100 iterations
## # weights:  11
## initial  value 447.778291 
## iter  10 value 444.552264
## iter  20 value 442.154272
## iter  30 value 441.140925
## final  value 441.135680 
## converged
## # weights:  31
## initial  value 611.595052 
## iter  10 value 427.787335
## iter  20 value 415.980203
## iter  30 value 410.125315
## iter  40 value 391.814859
## iter  50 value 385.503558
## iter  60 value 373.015366
## iter  70 value 362.670904
## iter  80 value 358.678559
## iter  90 value 357.916213
## iter 100 value 357.776894
## final  value 357.776894 
## stopped after 100 iterations
## # weights:  51
## initial  value 481.312445 
## iter  10 value 444.150631
## iter  20 value 440.403352
## iter  30 value 438.491325
## iter  40 value 437.685862
## iter  50 value 437.226028
## iter  60 value 436.950359
## iter  70 value 436.645257
## iter  80 value 436.393454
## iter  90 value 435.968519
## iter 100 value 435.857409
## final  value 435.857409 
## stopped after 100 iterations
## # weights:  11
## initial  value 498.128497 
## iter  10 value 442.814517
## iter  20 value 437.964725
## iter  30 value 433.532729
## iter  40 value 432.989723
## final  value 432.989713 
## converged
## # weights:  31
## initial  value 441.990558 
## iter  10 value 422.815713
## iter  20 value 405.440432
## iter  30 value 393.531535
## iter  40 value 387.230316
## iter  50 value 374.587012
## iter  60 value 353.396278
## iter  70 value 330.170804
## iter  80 value 328.354962
## iter  90 value 325.807510
## iter 100 value 323.936610
## final  value 323.936610 
## stopped after 100 iterations
## # weights:  51
## initial  value 637.426285 
## iter  10 value 430.942383
## iter  20 value 425.336334
## iter  30 value 414.278217
## iter  40 value 411.424333
## iter  50 value 409.461584
## iter  60 value 405.464875
## iter  70 value 395.163759
## iter  80 value 381.535163
## iter  90 value 375.165126
## iter 100 value 367.266267
## final  value 367.266267 
## stopped after 100 iterations
## # weights:  11
## initial  value 475.562573 
## iter  10 value 446.514433
## iter  20 value 444.767762
## final  value 444.757250 
## converged
## # weights:  31
## initial  value 447.440546 
## iter  10 value 445.738040
## iter  20 value 443.751230
## iter  30 value 439.446842
## iter  40 value 439.211934
## final  value 439.211375 
## converged
## # weights:  51
## initial  value 525.230335 
## iter  10 value 434.319083
## iter  20 value 420.572677
## iter  30 value 408.238123
## iter  40 value 405.226038
## iter  50 value 401.145910
## iter  60 value 399.510124
## iter  70 value 396.280817
## iter  80 value 368.171826
## iter  90 value 322.145643
## iter 100 value 313.255541
## final  value 313.255541 
## stopped after 100 iterations
## # weights:  11
## initial  value 585.825649 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 539.942063 
## final  value 446.857145 
## converged
## # weights:  51
## initial  value 571.576315 
## iter  10 value 432.969547
## iter  20 value 428.426183
## iter  30 value 426.771632
## iter  40 value 426.280610
## iter  50 value 425.332046
## iter  60 value 424.660923
## iter  70 value 424.324536
## iter  80 value 424.041340
## iter  90 value 423.884275
## iter 100 value 423.716604
## final  value 423.716604 
## stopped after 100 iterations
## # weights:  11
## initial  value 446.601597 
## iter  10 value 426.776944
## iter  20 value 421.009698
## iter  30 value 419.946033
## iter  40 value 419.725947
## iter  50 value 419.343126
## final  value 419.226922 
## converged
## # weights:  31
## initial  value 463.356727 
## iter  10 value 443.099760
## iter  20 value 437.933410
## iter  30 value 430.043283
## iter  40 value 415.023351
## iter  50 value 409.351619
## iter  60 value 401.416567
## iter  70 value 397.463658
## iter  80 value 380.843414
## iter  90 value 374.402271
## iter 100 value 343.132419
## final  value 343.132419 
## stopped after 100 iterations
## # weights:  51
## initial  value 494.012485 
## iter  10 value 427.119268
## iter  20 value 418.102541
## iter  30 value 409.032406
## iter  40 value 402.448581
## iter  50 value 394.935044
## iter  60 value 376.580226
## iter  70 value 369.580752
## iter  80 value 366.503601
## iter  90 value 355.339590
## iter 100 value 339.507547
## final  value 339.507547 
## stopped after 100 iterations
## # weights:  11
## initial  value 466.485908 
## iter  10 value 446.740176
## iter  20 value 446.471658
## iter  30 value 446.301382
## final  value 446.201174 
## converged
## # weights:  31
## initial  value 795.981270 
## iter  10 value 470.882667
## iter  20 value 438.980561
## iter  30 value 430.770119
## iter  40 value 427.380653
## iter  50 value 425.900972
## iter  60 value 425.216206
## iter  70 value 425.174533
## iter  80 value 425.168122
## iter  90 value 425.166681
## iter 100 value 425.165704
## final  value 425.165704 
## stopped after 100 iterations
## # weights:  51
## initial  value 496.179893 
## iter  10 value 435.058629
## iter  20 value 419.854786
## iter  30 value 404.971679
## iter  40 value 378.322249
## iter  50 value 330.854663
## iter  60 value 325.186757
## iter  70 value 317.068473
## iter  80 value 315.009405
## iter  90 value 314.689852
## iter 100 value 314.601255
## final  value 314.601255 
## stopped after 100 iterations
## # weights:  11
## initial  value 504.134550 
## final  value 446.857145 
## converged
## # weights:  31
## initial  value 523.909072 
## final  value 446.857145 
## converged
## # weights:  51
## initial  value 466.585000 
## iter  10 value 429.768681
## iter  20 value 401.600101
## iter  30 value 395.806385
## iter  40 value 380.310368
## iter  50 value 324.169008
## iter  60 value 317.060681
## iter  70 value 312.934260
## iter  80 value 312.381121
## iter  90 value 312.095945
## iter 100 value 311.773364
## final  value 311.773364 
## stopped after 100 iterations
## # weights:  11
## initial  value 592.806515 
## iter  10 value 444.059921
## iter  20 value 417.063664
## iter  30 value 411.176643
## iter  40 value 410.858695
## iter  50 value 400.105499
## iter  60 value 374.633398
## iter  70 value 349.919476
## iter  80 value 325.932975
## iter  90 value 325.532201
## final  value 325.498367 
## converged
## # weights:  31
## initial  value 470.249554 
## iter  10 value 429.160047
## iter  20 value 409.601734
## iter  30 value 404.511443
## iter  40 value 395.053965
## iter  50 value 391.459576
## iter  60 value 386.721042
## iter  70 value 373.540664
## iter  80 value 344.318389
## iter  90 value 328.026499
## iter 100 value 319.058435
## final  value 319.058435 
## stopped after 100 iterations
## # weights:  51
## initial  value 505.720093 
## iter  10 value 442.770394
## iter  20 value 427.492550
## iter  30 value 412.145056
## iter  40 value 405.313395
## iter  50 value 395.440056
## iter  60 value 390.372326
## iter  70 value 388.175642
## iter  80 value 386.284067
## iter  90 value 382.074381
## iter 100 value 379.860377
## final  value 379.860377 
## stopped after 100 iterations
## # weights:  11
## initial  value 447.634081 
## iter  10 value 441.292269
## iter  20 value 440.587289
## iter  30 value 440.119255
## iter  40 value 439.936705
## final  value 439.927966 
## converged
## # weights:  31
## initial  value 530.426505 
## iter  10 value 439.703919
## iter  20 value 433.576734
## iter  30 value 431.060526
## iter  40 value 418.948335
## iter  50 value 405.389500
## iter  60 value 394.743188
## iter  70 value 389.505600
## iter  80 value 386.813535
## iter  90 value 385.494069
## iter 100 value 385.146438
## final  value 385.146438 
## stopped after 100 iterations
## # weights:  51
## initial  value 449.309990 
## iter  10 value 428.879761
## iter  20 value 411.009135
## iter  30 value 404.232279
## iter  40 value 399.503184
## iter  50 value 397.304509
## iter  60 value 379.690499
## iter  70 value 326.887046
## iter  80 value 323.053451
## iter  90 value 322.221255
## iter 100 value 321.042584
## final  value 321.042584 
## stopped after 100 iterations
## # weights:  31
## initial  value 518.216217 
## iter  10 value 487.136798
## iter  20 value 461.393860
## iter  30 value 453.565517
## iter  40 value 452.247425
## iter  50 value 452.146495
## iter  60 value 451.597070
## iter  70 value 448.066461
## iter  80 value 435.395926
## iter  90 value 386.554916
## iter 100 value 366.591081
## final  value 366.591081 
## stopped after 100 iterations

4.d) Compare Algorithms Before Tuning

#results <- resamples(list(LM=fit.lm, LogReg=fit.glm, LDA=fit.lda, CART=fit.cart, NB=fit.nb, kNN=fit.knn, SVM=fit.svm, BagCART=fit.bagcart, RF=fit.rf, AdaBoost=fit.ada, GBM=fit.gbm, NNet=fit.nnet))
results <- resamples(list(LogReg=fit.glm, LDA=fit.lda, CART=fit.cart, NB=fit.nb, kNN=fit.knn, SVM=fit.svm, BagCART=fit.bagcart, RF=fit.rf, AdaBoost=fit.ada, GBM=fit.gbm, NNet=fit.nnet))
summary(results)
## 
## Call:
## summary.resamples(object = results)
## 
## Models: LogReg, LDA, CART, NB, kNN, SVM, BagCART, RF, AdaBoost, GBM, NNet 
## Number of resamples: 30 
## 
## Accuracy 
##               Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
## LogReg   0.6883117 0.7532468 0.7843472 0.7773354 0.8000256 0.8441558    0
## LDA      0.6883117 0.7662338 0.7792208 0.7760367 0.8000256 0.8311688    0
## CART     0.6753247 0.7272727 0.7532468 0.7513329 0.7784945 0.8181818    0
## NB       0.6883117 0.7368421 0.7532468 0.7555821 0.7792208 0.8051948    0
## kNN      0.6710526 0.7012987 0.7500000 0.7408863 0.7737953 0.8051948    0
## SVM      0.6883117 0.7426948 0.7727273 0.7682217 0.7922078 0.8441558    0
## BagCART  0.6493506 0.7272727 0.7532468 0.7526885 0.7792208 0.8289474    0
## RF       0.6883117 0.7402597 0.7662338 0.7674015 0.8000256 0.8441558    0
## AdaBoost 0.6623377 0.7272727 0.7532468 0.7548075 0.7763158 0.8289474    0
## GBM      0.6883117 0.7435065 0.7792208 0.7704090 0.8000256 0.8311688    0
## NNet     0.6363636 0.6883117 0.7385509 0.7287366 0.7629870 0.8441558    0
## 
## Kappa 
##               Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
## LogReg   0.2655008 0.4401342 0.4943168 0.4843861 0.5346588 0.6393443    0
## LDA      0.2786885 0.4425230 0.4897297 0.4802587 0.5346588 0.6057503    0
## CART     0.2786885 0.3886332 0.4307617 0.4292629 0.4878037 0.5792350    0
## NB       0.2786885 0.3980714 0.4627249 0.4488307 0.5033039 0.5531915    0
## kNN      0.2518219 0.3266159 0.4217663 0.4075877 0.4777357 0.5610034    0
## SVM      0.2228764 0.3990107 0.4717440 0.4599820 0.5232890 0.6518463    0
## BagCART  0.2233844 0.3836782 0.4532389 0.4431798 0.5147087 0.6016129    0
## RF       0.2655008 0.4115674 0.4688274 0.4746353 0.5456735 0.6393443    0
## AdaBoost 0.2323620 0.3959656 0.4318361 0.4425152 0.4955021 0.6091772    0
## GBM      0.2655008 0.4029214 0.4749298 0.4620527 0.5303393 0.6091772    0
## NNet     0.0754717 0.2352090 0.3858890 0.3561820 0.4627249 0.6518463    0
dotplot(results)

5. Improve Accuracy or Results

After we achieve a short list of machine learning algorithms with good level of accuracy, we can leverage ways to improve the accuracy of the models:

5.a) Algorithm Tuning

Finally, we will tune the three best-performing algorithms further and see whether we can get more accuracy out of them.

# Tuning algorithm #1 - Logistic Regression
# GLM requires no tuning parameters.
set.seed(seedNum)
fit.final1 <- fit.glm
print(fit.final1)
## Generalized Linear Model 
## 
## 768 samples
##   8 predictor
##   2 classes: 'neg', 'pos' 
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times) 
## Summary of sample sizes: 692, 691, 691, 691, 691, 692, ... 
## Resampling results:
## 
##   Accuracy   Kappa    
##   0.7773354  0.4843861
# Tuning algorithm #2 - Linear/Quadratic Discriminant Analysis
# LDA requires no tuning parameters.
set.seed(seedNum)
fit.final2 <- fit.lda
print(fit.final2)
## Linear Discriminant Analysis 
## 
## 768 samples
##   8 predictor
##   2 classes: 'neg', 'pos' 
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times) 
## Summary of sample sizes: 692, 691, 691, 691, 691, 692, ... 
## Resampling results:
## 
##   Accuracy   Kappa    
##   0.7760367  0.4802587
# Tuning algorithm #3 - Stochastic Gradient Boosting
set.seed(seedNum)
fit.final3 <- fit.lda
grid <- expand.grid(.n.trees=c(100, 250, 500), .shrinkage=c(0, 0.001, 0.1, 0.2, 0.3, 0.5, 1), .interaction.depth=c(1,2,3), .n.minobsinnode=c(1, 2, 3))
fit.final3 <- train(classVar~., data=dataset, method="gbm", metric="Accuracy", tuneGrid=grid, trControl=control)
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0002
##      3        1.2934             nan     0.0010    0.0002
##      4        1.2930             nan     0.0010    0.0002
##      5        1.2926             nan     0.0010    0.0002
##      6        1.2922             nan     0.0010    0.0002
##      7        1.2918             nan     0.0010    0.0002
##      8        1.2914             nan     0.0010    0.0002
##      9        1.2911             nan     0.0010    0.0002
##     10        1.2907             nan     0.0010    0.0002
##     20        1.2870             nan     0.0010    0.0002
##     40        1.2798             nan     0.0010    0.0002
##     60        1.2732             nan     0.0010    0.0002
##     80        1.2667             nan     0.0010    0.0001
##    100        1.2602             nan     0.0010    0.0002
##    120        1.2540             nan     0.0010    0.0001
##    140        1.2481             nan     0.0010    0.0001
##    160        1.2425             nan     0.0010    0.0001
##    180        1.2369             nan     0.0010    0.0001
##    200        1.2318             nan     0.0010    0.0001
##    220        1.2267             nan     0.0010    0.0001
##    240        1.2215             nan     0.0010    0.0001
##    260        1.2167             nan     0.0010    0.0001
##    280        1.2121             nan     0.0010    0.0001
##    300        1.2074             nan     0.0010    0.0001
##    320        1.2030             nan     0.0010    0.0001
##    340        1.1986             nan     0.0010    0.0001
##    360        1.1944             nan     0.0010    0.0001
##    380        1.1903             nan     0.0010    0.0001
##    400        1.1862             nan     0.0010    0.0001
##    420        1.1821             nan     0.0010    0.0001
##    440        1.1782             nan     0.0010    0.0001
##    460        1.1745             nan     0.0010    0.0001
##    480        1.1707             nan     0.0010    0.0001
##    500        1.1672             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0001
##      3        1.2935             nan     0.0010    0.0002
##      4        1.2931             nan     0.0010    0.0002
##      5        1.2928             nan     0.0010    0.0001
##      6        1.2924             nan     0.0010    0.0002
##      7        1.2920             nan     0.0010    0.0002
##      8        1.2916             nan     0.0010    0.0002
##      9        1.2913             nan     0.0010    0.0002
##     10        1.2909             nan     0.0010    0.0002
##     20        1.2872             nan     0.0010    0.0002
##     40        1.2799             nan     0.0010    0.0002
##     60        1.2732             nan     0.0010    0.0002
##     80        1.2665             nan     0.0010    0.0001
##    100        1.2603             nan     0.0010    0.0001
##    120        1.2541             nan     0.0010    0.0002
##    140        1.2481             nan     0.0010    0.0001
##    160        1.2423             nan     0.0010    0.0001
##    180        1.2366             nan     0.0010    0.0001
##    200        1.2312             nan     0.0010    0.0001
##    220        1.2260             nan     0.0010    0.0001
##    240        1.2209             nan     0.0010    0.0001
##    260        1.2160             nan     0.0010    0.0001
##    280        1.2114             nan     0.0010    0.0001
##    300        1.2067             nan     0.0010    0.0001
##    320        1.2023             nan     0.0010    0.0001
##    340        1.1978             nan     0.0010    0.0001
##    360        1.1937             nan     0.0010    0.0001
##    380        1.1895             nan     0.0010    0.0001
##    400        1.1855             nan     0.0010    0.0001
##    420        1.1814             nan     0.0010    0.0001
##    440        1.1776             nan     0.0010    0.0001
##    460        1.1739             nan     0.0010    0.0001
##    480        1.1702             nan     0.0010    0.0001
##    500        1.1666             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0002
##      3        1.2933             nan     0.0010    0.0002
##      4        1.2930             nan     0.0010    0.0002
##      5        1.2925             nan     0.0010    0.0001
##      6        1.2922             nan     0.0010    0.0002
##      7        1.2918             nan     0.0010    0.0002
##      8        1.2914             nan     0.0010    0.0002
##      9        1.2910             nan     0.0010    0.0001
##     10        1.2907             nan     0.0010    0.0002
##     20        1.2868             nan     0.0010    0.0002
##     40        1.2797             nan     0.0010    0.0001
##     60        1.2731             nan     0.0010    0.0002
##     80        1.2666             nan     0.0010    0.0001
##    100        1.2604             nan     0.0010    0.0001
##    120        1.2543             nan     0.0010    0.0001
##    140        1.2483             nan     0.0010    0.0001
##    160        1.2426             nan     0.0010    0.0001
##    180        1.2371             nan     0.0010    0.0001
##    200        1.2319             nan     0.0010    0.0001
##    220        1.2265             nan     0.0010    0.0001
##    240        1.2215             nan     0.0010    0.0001
##    260        1.2165             nan     0.0010    0.0001
##    280        1.2118             nan     0.0010    0.0001
##    300        1.2072             nan     0.0010    0.0001
##    320        1.2026             nan     0.0010    0.0001
##    340        1.1983             nan     0.0010    0.0001
##    360        1.1941             nan     0.0010    0.0001
##    380        1.1900             nan     0.0010    0.0001
##    400        1.1861             nan     0.0010    0.0001
##    420        1.1821             nan     0.0010    0.0001
##    440        1.1781             nan     0.0010    0.0001
##    460        1.1743             nan     0.0010    0.0001
##    480        1.1705             nan     0.0010    0.0001
##    500        1.1669             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2921             nan     0.0010    0.0002
##      6        1.2916             nan     0.0010    0.0002
##      7        1.2911             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2849             nan     0.0010    0.0002
##     40        1.2754             nan     0.0010    0.0002
##     60        1.2667             nan     0.0010    0.0002
##     80        1.2580             nan     0.0010    0.0002
##    100        1.2496             nan     0.0010    0.0002
##    120        1.2417             nan     0.0010    0.0002
##    140        1.2339             nan     0.0010    0.0001
##    160        1.2265             nan     0.0010    0.0001
##    180        1.2192             nan     0.0010    0.0002
##    200        1.2121             nan     0.0010    0.0001
##    220        1.2050             nan     0.0010    0.0001
##    240        1.1981             nan     0.0010    0.0001
##    260        1.1916             nan     0.0010    0.0002
##    280        1.1853             nan     0.0010    0.0002
##    300        1.1790             nan     0.0010    0.0001
##    320        1.1729             nan     0.0010    0.0001
##    340        1.1672             nan     0.0010    0.0001
##    360        1.1616             nan     0.0010    0.0001
##    380        1.1558             nan     0.0010    0.0001
##    400        1.1503             nan     0.0010    0.0001
##    420        1.1449             nan     0.0010    0.0001
##    440        1.1399             nan     0.0010    0.0001
##    460        1.1349             nan     0.0010    0.0001
##    480        1.1299             nan     0.0010    0.0001
##    500        1.1252             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2921             nan     0.0010    0.0002
##      6        1.2916             nan     0.0010    0.0002
##      7        1.2910             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2848             nan     0.0010    0.0002
##     40        1.2757             nan     0.0010    0.0002
##     60        1.2668             nan     0.0010    0.0002
##     80        1.2583             nan     0.0010    0.0002
##    100        1.2500             nan     0.0010    0.0002
##    120        1.2421             nan     0.0010    0.0002
##    140        1.2344             nan     0.0010    0.0002
##    160        1.2269             nan     0.0010    0.0001
##    180        1.2196             nan     0.0010    0.0002
##    200        1.2126             nan     0.0010    0.0002
##    220        1.2057             nan     0.0010    0.0002
##    240        1.1990             nan     0.0010    0.0002
##    260        1.1924             nan     0.0010    0.0001
##    280        1.1860             nan     0.0010    0.0001
##    300        1.1799             nan     0.0010    0.0001
##    320        1.1739             nan     0.0010    0.0001
##    340        1.1681             nan     0.0010    0.0001
##    360        1.1623             nan     0.0010    0.0001
##    380        1.1570             nan     0.0010    0.0001
##    400        1.1516             nan     0.0010    0.0001
##    420        1.1464             nan     0.0010    0.0001
##    440        1.1412             nan     0.0010    0.0001
##    460        1.1362             nan     0.0010    0.0001
##    480        1.1314             nan     0.0010    0.0001
##    500        1.1264             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2921             nan     0.0010    0.0002
##      6        1.2916             nan     0.0010    0.0002
##      7        1.2911             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2849             nan     0.0010    0.0002
##     40        1.2757             nan     0.0010    0.0002
##     60        1.2666             nan     0.0010    0.0002
##     80        1.2581             nan     0.0010    0.0002
##    100        1.2499             nan     0.0010    0.0002
##    120        1.2418             nan     0.0010    0.0002
##    140        1.2338             nan     0.0010    0.0002
##    160        1.2264             nan     0.0010    0.0002
##    180        1.2190             nan     0.0010    0.0002
##    200        1.2121             nan     0.0010    0.0002
##    220        1.2050             nan     0.0010    0.0002
##    240        1.1983             nan     0.0010    0.0001
##    260        1.1917             nan     0.0010    0.0001
##    280        1.1852             nan     0.0010    0.0001
##    300        1.1788             nan     0.0010    0.0002
##    320        1.1726             nan     0.0010    0.0001
##    340        1.1667             nan     0.0010    0.0001
##    360        1.1609             nan     0.0010    0.0001
##    380        1.1553             nan     0.0010    0.0001
##    400        1.1499             nan     0.0010    0.0001
##    420        1.1447             nan     0.0010    0.0001
##    440        1.1394             nan     0.0010    0.0001
##    460        1.1344             nan     0.0010    0.0001
##    480        1.1295             nan     0.0010    0.0001
##    500        1.1247             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2934             nan     0.0010    0.0002
##      3        1.2929             nan     0.0010    0.0002
##      4        1.2923             nan     0.0010    0.0003
##      5        1.2917             nan     0.0010    0.0003
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2906             nan     0.0010    0.0002
##      8        1.2901             nan     0.0010    0.0003
##      9        1.2896             nan     0.0010    0.0002
##     10        1.2891             nan     0.0010    0.0002
##     20        1.2835             nan     0.0010    0.0003
##     40        1.2727             nan     0.0010    0.0002
##     60        1.2624             nan     0.0010    0.0002
##     80        1.2523             nan     0.0010    0.0002
##    100        1.2426             nan     0.0010    0.0002
##    120        1.2333             nan     0.0010    0.0002
##    140        1.2243             nan     0.0010    0.0002
##    160        1.2157             nan     0.0010    0.0002
##    180        1.2074             nan     0.0010    0.0001
##    200        1.1989             nan     0.0010    0.0002
##    220        1.1909             nan     0.0010    0.0002
##    240        1.1832             nan     0.0010    0.0002
##    260        1.1758             nan     0.0010    0.0002
##    280        1.1684             nan     0.0010    0.0002
##    300        1.1613             nan     0.0010    0.0001
##    320        1.1544             nan     0.0010    0.0001
##    340        1.1476             nan     0.0010    0.0001
##    360        1.1411             nan     0.0010    0.0001
##    380        1.1347             nan     0.0010    0.0001
##    400        1.1286             nan     0.0010    0.0001
##    420        1.1227             nan     0.0010    0.0001
##    440        1.1170             nan     0.0010    0.0001
##    460        1.1112             nan     0.0010    0.0001
##    480        1.1054             nan     0.0010    0.0001
##    500        1.0998             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2934             nan     0.0010    0.0003
##      3        1.2929             nan     0.0010    0.0002
##      4        1.2923             nan     0.0010    0.0002
##      5        1.2919             nan     0.0010    0.0002
##      6        1.2913             nan     0.0010    0.0003
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2902             nan     0.0010    0.0002
##      9        1.2896             nan     0.0010    0.0003
##     10        1.2891             nan     0.0010    0.0002
##     20        1.2834             nan     0.0010    0.0003
##     40        1.2729             nan     0.0010    0.0003
##     60        1.2626             nan     0.0010    0.0002
##     80        1.2526             nan     0.0010    0.0002
##    100        1.2431             nan     0.0010    0.0002
##    120        1.2335             nan     0.0010    0.0002
##    140        1.2245             nan     0.0010    0.0002
##    160        1.2160             nan     0.0010    0.0001
##    180        1.2074             nan     0.0010    0.0002
##    200        1.1991             nan     0.0010    0.0001
##    220        1.1910             nan     0.0010    0.0002
##    240        1.1834             nan     0.0010    0.0002
##    260        1.1758             nan     0.0010    0.0002
##    280        1.1683             nan     0.0010    0.0002
##    300        1.1611             nan     0.0010    0.0002
##    320        1.1541             nan     0.0010    0.0001
##    340        1.1471             nan     0.0010    0.0001
##    360        1.1406             nan     0.0010    0.0001
##    380        1.1344             nan     0.0010    0.0001
##    400        1.1284             nan     0.0010    0.0001
##    420        1.1223             nan     0.0010    0.0001
##    440        1.1165             nan     0.0010    0.0001
##    460        1.1108             nan     0.0010    0.0001
##    480        1.1051             nan     0.0010    0.0001
##    500        1.0997             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2934             nan     0.0010    0.0003
##      3        1.2928             nan     0.0010    0.0003
##      4        1.2923             nan     0.0010    0.0002
##      5        1.2917             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0003
##      7        1.2906             nan     0.0010    0.0002
##      8        1.2900             nan     0.0010    0.0003
##      9        1.2895             nan     0.0010    0.0002
##     10        1.2890             nan     0.0010    0.0002
##     20        1.2836             nan     0.0010    0.0003
##     40        1.2731             nan     0.0010    0.0003
##     60        1.2627             nan     0.0010    0.0002
##     80        1.2529             nan     0.0010    0.0002
##    100        1.2433             nan     0.0010    0.0002
##    120        1.2339             nan     0.0010    0.0002
##    140        1.2247             nan     0.0010    0.0002
##    160        1.2160             nan     0.0010    0.0002
##    180        1.2075             nan     0.0010    0.0002
##    200        1.1992             nan     0.0010    0.0002
##    220        1.1913             nan     0.0010    0.0002
##    240        1.1835             nan     0.0010    0.0001
##    260        1.1759             nan     0.0010    0.0002
##    280        1.1687             nan     0.0010    0.0001
##    300        1.1612             nan     0.0010    0.0001
##    320        1.1541             nan     0.0010    0.0001
##    340        1.1473             nan     0.0010    0.0001
##    360        1.1407             nan     0.0010    0.0001
##    380        1.1343             nan     0.0010    0.0001
##    400        1.1280             nan     0.0010    0.0001
##    420        1.1221             nan     0.0010    0.0001
##    440        1.1160             nan     0.0010    0.0001
##    460        1.1104             nan     0.0010    0.0001
##    480        1.1046             nan     0.0010    0.0001
##    500        1.0991             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2604             nan     0.1000    0.0175
##      2        1.2332             nan     0.1000    0.0130
##      3        1.2062             nan     0.1000    0.0105
##      4        1.1849             nan     0.1000    0.0104
##      5        1.1700             nan     0.1000    0.0060
##      6        1.1523             nan     0.1000    0.0072
##      7        1.1373             nan     0.1000    0.0061
##      8        1.1225             nan     0.1000    0.0033
##      9        1.1086             nan     0.1000    0.0060
##     10        1.0929             nan     0.1000    0.0069
##     20        1.0074             nan     0.1000    0.0012
##     40        0.9273             nan     0.1000   -0.0010
##     60        0.8816             nan     0.1000   -0.0001
##     80        0.8559             nan     0.1000   -0.0008
##    100        0.8390             nan     0.1000   -0.0005
##    120        0.8255             nan     0.1000   -0.0008
##    140        0.8135             nan     0.1000   -0.0037
##    160        0.8024             nan     0.1000   -0.0009
##    180        0.7926             nan     0.1000   -0.0001
##    200        0.7839             nan     0.1000   -0.0007
##    220        0.7765             nan     0.1000   -0.0011
##    240        0.7701             nan     0.1000   -0.0016
##    260        0.7651             nan     0.1000   -0.0014
##    280        0.7583             nan     0.1000   -0.0005
##    300        0.7529             nan     0.1000   -0.0014
##    320        0.7478             nan     0.1000   -0.0008
##    340        0.7415             nan     0.1000   -0.0008
##    360        0.7360             nan     0.1000   -0.0009
##    380        0.7313             nan     0.1000   -0.0008
##    400        0.7276             nan     0.1000   -0.0009
##    420        0.7246             nan     0.1000   -0.0010
##    440        0.7215             nan     0.1000   -0.0008
##    460        0.7146             nan     0.1000   -0.0010
##    480        0.7086             nan     0.1000   -0.0007
##    500        0.7033             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2591             nan     0.1000    0.0164
##      2        1.2282             nan     0.1000    0.0145
##      3        1.2017             nan     0.1000    0.0116
##      4        1.1833             nan     0.1000    0.0093
##      5        1.1635             nan     0.1000    0.0070
##      6        1.1442             nan     0.1000    0.0083
##      7        1.1295             nan     0.1000    0.0053
##      8        1.1151             nan     0.1000    0.0066
##      9        1.0996             nan     0.1000    0.0051
##     10        1.0872             nan     0.1000    0.0036
##     20        1.0012             nan     0.1000    0.0011
##     40        0.9143             nan     0.1000    0.0002
##     60        0.8764             nan     0.1000   -0.0003
##     80        0.8527             nan     0.1000   -0.0002
##    100        0.8330             nan     0.1000    0.0002
##    120        0.8200             nan     0.1000   -0.0013
##    140        0.8091             nan     0.1000   -0.0019
##    160        0.7985             nan     0.1000   -0.0017
##    180        0.7886             nan     0.1000   -0.0003
##    200        0.7812             nan     0.1000   -0.0007
##    220        0.7744             nan     0.1000   -0.0016
##    240        0.7669             nan     0.1000   -0.0001
##    260        0.7615             nan     0.1000   -0.0010
##    280        0.7552             nan     0.1000   -0.0016
##    300        0.7496             nan     0.1000   -0.0007
##    320        0.7449             nan     0.1000   -0.0008
##    340        0.7403             nan     0.1000   -0.0020
##    360        0.7350             nan     0.1000   -0.0006
##    380        0.7281             nan     0.1000   -0.0004
##    400        0.7222             nan     0.1000   -0.0002
##    420        0.7185             nan     0.1000   -0.0010
##    440        0.7139             nan     0.1000   -0.0013
##    460        0.7112             nan     0.1000   -0.0007
##    480        0.7069             nan     0.1000   -0.0010
##    500        0.7043             nan     0.1000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2618             nan     0.1000    0.0172
##      2        1.2319             nan     0.1000    0.0151
##      3        1.2010             nan     0.1000    0.0118
##      4        1.1820             nan     0.1000    0.0077
##      5        1.1666             nan     0.1000    0.0064
##      6        1.1482             nan     0.1000    0.0082
##      7        1.1315             nan     0.1000    0.0076
##      8        1.1178             nan     0.1000    0.0061
##      9        1.1031             nan     0.1000    0.0066
##     10        1.0887             nan     0.1000    0.0053
##     20        1.0065             nan     0.1000    0.0014
##     40        0.9167             nan     0.1000   -0.0001
##     60        0.8806             nan     0.1000   -0.0004
##     80        0.8571             nan     0.1000   -0.0025
##    100        0.8395             nan     0.1000   -0.0008
##    120        0.8237             nan     0.1000   -0.0003
##    140        0.8097             nan     0.1000   -0.0005
##    160        0.7966             nan     0.1000   -0.0010
##    180        0.7856             nan     0.1000   -0.0011
##    200        0.7794             nan     0.1000   -0.0004
##    220        0.7728             nan     0.1000   -0.0014
##    240        0.7674             nan     0.1000   -0.0016
##    260        0.7605             nan     0.1000   -0.0009
##    280        0.7534             nan     0.1000   -0.0013
##    300        0.7472             nan     0.1000   -0.0003
##    320        0.7416             nan     0.1000   -0.0005
##    340        0.7368             nan     0.1000   -0.0017
##    360        0.7319             nan     0.1000   -0.0000
##    380        0.7254             nan     0.1000   -0.0017
##    400        0.7200             nan     0.1000   -0.0003
##    420        0.7153             nan     0.1000   -0.0013
##    440        0.7099             nan     0.1000   -0.0014
##    460        0.7056             nan     0.1000   -0.0019
##    480        0.7001             nan     0.1000   -0.0004
##    500        0.6966             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2490             nan     0.1000    0.0200
##      2        1.2120             nan     0.1000    0.0160
##      3        1.1790             nan     0.1000    0.0151
##      4        1.1507             nan     0.1000    0.0114
##      5        1.1271             nan     0.1000    0.0098
##      6        1.1061             nan     0.1000    0.0085
##      7        1.0855             nan     0.1000    0.0081
##      8        1.0654             nan     0.1000    0.0078
##      9        1.0485             nan     0.1000    0.0070
##     10        1.0327             nan     0.1000    0.0049
##     20        0.9375             nan     0.1000    0.0008
##     40        0.8467             nan     0.1000   -0.0003
##     60        0.8009             nan     0.1000   -0.0014
##     80        0.7673             nan     0.1000   -0.0023
##    100        0.7409             nan     0.1000   -0.0013
##    120        0.7142             nan     0.1000   -0.0013
##    140        0.6933             nan     0.1000   -0.0015
##    160        0.6752             nan     0.1000   -0.0007
##    180        0.6534             nan     0.1000   -0.0018
##    200        0.6336             nan     0.1000   -0.0018
##    220        0.6172             nan     0.1000   -0.0016
##    240        0.5989             nan     0.1000   -0.0011
##    260        0.5857             nan     0.1000    0.0004
##    280        0.5705             nan     0.1000   -0.0004
##    300        0.5564             nan     0.1000   -0.0014
##    320        0.5456             nan     0.1000   -0.0004
##    340        0.5329             nan     0.1000   -0.0006
##    360        0.5177             nan     0.1000   -0.0011
##    380        0.5063             nan     0.1000   -0.0015
##    400        0.4936             nan     0.1000   -0.0004
##    420        0.4838             nan     0.1000   -0.0007
##    440        0.4748             nan     0.1000   -0.0007
##    460        0.4664             nan     0.1000   -0.0012
##    480        0.4557             nan     0.1000   -0.0005
##    500        0.4464             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2519             nan     0.1000    0.0202
##      2        1.2121             nan     0.1000    0.0176
##      3        1.1785             nan     0.1000    0.0157
##      4        1.1466             nan     0.1000    0.0113
##      5        1.1238             nan     0.1000    0.0096
##      6        1.0974             nan     0.1000    0.0088
##      7        1.0782             nan     0.1000    0.0089
##      8        1.0632             nan     0.1000    0.0050
##      9        1.0479             nan     0.1000    0.0052
##     10        1.0314             nan     0.1000    0.0066
##     20        0.9420             nan     0.1000    0.0006
##     40        0.8503             nan     0.1000   -0.0000
##     60        0.8000             nan     0.1000   -0.0013
##     80        0.7643             nan     0.1000   -0.0012
##    100        0.7417             nan     0.1000   -0.0016
##    120        0.7114             nan     0.1000   -0.0010
##    140        0.6879             nan     0.1000   -0.0018
##    160        0.6690             nan     0.1000   -0.0008
##    180        0.6498             nan     0.1000   -0.0015
##    200        0.6309             nan     0.1000   -0.0011
##    220        0.6143             nan     0.1000   -0.0011
##    240        0.5975             nan     0.1000   -0.0007
##    260        0.5834             nan     0.1000   -0.0011
##    280        0.5688             nan     0.1000   -0.0013
##    300        0.5601             nan     0.1000   -0.0011
##    320        0.5476             nan     0.1000   -0.0008
##    340        0.5354             nan     0.1000   -0.0007
##    360        0.5207             nan     0.1000   -0.0005
##    380        0.5104             nan     0.1000   -0.0013
##    400        0.5011             nan     0.1000   -0.0004
##    420        0.4895             nan     0.1000   -0.0011
##    440        0.4782             nan     0.1000   -0.0011
##    460        0.4636             nan     0.1000   -0.0004
##    480        0.4542             nan     0.1000   -0.0007
##    500        0.4449             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2489             nan     0.1000    0.0198
##      2        1.2049             nan     0.1000    0.0190
##      3        1.1749             nan     0.1000    0.0122
##      4        1.1438             nan     0.1000    0.0123
##      5        1.1182             nan     0.1000    0.0085
##      6        1.0941             nan     0.1000    0.0116
##      7        1.0737             nan     0.1000    0.0074
##      8        1.0543             nan     0.1000    0.0084
##      9        1.0403             nan     0.1000    0.0045
##     10        1.0245             nan     0.1000    0.0050
##     20        0.9340             nan     0.1000    0.0000
##     40        0.8585             nan     0.1000    0.0006
##     60        0.8145             nan     0.1000   -0.0006
##     80        0.7751             nan     0.1000   -0.0008
##    100        0.7433             nan     0.1000   -0.0024
##    120        0.7220             nan     0.1000   -0.0017
##    140        0.6992             nan     0.1000   -0.0017
##    160        0.6804             nan     0.1000   -0.0017
##    180        0.6597             nan     0.1000   -0.0004
##    200        0.6390             nan     0.1000   -0.0009
##    220        0.6208             nan     0.1000   -0.0021
##    240        0.6076             nan     0.1000   -0.0005
##    260        0.5954             nan     0.1000   -0.0026
##    280        0.5782             nan     0.1000   -0.0014
##    300        0.5651             nan     0.1000   -0.0011
##    320        0.5535             nan     0.1000   -0.0008
##    340        0.5413             nan     0.1000   -0.0009
##    360        0.5298             nan     0.1000   -0.0010
##    380        0.5159             nan     0.1000   -0.0003
##    400        0.5065             nan     0.1000   -0.0025
##    420        0.4975             nan     0.1000   -0.0007
##    440        0.4861             nan     0.1000   -0.0011
##    460        0.4754             nan     0.1000   -0.0003
##    480        0.4676             nan     0.1000   -0.0021
##    500        0.4586             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2431             nan     0.1000    0.0248
##      2        1.2008             nan     0.1000    0.0189
##      3        1.1621             nan     0.1000    0.0166
##      4        1.1254             nan     0.1000    0.0153
##      5        1.0980             nan     0.1000    0.0086
##      6        1.0710             nan     0.1000    0.0106
##      7        1.0472             nan     0.1000    0.0114
##      8        1.0260             nan     0.1000    0.0081
##      9        1.0104             nan     0.1000    0.0049
##     10        0.9987             nan     0.1000    0.0019
##     20        0.9038             nan     0.1000    0.0005
##     40        0.8048             nan     0.1000   -0.0016
##     60        0.7492             nan     0.1000   -0.0011
##     80        0.7085             nan     0.1000   -0.0012
##    100        0.6688             nan     0.1000   -0.0018
##    120        0.6357             nan     0.1000   -0.0017
##    140        0.6061             nan     0.1000   -0.0016
##    160        0.5792             nan     0.1000   -0.0008
##    180        0.5498             nan     0.1000   -0.0021
##    200        0.5252             nan     0.1000   -0.0016
##    220        0.4979             nan     0.1000    0.0000
##    240        0.4779             nan     0.1000   -0.0005
##    260        0.4566             nan     0.1000   -0.0012
##    280        0.4393             nan     0.1000   -0.0008
##    300        0.4199             nan     0.1000   -0.0011
##    320        0.4063             nan     0.1000   -0.0011
##    340        0.3915             nan     0.1000   -0.0005
##    360        0.3754             nan     0.1000   -0.0010
##    380        0.3606             nan     0.1000   -0.0010
##    400        0.3494             nan     0.1000   -0.0005
##    420        0.3373             nan     0.1000   -0.0012
##    440        0.3268             nan     0.1000   -0.0012
##    460        0.3147             nan     0.1000   -0.0005
##    480        0.3025             nan     0.1000   -0.0006
##    500        0.2920             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2450             nan     0.1000    0.0251
##      2        1.2075             nan     0.1000    0.0168
##      3        1.1658             nan     0.1000    0.0204
##      4        1.1290             nan     0.1000    0.0129
##      5        1.0992             nan     0.1000    0.0107
##      6        1.0729             nan     0.1000    0.0089
##      7        1.0529             nan     0.1000    0.0083
##      8        1.0373             nan     0.1000    0.0061
##      9        1.0179             nan     0.1000    0.0084
##     10        0.9986             nan     0.1000    0.0066
##     20        0.8999             nan     0.1000    0.0011
##     40        0.8053             nan     0.1000   -0.0006
##     60        0.7421             nan     0.1000   -0.0013
##     80        0.7014             nan     0.1000   -0.0022
##    100        0.6651             nan     0.1000   -0.0008
##    120        0.6281             nan     0.1000   -0.0010
##    140        0.5928             nan     0.1000   -0.0005
##    160        0.5640             nan     0.1000   -0.0016
##    180        0.5393             nan     0.1000   -0.0014
##    200        0.5187             nan     0.1000   -0.0014
##    220        0.4946             nan     0.1000   -0.0007
##    240        0.4725             nan     0.1000   -0.0015
##    260        0.4547             nan     0.1000   -0.0014
##    280        0.4304             nan     0.1000   -0.0009
##    300        0.4131             nan     0.1000   -0.0010
##    320        0.3967             nan     0.1000   -0.0011
##    340        0.3825             nan     0.1000   -0.0008
##    360        0.3683             nan     0.1000   -0.0007
##    380        0.3528             nan     0.1000   -0.0008
##    400        0.3402             nan     0.1000   -0.0013
##    420        0.3276             nan     0.1000   -0.0011
##    440        0.3153             nan     0.1000   -0.0008
##    460        0.3015             nan     0.1000   -0.0003
##    480        0.2908             nan     0.1000   -0.0007
##    500        0.2822             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2400             nan     0.1000    0.0265
##      2        1.1940             nan     0.1000    0.0179
##      3        1.1592             nan     0.1000    0.0113
##      4        1.1295             nan     0.1000    0.0104
##      5        1.1020             nan     0.1000    0.0114
##      6        1.0740             nan     0.1000    0.0113
##      7        1.0502             nan     0.1000    0.0086
##      8        1.0296             nan     0.1000    0.0087
##      9        1.0121             nan     0.1000    0.0069
##     10        0.9960             nan     0.1000    0.0048
##     20        0.8978             nan     0.1000   -0.0012
##     40        0.8082             nan     0.1000   -0.0004
##     60        0.7594             nan     0.1000   -0.0016
##     80        0.7176             nan     0.1000   -0.0010
##    100        0.6810             nan     0.1000   -0.0022
##    120        0.6462             nan     0.1000   -0.0007
##    140        0.6181             nan     0.1000   -0.0016
##    160        0.5799             nan     0.1000   -0.0006
##    180        0.5503             nan     0.1000   -0.0012
##    200        0.5213             nan     0.1000   -0.0020
##    220        0.4963             nan     0.1000   -0.0019
##    240        0.4750             nan     0.1000   -0.0007
##    260        0.4584             nan     0.1000   -0.0012
##    280        0.4363             nan     0.1000   -0.0004
##    300        0.4195             nan     0.1000   -0.0012
##    320        0.4028             nan     0.1000   -0.0003
##    340        0.3877             nan     0.1000   -0.0005
##    360        0.3728             nan     0.1000   -0.0010
##    380        0.3608             nan     0.1000   -0.0015
##    400        0.3472             nan     0.1000   -0.0007
##    420        0.3337             nan     0.1000   -0.0007
##    440        0.3211             nan     0.1000   -0.0008
##    460        0.3096             nan     0.1000   -0.0009
##    480        0.3016             nan     0.1000   -0.0010
##    500        0.2898             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2192             nan     0.2000    0.0308
##      2        1.1762             nan     0.2000    0.0164
##      3        1.1461             nan     0.2000    0.0094
##      4        1.1211             nan     0.2000    0.0096
##      5        1.0935             nan     0.2000    0.0105
##      6        1.0697             nan     0.2000    0.0098
##      7        1.0474             nan     0.2000    0.0068
##      8        1.0294             nan     0.2000    0.0059
##      9        1.0092             nan     0.2000    0.0091
##     10        0.9994             nan     0.2000    0.0016
##     20        0.9219             nan     0.2000   -0.0023
##     40        0.8602             nan     0.2000   -0.0026
##     60        0.8307             nan     0.2000   -0.0002
##     80        0.8017             nan     0.2000   -0.0009
##    100        0.7810             nan     0.2000   -0.0023
##    120        0.7628             nan     0.2000    0.0001
##    140        0.7522             nan     0.2000   -0.0017
##    160        0.7340             nan     0.2000   -0.0010
##    180        0.7263             nan     0.2000   -0.0011
##    200        0.7168             nan     0.2000   -0.0009
##    220        0.7094             nan     0.2000   -0.0036
##    240        0.7004             nan     0.2000   -0.0020
##    260        0.6890             nan     0.2000   -0.0012
##    280        0.6784             nan     0.2000   -0.0021
##    300        0.6731             nan     0.2000   -0.0011
##    320        0.6666             nan     0.2000   -0.0010
##    340        0.6578             nan     0.2000    0.0000
##    360        0.6541             nan     0.2000   -0.0007
##    380        0.6519             nan     0.2000   -0.0031
##    400        0.6409             nan     0.2000   -0.0009
##    420        0.6367             nan     0.2000   -0.0005
##    440        0.6332             nan     0.2000   -0.0028
##    460        0.6271             nan     0.2000   -0.0011
##    480        0.6210             nan     0.2000   -0.0016
##    500        0.6194             nan     0.2000   -0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2247             nan     0.2000    0.0319
##      2        1.1748             nan     0.2000    0.0239
##      3        1.1426             nan     0.2000    0.0127
##      4        1.1168             nan     0.2000    0.0123
##      5        1.0982             nan     0.2000    0.0052
##      6        1.0745             nan     0.2000    0.0113
##      7        1.0504             nan     0.2000    0.0126
##      8        1.0351             nan     0.2000    0.0040
##      9        1.0159             nan     0.2000    0.0099
##     10        1.0064             nan     0.2000    0.0009
##     20        0.9272             nan     0.2000    0.0014
##     40        0.8584             nan     0.2000   -0.0026
##     60        0.8221             nan     0.2000   -0.0030
##     80        0.7993             nan     0.2000   -0.0012
##    100        0.7847             nan     0.2000   -0.0009
##    120        0.7694             nan     0.2000   -0.0008
##    140        0.7584             nan     0.2000   -0.0018
##    160        0.7467             nan     0.2000   -0.0015
##    180        0.7372             nan     0.2000   -0.0023
##    200        0.7297             nan     0.2000   -0.0008
##    220        0.7200             nan     0.2000   -0.0009
##    240        0.7126             nan     0.2000   -0.0013
##    260        0.7085             nan     0.2000   -0.0012
##    280        0.7013             nan     0.2000   -0.0023
##    300        0.6905             nan     0.2000   -0.0021
##    320        0.6872             nan     0.2000   -0.0024
##    340        0.6778             nan     0.2000   -0.0013
##    360        0.6731             nan     0.2000   -0.0018
##    380        0.6670             nan     0.2000   -0.0007
##    400        0.6595             nan     0.2000   -0.0017
##    420        0.6551             nan     0.2000   -0.0008
##    440        0.6490             nan     0.2000   -0.0013
##    460        0.6476             nan     0.2000   -0.0039
##    480        0.6397             nan     0.2000   -0.0028
##    500        0.6341             nan     0.2000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2248             nan     0.2000    0.0306
##      2        1.1755             nan     0.2000    0.0172
##      3        1.1435             nan     0.2000    0.0135
##      4        1.1175             nan     0.2000    0.0093
##      5        1.0883             nan     0.2000    0.0144
##      6        1.0626             nan     0.2000    0.0105
##      7        1.0425             nan     0.2000    0.0090
##      8        1.0273             nan     0.2000    0.0044
##      9        1.0139             nan     0.2000    0.0048
##     10        0.9979             nan     0.2000    0.0070
##     20        0.9199             nan     0.2000    0.0002
##     40        0.8583             nan     0.2000   -0.0012
##     60        0.8298             nan     0.2000   -0.0025
##     80        0.8089             nan     0.2000   -0.0031
##    100        0.7850             nan     0.2000   -0.0021
##    120        0.7672             nan     0.2000   -0.0015
##    140        0.7575             nan     0.2000   -0.0031
##    160        0.7468             nan     0.2000   -0.0010
##    180        0.7380             nan     0.2000   -0.0018
##    200        0.7267             nan     0.2000   -0.0025
##    220        0.7173             nan     0.2000   -0.0015
##    240        0.7082             nan     0.2000   -0.0012
##    260        0.6992             nan     0.2000   -0.0009
##    280        0.6915             nan     0.2000   -0.0013
##    300        0.6814             nan     0.2000   -0.0019
##    320        0.6776             nan     0.2000   -0.0019
##    340        0.6686             nan     0.2000   -0.0011
##    360        0.6597             nan     0.2000   -0.0014
##    380        0.6531             nan     0.2000   -0.0022
##    400        0.6487             nan     0.2000   -0.0022
##    420        0.6428             nan     0.2000   -0.0029
##    440        0.6362             nan     0.2000   -0.0009
##    460        0.6336             nan     0.2000   -0.0023
##    480        0.6268             nan     0.2000   -0.0007
##    500        0.6193             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2080             nan     0.2000    0.0413
##      2        1.1478             nan     0.2000    0.0288
##      3        1.0970             nan     0.2000    0.0197
##      4        1.0510             nan     0.2000    0.0188
##      5        1.0197             nan     0.2000    0.0127
##      6        0.9994             nan     0.2000    0.0048
##      7        0.9820             nan     0.2000    0.0050
##      8        0.9603             nan     0.2000    0.0029
##      9        0.9498             nan     0.2000    0.0026
##     10        0.9380             nan     0.2000    0.0014
##     20        0.8510             nan     0.2000   -0.0028
##     40        0.7710             nan     0.2000   -0.0021
##     60        0.7287             nan     0.2000   -0.0026
##     80        0.6934             nan     0.2000   -0.0017
##    100        0.6633             nan     0.2000   -0.0025
##    120        0.6291             nan     0.2000   -0.0008
##    140        0.6043             nan     0.2000   -0.0047
##    160        0.5644             nan     0.2000   -0.0023
##    180        0.5384             nan     0.2000   -0.0024
##    200        0.5137             nan     0.2000   -0.0009
##    220        0.4927             nan     0.2000   -0.0037
##    240        0.4706             nan     0.2000   -0.0029
##    260        0.4441             nan     0.2000   -0.0020
##    280        0.4224             nan     0.2000   -0.0019
##    300        0.4073             nan     0.2000   -0.0017
##    320        0.3900             nan     0.2000   -0.0023
##    340        0.3791             nan     0.2000   -0.0037
##    360        0.3638             nan     0.2000   -0.0021
##    380        0.3432             nan     0.2000   -0.0012
##    400        0.3293             nan     0.2000   -0.0009
##    420        0.3174             nan     0.2000   -0.0012
##    440        0.3038             nan     0.2000   -0.0020
##    460        0.2941             nan     0.2000   -0.0008
##    480        0.2837             nan     0.2000   -0.0010
##    500        0.2746             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2058             nan     0.2000    0.0412
##      2        1.1396             nan     0.2000    0.0247
##      3        1.0984             nan     0.2000    0.0149
##      4        1.0528             nan     0.2000    0.0125
##      5        1.0276             nan     0.2000    0.0091
##      6        1.0112             nan     0.2000    0.0021
##      7        0.9852             nan     0.2000    0.0033
##      8        0.9685             nan     0.2000    0.0045
##      9        0.9569             nan     0.2000   -0.0020
##     10        0.9425             nan     0.2000    0.0032
##     20        0.8571             nan     0.2000    0.0009
##     40        0.7872             nan     0.2000   -0.0021
##     60        0.7388             nan     0.2000   -0.0051
##     80        0.7001             nan     0.2000   -0.0016
##    100        0.6696             nan     0.2000   -0.0030
##    120        0.6358             nan     0.2000   -0.0027
##    140        0.6000             nan     0.2000   -0.0023
##    160        0.5734             nan     0.2000   -0.0018
##    180        0.5463             nan     0.2000   -0.0029
##    200        0.5221             nan     0.2000   -0.0014
##    220        0.4970             nan     0.2000   -0.0037
##    240        0.4741             nan     0.2000   -0.0025
##    260        0.4580             nan     0.2000   -0.0020
##    280        0.4413             nan     0.2000   -0.0014
##    300        0.4187             nan     0.2000   -0.0012
##    320        0.4013             nan     0.2000   -0.0021
##    340        0.3894             nan     0.2000   -0.0021
##    360        0.3755             nan     0.2000   -0.0006
##    380        0.3627             nan     0.2000   -0.0019
##    400        0.3509             nan     0.2000   -0.0030
##    420        0.3357             nan     0.2000   -0.0014
##    440        0.3241             nan     0.2000   -0.0010
##    460        0.3134             nan     0.2000   -0.0014
##    480        0.3029             nan     0.2000   -0.0008
##    500        0.2939             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2094             nan     0.2000    0.0361
##      2        1.1368             nan     0.2000    0.0331
##      3        1.0960             nan     0.2000    0.0113
##      4        1.0569             nan     0.2000    0.0160
##      5        1.0279             nan     0.2000    0.0084
##      6        1.0013             nan     0.2000    0.0073
##      7        0.9784             nan     0.2000    0.0082
##      8        0.9610             nan     0.2000    0.0050
##      9        0.9491             nan     0.2000    0.0030
##     10        0.9374             nan     0.2000    0.0003
##     20        0.8513             nan     0.2000   -0.0021
##     40        0.7765             nan     0.2000   -0.0024
##     60        0.7248             nan     0.2000   -0.0029
##     80        0.6987             nan     0.2000   -0.0009
##    100        0.6681             nan     0.2000   -0.0019
##    120        0.6283             nan     0.2000   -0.0022
##    140        0.6067             nan     0.2000   -0.0034
##    160        0.5785             nan     0.2000   -0.0021
##    180        0.5486             nan     0.2000   -0.0036
##    200        0.5247             nan     0.2000   -0.0028
##    220        0.5026             nan     0.2000   -0.0031
##    240        0.4831             nan     0.2000   -0.0027
##    260        0.4612             nan     0.2000   -0.0026
##    280        0.4422             nan     0.2000   -0.0023
##    300        0.4268             nan     0.2000   -0.0013
##    320        0.4085             nan     0.2000   -0.0010
##    340        0.3950             nan     0.2000   -0.0025
##    360        0.3744             nan     0.2000   -0.0018
##    380        0.3626             nan     0.2000   -0.0020
##    400        0.3465             nan     0.2000   -0.0008
##    420        0.3338             nan     0.2000   -0.0022
##    440        0.3248             nan     0.2000   -0.0024
##    460        0.3144             nan     0.2000   -0.0024
##    480        0.2992             nan     0.2000   -0.0005
##    500        0.2865             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1908             nan     0.2000    0.0427
##      2        1.1149             nan     0.2000    0.0351
##      3        1.0688             nan     0.2000    0.0164
##      4        1.0212             nan     0.2000    0.0207
##      5        0.9944             nan     0.2000    0.0076
##      6        0.9672             nan     0.2000    0.0081
##      7        0.9412             nan     0.2000    0.0067
##      8        0.9224             nan     0.2000    0.0058
##      9        0.9058             nan     0.2000    0.0038
##     10        0.8923             nan     0.2000   -0.0001
##     20        0.7961             nan     0.2000   -0.0008
##     40        0.7010             nan     0.2000   -0.0017
##     60        0.6334             nan     0.2000   -0.0047
##     80        0.5772             nan     0.2000   -0.0023
##    100        0.5496             nan     0.2000   -0.0046
##    120        0.5041             nan     0.2000   -0.0031
##    140        0.4638             nan     0.2000   -0.0047
##    160        0.4256             nan     0.2000   -0.0014
##    180        0.3916             nan     0.2000   -0.0033
##    200        0.3629             nan     0.2000   -0.0017
##    220        0.3309             nan     0.2000    0.0003
##    240        0.3078             nan     0.2000   -0.0009
##    260        0.2856             nan     0.2000   -0.0019
##    280        0.2681             nan     0.2000   -0.0029
##    300        0.2485             nan     0.2000   -0.0010
##    320        0.2308             nan     0.2000   -0.0011
##    340        0.2186             nan     0.2000   -0.0018
##    360        0.2048             nan     0.2000   -0.0008
##    380        0.1904             nan     0.2000   -0.0008
##    400        0.1788             nan     0.2000   -0.0002
##    420        0.1680             nan     0.2000   -0.0006
##    440        0.1558             nan     0.2000   -0.0021
##    460        0.1451             nan     0.2000   -0.0006
##    480        0.1369             nan     0.2000   -0.0005
##    500        0.1295             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2056             nan     0.2000    0.0347
##      2        1.1331             nan     0.2000    0.0300
##      3        1.0757             nan     0.2000    0.0262
##      4        1.0360             nan     0.2000    0.0158
##      5        1.0060             nan     0.2000    0.0102
##      6        0.9812             nan     0.2000    0.0061
##      7        0.9554             nan     0.2000    0.0092
##      8        0.9419             nan     0.2000   -0.0045
##      9        0.9228             nan     0.2000    0.0036
##     10        0.9080             nan     0.2000    0.0038
##     20        0.8122             nan     0.2000   -0.0024
##     40        0.7196             nan     0.2000   -0.0013
##     60        0.6444             nan     0.2000   -0.0016
##     80        0.5872             nan     0.2000   -0.0015
##    100        0.5363             nan     0.2000   -0.0017
##    120        0.4911             nan     0.2000   -0.0006
##    140        0.4443             nan     0.2000   -0.0036
##    160        0.4075             nan     0.2000   -0.0015
##    180        0.3739             nan     0.2000   -0.0037
##    200        0.3772             nan     0.2000   -0.0012
##    220        0.3273             nan     0.2000   -0.0016
##    240        0.3003             nan     0.2000   -0.0006
##    260        0.2774             nan     0.2000   -0.0010
##    280        0.2579             nan     0.2000   -0.0012
##    300        0.2380             nan     0.2000   -0.0006
##    320        0.2183             nan     0.2000   -0.0013
##    340        0.2050             nan     0.2000   -0.0006
##    360        0.1914             nan     0.2000   -0.0002
##    380        0.1787             nan     0.2000   -0.0015
##    400        0.1665             nan     0.2000   -0.0013
##    420        0.1559             nan     0.2000   -0.0007
##    440        0.1460             nan     0.2000   -0.0013
##    460        0.1361             nan     0.2000   -0.0014
##    480        0.1273             nan     0.2000   -0.0009
##    500        0.1208             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1948             nan     0.2000    0.0432
##      2        1.1169             nan     0.2000    0.0323
##      3        1.0731             nan     0.2000    0.0129
##      4        1.0264             nan     0.2000    0.0189
##      5        0.9970             nan     0.2000    0.0073
##      6        0.9792             nan     0.2000    0.0019
##      7        0.9626             nan     0.2000    0.0009
##      8        0.9369             nan     0.2000    0.0020
##      9        0.9185             nan     0.2000    0.0022
##     10        0.9040             nan     0.2000    0.0045
##     20        0.8123             nan     0.2000   -0.0019
##     40        0.7199             nan     0.2000   -0.0008
##     60        0.6546             nan     0.2000   -0.0015
##     80        0.5996             nan     0.2000   -0.0046
##    100        0.5444             nan     0.2000   -0.0034
##    120        0.5031             nan     0.2000   -0.0016
##    140        0.4657             nan     0.2000   -0.0026
##    160        0.4283             nan     0.2000   -0.0012
##    180        0.3958             nan     0.2000   -0.0037
##    200        0.3634             nan     0.2000   -0.0018
##    220        0.3349             nan     0.2000   -0.0029
##    240        0.3112             nan     0.2000   -0.0033
##    260        0.2865             nan     0.2000   -0.0020
##    280        0.2682             nan     0.2000   -0.0002
##    300        0.2490             nan     0.2000   -0.0004
##    320        0.2329             nan     0.2000   -0.0013
##    340        0.2179             nan     0.2000   -0.0017
##    360        0.2022             nan     0.2000   -0.0011
##    380        0.1896             nan     0.2000   -0.0022
##    400        0.1803             nan     0.2000   -0.0012
##    420        0.1693             nan     0.2000   -0.0009
##    440        0.1594             nan     0.2000   -0.0006
##    460        0.1488             nan     0.2000   -0.0010
##    480        0.1401             nan     0.2000   -0.0009
##    500        0.1305             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1976             nan     0.3000    0.0422
##      2        1.1515             nan     0.3000    0.0196
##      3        1.1085             nan     0.3000    0.0179
##      4        1.0697             nan     0.3000    0.0178
##      5        1.0347             nan     0.3000    0.0137
##      6        1.0169             nan     0.3000    0.0035
##      7        0.9958             nan     0.3000    0.0073
##      8        0.9801             nan     0.3000    0.0037
##      9        0.9728             nan     0.3000   -0.0036
##     10        0.9609             nan     0.3000    0.0063
##     20        0.8941             nan     0.3000    0.0000
##     40        0.8337             nan     0.3000   -0.0020
##     60        0.8029             nan     0.3000   -0.0042
##     80        0.7870             nan     0.3000   -0.0028
##    100        0.7683             nan     0.3000   -0.0019
##    120        0.7477             nan     0.3000   -0.0023
##    140        0.7329             nan     0.3000   -0.0021
##    160        0.7231             nan     0.3000   -0.0020
##    180        0.7031             nan     0.3000   -0.0049
##    200        0.6885             nan     0.3000   -0.0038
##    220        0.6778             nan     0.3000   -0.0025
##    240        0.6654             nan     0.3000   -0.0022
##    260        0.6536             nan     0.3000   -0.0027
##    280        0.6448             nan     0.3000   -0.0026
##    300        0.6323             nan     0.3000   -0.0033
##    320        0.6229             nan     0.3000   -0.0041
##    340        0.6172             nan     0.3000   -0.0053
##    360        0.6062             nan     0.3000   -0.0022
##    380        0.6021             nan     0.3000   -0.0040
##    400        0.5916             nan     0.3000   -0.0027
##    420        0.5845             nan     0.3000   -0.0033
##    440        0.5762             nan     0.3000   -0.0012
##    460        0.5693             nan     0.3000   -0.0022
##    480        0.5639             nan     0.3000   -0.0008
##    500        0.5571             nan     0.3000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1969             nan     0.3000    0.0455
##      2        1.1452             nan     0.3000    0.0237
##      3        1.1030             nan     0.3000    0.0170
##      4        1.0562             nan     0.3000    0.0129
##      5        1.0330             nan     0.3000    0.0117
##      6        1.0243             nan     0.3000   -0.0071
##      7        1.0042             nan     0.3000    0.0082
##      8        0.9869             nan     0.3000    0.0047
##      9        0.9728             nan     0.3000    0.0050
##     10        0.9619             nan     0.3000    0.0026
##     20        0.8796             nan     0.3000   -0.0009
##     40        0.8254             nan     0.3000   -0.0044
##     60        0.7991             nan     0.3000   -0.0054
##     80        0.7734             nan     0.3000   -0.0043
##    100        0.7549             nan     0.3000   -0.0046
##    120        0.7343             nan     0.3000   -0.0031
##    140        0.7232             nan     0.3000   -0.0018
##    160        0.7083             nan     0.3000   -0.0038
##    180        0.6950             nan     0.3000   -0.0025
##    200        0.6875             nan     0.3000   -0.0007
##    220        0.6785             nan     0.3000   -0.0023
##    240        0.6705             nan     0.3000   -0.0010
##    260        0.6633             nan     0.3000   -0.0015
##    280        0.6485             nan     0.3000   -0.0022
##    300        0.6380             nan     0.3000   -0.0037
##    320        0.6297             nan     0.3000   -0.0035
##    340        0.6195             nan     0.3000   -0.0021
##    360        0.6117             nan     0.3000   -0.0005
##    380        0.6033             nan     0.3000   -0.0006
##    400        0.5949             nan     0.3000   -0.0028
##    420        0.5900             nan     0.3000   -0.0036
##    440        0.5839             nan     0.3000   -0.0010
##    460        0.5754             nan     0.3000   -0.0041
##    480        0.5707             nan     0.3000   -0.0021
##    500        0.5655             nan     0.3000   -0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2059             nan     0.3000    0.0421
##      2        1.1561             nan     0.3000    0.0194
##      3        1.1093             nan     0.3000    0.0131
##      4        1.0654             nan     0.3000    0.0109
##      5        1.0358             nan     0.3000    0.0142
##      6        1.0110             nan     0.3000    0.0104
##      7        0.9979             nan     0.3000    0.0019
##      8        0.9817             nan     0.3000    0.0050
##      9        0.9570             nan     0.3000    0.0028
##     10        0.9479             nan     0.3000    0.0015
##     20        0.8853             nan     0.3000    0.0015
##     40        0.8314             nan     0.3000   -0.0004
##     60        0.7979             nan     0.3000   -0.0061
##     80        0.7743             nan     0.3000   -0.0033
##    100        0.7580             nan     0.3000   -0.0029
##    120        0.7400             nan     0.3000   -0.0009
##    140        0.7287             nan     0.3000   -0.0042
##    160        0.7131             nan     0.3000   -0.0034
##    180        0.6985             nan     0.3000   -0.0015
##    200        0.6865             nan     0.3000   -0.0010
##    220        0.6804             nan     0.3000   -0.0017
##    240        0.6692             nan     0.3000   -0.0040
##    260        0.6606             nan     0.3000   -0.0050
##    280        0.6502             nan     0.3000   -0.0024
##    300        0.6436             nan     0.3000   -0.0022
##    320        0.6365             nan     0.3000   -0.0026
##    340        0.6238             nan     0.3000   -0.0021
##    360        0.6162             nan     0.3000   -0.0051
##    380        0.6089             nan     0.3000   -0.0031
##    400        0.6033             nan     0.3000   -0.0032
##    420        0.5960             nan     0.3000   -0.0014
##    440        0.5936             nan     0.3000   -0.0020
##    460        0.5844             nan     0.3000   -0.0002
##    480        0.5787             nan     0.3000   -0.0027
##    500        0.5705             nan     0.3000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1852             nan     0.3000    0.0455
##      2        1.1112             nan     0.3000    0.0325
##      3        1.0663             nan     0.3000    0.0085
##      4        1.0264             nan     0.3000    0.0096
##      5        0.9870             nan     0.3000    0.0101
##      6        0.9587             nan     0.3000    0.0111
##      7        0.9456             nan     0.3000    0.0008
##      8        0.9368             nan     0.3000   -0.0080
##      9        0.9271             nan     0.3000   -0.0047
##     10        0.9119             nan     0.3000    0.0011
##     20        0.8400             nan     0.3000   -0.0025
##     40        0.7323             nan     0.3000   -0.0007
##     60        0.6816             nan     0.3000   -0.0077
##     80        0.6560             nan     0.3000   -0.0033
##    100        1.5037             nan     0.3000   -0.0028
##    120        1.4806             nan     0.3000   -0.0027
##    140        1.4549             nan     0.3000   -0.0020
##    160        1.4426             nan     0.3000   -0.0063
##    180        1.4326             nan     0.3000   -0.0021
##    200        1.4187             nan     0.3000   -0.0043
##    220        1.3849             nan     0.3000   -0.0007
##    240        1.3739             nan     0.3000   -0.0006
##    260        1.3669             nan     0.3000   -0.0027
##    280        1.3528             nan     0.3000   -0.0023
##    300        1.3497             nan     0.3000   -0.0005
##    320        1.3431             nan     0.3000   -0.0013
##    340        1.3315             nan     0.3000   -0.0010
##    360        1.3180             nan     0.3000   -0.0008
##    380        1.3158             nan     0.3000   -0.0000
##    400        1.2966             nan     0.3000   -0.0033
##    420        1.2941             nan     0.3000   -0.0031
##    440        1.2909             nan     0.3000   -0.0001
##    460        1.2838             nan     0.3000   -0.0003
##    480        1.2808             nan     0.3000   -0.0019
##    500        1.2792             nan     0.3000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1701             nan     0.3000    0.0583
##      2        1.1008             nan     0.3000    0.0327
##      3        1.0479             nan     0.3000    0.0107
##      4        1.0100             nan     0.3000    0.0093
##      5        0.9846             nan     0.3000    0.0092
##      6        0.9564             nan     0.3000    0.0100
##      7        0.9333             nan     0.3000    0.0087
##      8        0.9251             nan     0.3000   -0.0093
##      9        0.9162             nan     0.3000   -0.0040
##     10        0.9042             nan     0.3000   -0.0010
##     20        0.8228             nan     0.3000   -0.0007
##     40        0.7340             nan     0.3000   -0.0036
##     60        0.6624             nan     0.3000   -0.0022
##     80        0.6044             nan     0.3000   -0.0040
##    100        0.6325             nan     0.3000   -0.0053
##    120        0.5887             nan     0.3000   -0.0063
##    140        0.5557             nan     0.3000   -0.0057
##    160        0.5306             nan     0.3000   -0.0431
##    180 1171456550472.2358             nan     0.3000    0.0001
##    200 1171456550473.5171             nan     0.3000    0.0001
##    220 1171457221375.7810             nan     0.3000   -0.0009
##    240 1171457234185.0596             nan     0.3000    0.0001
##    260           inf             nan     0.3000       nan
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1752             nan     0.3000    0.0473
##      2        1.1059             nan     0.3000    0.0282
##      3        1.0499             nan     0.3000    0.0255
##      4        1.0049             nan     0.3000    0.0130
##      5        0.9746             nan     0.3000    0.0074
##      6        0.9544             nan     0.3000    0.0030
##      7        0.9379             nan     0.3000    0.0010
##      8        0.9261             nan     0.3000   -0.0023
##      9        0.9127             nan     0.3000    0.0030
##     10        0.9015             nan     0.3000   -0.0019
##     20        0.8230             nan     0.3000   -0.0049
##     40        0.7396             nan     0.3000   -0.0052
##     60        0.6944             nan     0.3000   -0.0043
##     80        0.6384             nan     0.3000   -0.0016
##    100        0.6015             nan     0.3000   -0.0010
##    120        0.5650             nan     0.3000   -0.0020
##    140        0.5354             nan     0.3000   -0.0013
##    160        0.4988             nan     0.3000   -0.0024
##    180        0.4682             nan     0.3000   -0.0055
##    200        0.4384             nan     0.3000   -0.0037
##    220        0.4188             nan     0.3000   -0.0036
##    240        0.3925             nan     0.3000   -0.0047
##    260        0.3689             nan     0.3000   -0.0017
##    280        0.3429             nan     0.3000   -0.0014
##    300        0.3253             nan     0.3000   -0.0025
##    320        0.3083             nan     0.3000   -0.0032
##    340        0.2943             nan     0.3000   -0.0005
##    360        0.2799             nan     0.3000   -0.0011
##    380        0.2643             nan     0.3000   -0.0014
##    400        0.2529             nan     0.3000   -0.0017
##    420        0.2397             nan     0.3000   -0.0018
##    440        0.2298             nan     0.3000   -0.0006
##    460        0.2196             nan     0.3000   -0.0024
##    480        0.2072             nan     0.3000   -0.0018
##    500        0.1950             nan     0.3000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1574             nan     0.3000    0.0710
##      2        1.0678             nan     0.3000    0.0379
##      3        1.0106             nan     0.3000    0.0231
##      4        0.9796             nan     0.3000    0.0076
##      5        0.9555             nan     0.3000    0.0048
##      6        0.9340             nan     0.3000    0.0007
##      7        0.9034             nan     0.3000    0.0080
##      8        0.8871             nan     0.3000    0.0036
##      9        0.8668             nan     0.3000    0.0033
##     10        0.8562             nan     0.3000   -0.0030
##     20        0.7773             nan     0.3000   -0.0056
##     40        0.6859             nan     0.3000   -0.0266
##     60        0.5933             nan     0.3000   -0.0020
##     80        0.5039             nan     0.3000    0.0001
##    100        0.4518             nan     0.3000   -0.0017
##    120        0.4117             nan     0.3000   -0.0042
##    140        0.3666             nan     0.3000   -0.0016
##    160        0.3410             nan     0.3000   -0.0042
##    180        0.2997             nan     0.3000   -0.0036
##    200        0.2681             nan     0.3000   -0.0013
##    220        0.2434             nan     0.3000   -0.0011
##    240        0.2164             nan     0.3000   -0.0009
##    260        0.1965             nan     0.3000   -0.0018
##    280        0.1808             nan     0.3000   -0.0009
##    300        0.1632             nan     0.3000   -0.0017
##    320        0.1531             nan     0.3000   -0.0006
##    340        0.1393             nan     0.3000   -0.0006
##    360        0.1283             nan     0.3000   -0.0014
##    380        0.1169             nan     0.3000   -0.0011
##    400        0.1088             nan     0.3000   -0.0010
##    420        0.0976             nan     0.3000   -0.0002
##    440        0.0914             nan     0.3000   -0.0004
##    460        0.0844             nan     0.3000   -0.0003
##    480        0.0776             nan     0.3000   -0.0002
##    500        0.0726             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1461             nan     0.3000    0.0680
##      2        1.0700             nan     0.3000    0.0313
##      3        1.0015             nan     0.3000    0.0141
##      4        0.9621             nan     0.3000    0.0123
##      5        0.9384             nan     0.3000    0.0015
##      6        0.9124             nan     0.3000    0.0064
##      7        0.8910             nan     0.3000   -0.0026
##      8        0.8648             nan     0.3000    0.0035
##      9        0.8555             nan     0.3000   -0.0038
##     10        0.8490             nan     0.3000   -0.0024
##     20        0.7729             nan     0.3000   -0.0067
##     40        0.6477             nan     0.3000   -0.0020
##     60        0.5495             nan     0.3000   -0.0056
##     80        0.4976             nan     0.3000   -0.0038
##    100        0.4357             nan     0.3000   -0.0034
##    120        0.3874             nan     0.3000   -0.0030
##    140        0.3458             nan     0.3000   -0.0016
##    160        0.3108             nan     0.3000   -0.0004
##    180        0.2823             nan     0.3000   -0.0026
##    200        0.2540             nan     0.3000   -0.0039
##    220        0.2263             nan     0.3000   -0.0008
##    240        0.2089             nan     0.3000   -0.0018
##    260        0.1931             nan     0.3000   -0.0019
##    280        0.1751             nan     0.3000   -0.0025
##    300        0.1562             nan     0.3000   -0.0001
##    320        0.1401             nan     0.3000   -0.0010
##    340        0.1274             nan     0.3000   -0.0010
##    360        0.1184             nan     0.3000   -0.0007
##    380        0.1084             nan     0.3000   -0.0008
##    400        0.1009             nan     0.3000   -0.0012
##    420        0.0915             nan     0.3000   -0.0007
##    440        0.0844             nan     0.3000   -0.0006
##    460        0.0787             nan     0.3000   -0.0008
##    480        0.0739             nan     0.3000   -0.0015
##    500        0.0675             nan     0.3000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1512             nan     0.3000    0.0688
##      2        1.0611             nan     0.3000    0.0389
##      3        1.0059             nan     0.3000    0.0244
##      4        0.9664             nan     0.3000    0.0051
##      5        0.9372             nan     0.3000    0.0028
##      6        0.9182             nan     0.3000    0.0015
##      7        0.8990             nan     0.3000    0.0005
##      8        0.8802             nan     0.3000    0.0015
##      9        0.8608             nan     0.3000    0.0066
##     10        0.8444             nan     0.3000    0.0026
##     20        0.7680             nan     0.3000   -0.0045
##     40        0.6492             nan     0.3000   -0.0036
##     60        0.5829             nan     0.3000   -0.0080
##     80        0.5257             nan     0.3000   -0.0034
##    100        0.4490             nan     0.3000   -0.0041
##    120        0.3853             nan     0.3000   -0.0014
##    140        0.3483             nan     0.3000   -0.0031
##    160        0.3128             nan     0.3000   -0.0048
##    180        0.2876             nan     0.3000   -0.0020
##    200        0.2589             nan     0.3000   -0.0029
##    220        0.2334             nan     0.3000   -0.0019
##    240        0.2079             nan     0.3000   -0.0010
##    260        0.1925             nan     0.3000   -0.0010
##    280        0.1741             nan     0.3000   -0.0015
##    300        0.1601             nan     0.3000   -0.0017
##    320        0.1463             nan     0.3000   -0.0015
##    340        0.1333             nan     0.3000   -0.0011
##    360        0.1198             nan     0.3000   -0.0007
##    380        0.1129             nan     0.3000   -0.0013
##    400        0.1047             nan     0.3000   -0.0005
##    420        0.0941             nan     0.3000   -0.0010
##    440        0.0865             nan     0.3000   -0.0009
##    460        0.0791             nan     0.3000   -0.0010
##    480        0.0722             nan     0.3000   -0.0004
##    500        0.0666             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1525             nan     0.5000    0.0649
##      2        1.0875             nan     0.5000    0.0253
##      3        1.0346             nan     0.5000    0.0207
##      4        0.9898             nan     0.5000    0.0199
##      5        0.9755             nan     0.5000   -0.0057
##      6        0.9640             nan     0.5000   -0.0025
##      7        0.9498             nan     0.5000   -0.0042
##      8        0.9338             nan     0.5000   -0.0002
##      9        0.9126             nan     0.5000    0.0081
##     10        0.9117             nan     0.5000   -0.0088
##     20        0.8606             nan     0.5000   -0.0148
##     40        0.8126             nan     0.5000   -0.0137
##     60        0.7747             nan     0.5000   -0.0066
##     80        0.7467             nan     0.5000   -0.0077
##    100        0.7186             nan     0.5000   -0.0019
##    120        0.6875             nan     0.5000   -0.0036
##    140        0.6725             nan     0.5000   -0.0036
##    160        0.7299             nan     0.5000   -0.0068
##    180     1716.2939             nan     0.5000   -0.0011
##    200     1716.2841             nan     0.5000    0.0000
##    220     1716.2901             nan     0.5000   -0.0026
##    240     1716.2782             nan     0.5000    0.0004
##    260     1716.2708             nan     0.5000   -0.0022
##    280     1716.2592             nan     0.5000   -0.0011
##    300     1716.2484             nan     0.5000   -0.0001
##    320     1716.2452             nan     0.5000   -0.0000
##    340     1716.2417             nan     0.5000   -0.0006
##    360     1716.2379             nan     0.5000   -0.0000
##    380     1716.2320             nan     0.5000   -0.0015
##    400     1716.2260             nan     0.5000   -0.0024
##    420     1716.2253             nan     0.5000   -0.0064
##    440     1716.2185             nan     0.5000   -0.0000
##    460     1716.2142             nan     0.5000   -0.0016
##    480     1716.2140             nan     0.5000    0.0010
##    500     1716.2007             nan     0.5000   -0.0049
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1663             nan     0.5000    0.0637
##      2        1.0989             nan     0.5000    0.0306
##      3        1.0627             nan     0.5000    0.0049
##      4        1.0032             nan     0.5000    0.0261
##      5        0.9780             nan     0.5000    0.0080
##      6        0.9450             nan     0.5000    0.0135
##      7        0.9401             nan     0.5000   -0.0021
##      8        0.9348             nan     0.5000   -0.0042
##      9        0.9170             nan     0.5000    0.0076
##     10        0.9186             nan     0.5000   -0.0127
##     20        0.8529             nan     0.5000   -0.0030
##     40        0.7989             nan     0.5000   -0.0060
##     60        0.7473             nan     0.5000   -0.0009
##     80        0.7368             nan     0.5000   -0.0083
##    100        0.7161             nan     0.5000   -0.0054
##    120        0.7016             nan     0.5000   -0.0079
##    140        0.6772             nan     0.5000   -0.0077
##    160        0.6601             nan     0.5000   -0.0043
##    180        0.6508             nan     0.5000   -0.0029
##    200        0.6339             nan     0.5000   -0.0040
##    220        0.6188             nan     0.5000   -0.0062
##    240        0.6079             nan     0.5000   -0.0046
##    260        0.5946             nan     0.5000   -0.0102
##    280        0.5858             nan     0.5000   -0.0055
##    300        0.5796             nan     0.5000   -0.0055
##    320        0.5691             nan     0.5000   -0.0046
##    340        0.5562             nan     0.5000   -0.0008
##    360        0.5420             nan     0.5000   -0.0036
##    380        0.5366             nan     0.5000   -0.0027
##    400        0.5271             nan     0.5000   -0.0070
##    420        0.5184             nan     0.5000   -0.0063
##    440        0.5173             nan     0.5000   -0.0082
##    460        0.5028             nan     0.5000   -0.0032
##    480        0.4939             nan     0.5000   -0.0007
##    500        0.4943             nan     0.5000   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1543             nan     0.5000    0.0589
##      2        1.0898             nan     0.5000    0.0268
##      3        1.0365             nan     0.5000    0.0207
##      4        0.9952             nan     0.5000    0.0061
##      5        0.9765             nan     0.5000   -0.0004
##      6        0.9587             nan     0.5000   -0.0002
##      7        0.9418             nan     0.5000   -0.0026
##      8        0.9233             nan     0.5000    0.0065
##      9        0.9303             nan     0.5000   -0.0158
##     10        0.9219             nan     0.5000    0.0023
##     20        0.8634             nan     0.5000   -0.0021
##     40        0.8012             nan     0.5000   -0.0046
##     60        0.7725             nan     0.5000   -0.0070
##     80        0.7457             nan     0.5000   -0.0056
##    100        0.7187             nan     0.5000   -0.0021
##    120        0.6977             nan     0.5000   -0.0053
##    140        0.6878             nan     0.5000   -0.0084
##    160        0.6602             nan     0.5000   -0.0021
##    180        0.6455             nan     0.5000   -0.0045
##    200        0.6187             nan     0.5000   -0.0034
##    220        0.6088             nan     0.5000   -0.0046
##    240        0.6016             nan     0.5000   -0.0058
##    260        0.5923             nan     0.5000   -0.0015
##    280        0.5837             nan     0.5000   -0.0067
##    300        0.5867             nan     0.5000   -0.0024
##    320        0.5735             nan     0.5000   -0.0060
##    340        0.5625             nan     0.5000   -0.0021
##    360        0.5550             nan     0.5000   -0.0034
##    380        0.5472             nan     0.5000   -0.0038
##    400        0.5368             nan     0.5000   -0.0011
##    420        0.5299             nan     0.5000   -0.0071
##    440        0.5230             nan     0.5000   -0.0041
##    460        0.5240             nan     0.5000   -0.0106
##    480        0.5086             nan     0.5000   -0.0051
##    500        0.4938             nan     0.5000   -0.0041
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1295             nan     0.5000    0.0840
##      2        1.0371             nan     0.5000    0.0361
##      3        0.9830             nan     0.5000    0.0176
##      4        0.9487             nan     0.5000    0.0028
##      5        0.9214             nan     0.5000   -0.0043
##      6        0.8987             nan     0.5000    0.0060
##      7        0.8876             nan     0.5000   -0.0035
##      8        0.8755             nan     0.5000   -0.0058
##      9        0.8751             nan     0.5000   -0.0114
##     10        0.8656             nan     0.5000   -0.0040
##     20        0.7769             nan     0.5000   -0.0117
##     40        0.7044             nan     0.5000   -0.0159
##     60        0.6295             nan     0.5000   -0.0124
##     80        0.5649             nan     0.5000   -0.0085
##    100        0.4889             nan     0.5000   -0.0048
##    120        0.4411             nan     0.5000   -0.0071
##    140        0.3963             nan     0.5000   -0.0048
##    160        0.3469             nan     0.5000   -0.0030
##    180        0.3191             nan     0.5000   -0.0040
##    200        0.2952             nan     0.5000   -0.0006
##    220        0.2835             nan     0.5000   -0.0074
##    240        0.2468             nan     0.5000   -0.0029
##    260        0.2269             nan     0.5000   -0.0027
##    280        0.1978             nan     0.5000   -0.0019
##    300        0.1817             nan     0.5000   -0.0015
##    320        0.1636             nan     0.5000   -0.0014
##    340        0.1473             nan     0.5000   -0.0013
##    360        0.1338             nan     0.5000   -0.0010
##    380        0.1221             nan     0.5000   -0.0004
##    400        0.1099             nan     0.5000   -0.0028
##    420        0.1012             nan     0.5000   -0.0003
##    440        0.0952             nan     0.5000   -0.0013
##    460        0.0899             nan     0.5000   -0.0012
##    480        0.0849             nan     0.5000   -0.0012
##    500        0.0782             nan     0.5000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1207             nan     0.5000    0.0748
##      2        1.0155             nan     0.5000    0.0349
##      3        0.9816             nan     0.5000    0.0079
##      4        0.9573             nan     0.5000    0.0020
##      5        0.9348             nan     0.5000    0.0067
##      6        0.9093             nan     0.5000    0.0044
##      7        0.8896             nan     0.5000   -0.0021
##      8        0.8720             nan     0.5000   -0.0050
##      9        0.8616             nan     0.5000   -0.0029
##     10        0.8452             nan     0.5000    0.0011
##     20        0.7620             nan     0.5000   -0.0048
##     40        0.6820             nan     0.5000   -0.0128
##     60        0.6016             nan     0.5000   -0.0060
##     80        0.5633             nan     0.5000   -0.0082
##    100        0.5151             nan     0.5000   -0.0038
##    120        0.4646             nan     0.5000   -0.0109
##    140        0.4209             nan     0.5000   -0.0092
##    160        0.3780             nan     0.5000   -0.0084
##    180        0.3378             nan     0.5000   -0.0002
##    200        0.3243             nan     0.5000   -0.0019
##    220        0.2979             nan     0.5000   -0.0100
##    240        0.2622             nan     0.5000   -0.0028
##    260        0.2395             nan     0.5000   -0.0065
##    280        0.2207             nan     0.5000   -0.0040
##    300        0.2006             nan     0.5000   -0.0016
##    320        0.1813             nan     0.5000   -0.0025
##    340        0.1681             nan     0.5000   -0.0024
##    360        0.1586             nan     0.5000    0.0001
##    380        0.1472             nan     0.5000   -0.0009
##    400        0.1363             nan     0.5000   -0.0038
##    420        0.1231             nan     0.5000   -0.0013
##    440        0.1174             nan     0.5000   -0.0042
##    460        0.1050             nan     0.5000   -0.0010
##    480        0.0960             nan     0.5000   -0.0001
##    500        0.0905             nan     0.5000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1093             nan     0.5000    0.0706
##      2        1.0138             nan     0.5000    0.0437
##      3        0.9690             nan     0.5000    0.0140
##      4        0.9372             nan     0.5000    0.0100
##      5        0.9144             nan     0.5000    0.0077
##      6        0.8957             nan     0.5000    0.0012
##      7        0.8741             nan     0.5000   -0.0038
##      8        0.8613             nan     0.5000   -0.0031
##      9        0.8506             nan     0.5000   -0.0036
##     10        0.8401             nan     0.5000   -0.0045
##     20        0.7748             nan     0.5000   -0.0020
##     40        0.6877             nan     0.5000   -0.0093
##     60        0.6360             nan     0.5000   -0.0187
##     80        0.5677             nan     0.5000   -0.0032
##    100        0.5203             nan     0.5000   -0.0079
##    120        0.4711             nan     0.5000   -0.0099
##    140        0.4194             nan     0.5000   -0.0027
##    160        0.3960             nan     0.5000   -0.0041
##    180        0.3549             nan     0.5000   -0.0084
##    200        0.3240             nan     0.5000   -0.0034
##    220        0.6802             nan     0.5000   -0.2890
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0763             nan     0.5000    0.0994
##      2        0.9961             nan     0.5000    0.0292
##      3        0.9262             nan     0.5000    0.0261
##      4        0.9056             nan     0.5000   -0.0033
##      5        0.8717             nan     0.5000    0.0055
##      6        0.8608             nan     0.5000   -0.0064
##      7        0.8375             nan     0.5000   -0.0024
##      8        0.8237             nan     0.5000   -0.0126
##      9        0.8121             nan     0.5000   -0.0074
##     10        0.8055             nan     0.5000   -0.0180
##     20        0.7026             nan     0.5000   -0.0084
##     40        0.5635             nan     0.5000   -0.0046
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1334             nan     0.5000    0.0607
##      2        1.0117             nan     0.5000    0.0526
##      3        0.9480             nan     0.5000    0.0202
##      4        0.9122             nan     0.5000    0.0071
##      5        0.8869             nan     0.5000   -0.0072
##      6        0.8641             nan     0.5000   -0.0085
##      7        0.8422             nan     0.5000   -0.0042
##      8        0.8230             nan     0.5000   -0.0093
##      9        0.8088             nan     0.5000   -0.0055
##     10        0.7955             nan     0.5000   -0.0099
##     20        0.7025             nan     0.5000   -0.0166
##     40        0.5545             nan     0.5000   -0.0093
##     60        0.4491             nan     0.5000   -0.0020
##     80        0.3908             nan     0.5000   -0.0088
##    100        0.3030             nan     0.5000   -0.0061
##    120        0.2542             nan     0.5000   -0.0066
##    140        0.2175             nan     0.5000   -0.0038
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0768             nan     0.5000    0.0945
##      2        0.9682             nan     0.5000    0.0416
##      3        0.9184             nan     0.5000    0.0178
##      4        0.8762             nan     0.5000    0.0050
##      5        0.8730             nan     0.5000   -0.0137
##      6        0.8634             nan     0.5000   -0.0170
##      7        0.8466             nan     0.5000   -0.0150
##      8        0.8433             nan     0.5000   -0.0216
##      9        0.8238             nan     0.5000   -0.0082
##     10        0.8019             nan     0.5000   -0.0057
##     20        0.6814             nan     0.5000   -0.0089
##     40        4.1041             nan     0.5000   -0.0107
##     60        3.9566             nan     0.5000   -0.0179
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1391             nan     1.0000    0.0372
##      2        1.0529             nan     1.0000    0.0151
##      3        1.0032             nan     1.0000    0.0222
##      4        0.9934             nan     1.0000   -0.0208
##      5        1.0069             nan     1.0000   -0.0294
##      6        0.9609             nan     1.0000    0.0058
##      7        0.9346             nan     1.0000    0.0124
##      8        0.9308             nan     1.0000   -0.0077
##      9        0.9249             nan     1.0000   -0.0096
##     10        0.9369             nan     1.0000   -0.0303
##     20        0.8722             nan     1.0000   -0.0064
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1279             nan     1.0000    0.0464
##      2        1.0953             nan     1.0000   -0.0026
##      3        1.0060             nan     1.0000    0.0363
##      4        0.9876             nan     1.0000   -0.0055
##      5        0.9605             nan     1.0000    0.0077
##      6        0.9465             nan     1.0000   -0.0086
##      7        0.9488             nan     1.0000   -0.0117
##      8        0.9429             nan     1.0000   -0.0047
##      9        0.9253             nan     1.0000   -0.0034
##     10        0.9394             nan     1.0000   -0.0217
##     20        0.9342             nan     1.0000   -0.0318
##     40        0.8437             nan     1.0000   -0.0063
##     60        0.8421             nan     1.0000   -0.0170
##     80        0.8354             nan     1.0000   -0.0282
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1181             nan     1.0000    0.0592
##      2        1.0403             nan     1.0000    0.0262
##      3        1.0009             nan     1.0000   -0.0306
##      4        0.9831             nan     1.0000   -0.0038
##      5        0.9793             nan     1.0000   -0.0248
##      6        0.9637             nan     1.0000   -0.0228
##      7        0.9235             nan     1.0000    0.0037
##      8        0.9070             nan     1.0000    0.0063
##      9        0.9229             nan     1.0000   -0.0357
##     10        0.9401             nan     1.0000   -0.0307
##     20        0.8856             nan     1.0000   -0.0134
##     40        0.9057             nan     1.0000   -0.0086
##     60        3.5014             nan     1.0000   -0.0015
##     80        3.4953             nan     1.0000   -0.0112
##    100        3.5007             nan     1.0000   -0.0036
##    120        3.4778             nan     1.0000    0.0000
##    140        3.4688             nan     1.0000   -0.0016
##    160        3.4740             nan     1.0000   -0.0045
##    180        3.4651             nan     1.0000   -0.0000
##    200        3.4570             nan     1.0000   -0.0008
##    220        3.4557             nan     1.0000   -0.0019
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0799             nan     1.0000    0.0775
##      2        0.9895             nan     1.0000    0.0383
##      3        0.9361             nan     1.0000    0.0127
##      4        0.9197             nan     1.0000   -0.0206
##      5        0.9065             nan     1.0000   -0.0283
##      6        0.8929             nan     1.0000   -0.0137
##      7        0.9096             nan     1.0000   -0.0428
##      8        0.9614             nan     1.0000   -0.0780
##      9        0.9407             nan     1.0000   -0.0337
##     10        0.9539             nan     1.0000   -0.0395
##     20        4.6669             nan     1.0000   -0.0207
##     40        5.4855             nan     1.0000   -0.0116
##     60       12.2630             nan     1.0000   -3.2427
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0430             nan     1.0000    0.1295
##      2        0.9864             nan     1.0000    0.0111
##      3        0.9641             nan     1.0000   -0.0370
##      4        0.9633             nan     1.0000   -0.0319
##      5        1.0178             nan     1.0000   -0.0800
##      6        0.9808             nan     1.0000   -0.0083
##      7        0.9601             nan     1.0000   -0.0237
##      8        0.9719             nan     1.0000   -0.0508
##      9        0.9539             nan     1.0000   -0.0278
##     10        0.9553             nan     1.0000   -0.0237
##     20        1.9161             nan     1.0000   -0.0418
##     40        2.2259             nan     1.0000   -0.0343
##     60     1255.6231             nan     1.0000   -0.0530
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0508             nan     1.0000    0.0957
##      2        0.9677             nan     1.0000    0.0354
##      3        0.9898             nan     1.0000   -0.0567
##      4        0.9887             nan     1.0000   -0.0401
##      5        0.9769             nan     1.0000   -0.0181
##      6        0.9718             nan     1.0000   -0.0234
##      7        1.8630             nan     1.0000   -0.4881
##      8        1.8485             nan     1.0000   -0.0045
##      9        1.8691             nan     1.0000   -0.0628
##     10        1.8677             nan     1.0000   -0.0500
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0189             nan     1.0000    0.1245
##      2        0.9796             nan     1.0000   -0.0235
##      3        0.9873             nan     1.0000   -0.0587
##      4        0.9764             nan     1.0000   -0.0579
##      5        2.0290             nan     1.0000   -1.1181
##      6        2.0455             nan     1.0000   -0.0609
##      7        2.0602             nan     1.0000   -0.0637
##      8        2.0396             nan     1.0000   -0.0319
##      9        2.0241             nan     1.0000   -0.0017
##     10        1.9838             nan     1.0000   -0.0189
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0523             nan     1.0000    0.0841
##      2        0.9418             nan     1.0000    0.0341
##      3        0.9254             nan     1.0000   -0.0332
##      4        0.9628             nan     1.0000   -0.0880
##      5        0.9401             nan     1.0000   -0.0114
##      6        0.9196             nan     1.0000   -0.0155
##      7        0.9006             nan     1.0000   -0.0063
##      8        0.8983             nan     1.0000   -0.0492
##      9        0.9317             nan     1.0000   -0.0787
##     10        0.9098             nan     1.0000   -0.0318
##     20        1.0648             nan     1.0000   -0.0513
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        0.9982             nan     1.0000    0.1173
##      2        0.9378             nan     1.0000    0.0118
##      3        0.9120             nan     1.0000   -0.0129
##      4        0.9080             nan     1.0000   -0.0406
##      5        0.9083             nan     1.0000   -0.0420
##      6        0.8711             nan     1.0000   -0.0169
##      7        0.8772             nan     1.0000   -0.0569
##      8        0.8534             nan     1.0000   -0.0133
##      9        0.8425             nan     1.0000   -0.0462
##     10        0.8741             nan     1.0000   -0.0688
##     20        0.9207             nan     1.0000   -0.0179
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2921             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2895             nan     0.0010    0.0002
##     20        1.2858             nan     0.0010    0.0002
##     40        1.2784             nan     0.0010    0.0002
##     60        1.2714             nan     0.0010    0.0002
##     80        1.2646             nan     0.0010    0.0001
##    100        1.2582             nan     0.0010    0.0001
##    120        1.2520             nan     0.0010    0.0001
##    140        1.2458             nan     0.0010    0.0001
##    160        1.2400             nan     0.0010    0.0001
##    180        1.2346             nan     0.0010    0.0001
##    200        1.2294             nan     0.0010    0.0001
##    220        1.2240             nan     0.0010    0.0001
##    240        1.2188             nan     0.0010    0.0001
##    260        1.2140             nan     0.0010    0.0001
##    280        1.2092             nan     0.0010    0.0001
##    300        1.2045             nan     0.0010    0.0001
##    320        1.1997             nan     0.0010    0.0001
##    340        1.1954             nan     0.0010    0.0001
##    360        1.1911             nan     0.0010    0.0001
##    380        1.1869             nan     0.0010    0.0001
##    400        1.1827             nan     0.0010    0.0001
##    420        1.1788             nan     0.0010    0.0001
##    440        1.1750             nan     0.0010    0.0001
##    460        1.1711             nan     0.0010    0.0001
##    480        1.1672             nan     0.0010    0.0001
##    500        1.1636             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0001
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2895             nan     0.0010    0.0002
##     20        1.2857             nan     0.0010    0.0002
##     40        1.2784             nan     0.0010    0.0002
##     60        1.2715             nan     0.0010    0.0002
##     80        1.2647             nan     0.0010    0.0001
##    100        1.2583             nan     0.0010    0.0001
##    120        1.2518             nan     0.0010    0.0001
##    140        1.2459             nan     0.0010    0.0001
##    160        1.2401             nan     0.0010    0.0001
##    180        1.2347             nan     0.0010    0.0001
##    200        1.2292             nan     0.0010    0.0001
##    220        1.2240             nan     0.0010    0.0001
##    240        1.2189             nan     0.0010    0.0001
##    260        1.2139             nan     0.0010    0.0001
##    280        1.2090             nan     0.0010    0.0001
##    300        1.2040             nan     0.0010    0.0001
##    320        1.1993             nan     0.0010    0.0001
##    340        1.1949             nan     0.0010    0.0001
##    360        1.1905             nan     0.0010    0.0001
##    380        1.1861             nan     0.0010    0.0001
##    400        1.1819             nan     0.0010    0.0001
##    420        1.1779             nan     0.0010    0.0001
##    440        1.1741             nan     0.0010    0.0001
##    460        1.1702             nan     0.0010    0.0001
##    480        1.1665             nan     0.0010    0.0001
##    500        1.1628             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2921             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2906             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2895             nan     0.0010    0.0001
##     20        1.2858             nan     0.0010    0.0002
##     40        1.2787             nan     0.0010    0.0002
##     60        1.2718             nan     0.0010    0.0002
##     80        1.2650             nan     0.0010    0.0002
##    100        1.2584             nan     0.0010    0.0001
##    120        1.2520             nan     0.0010    0.0001
##    140        1.2460             nan     0.0010    0.0002
##    160        1.2401             nan     0.0010    0.0001
##    180        1.2345             nan     0.0010    0.0001
##    200        1.2288             nan     0.0010    0.0001
##    220        1.2236             nan     0.0010    0.0001
##    240        1.2183             nan     0.0010    0.0001
##    260        1.2135             nan     0.0010    0.0001
##    280        1.2087             nan     0.0010    0.0001
##    300        1.2040             nan     0.0010    0.0001
##    320        1.1995             nan     0.0010    0.0001
##    340        1.1951             nan     0.0010    0.0001
##    360        1.1908             nan     0.0010    0.0001
##    380        1.1867             nan     0.0010    0.0001
##    400        1.1826             nan     0.0010    0.0001
##    420        1.1785             nan     0.0010    0.0001
##    440        1.1746             nan     0.0010    0.0001
##    460        1.1708             nan     0.0010    0.0001
##    480        1.1669             nan     0.0010    0.0001
##    500        1.1633             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2903             nan     0.0010    0.0002
##      7        1.2898             nan     0.0010    0.0002
##      8        1.2893             nan     0.0010    0.0002
##      9        1.2888             nan     0.0010    0.0002
##     10        1.2884             nan     0.0010    0.0002
##     20        1.2836             nan     0.0010    0.0002
##     40        1.2745             nan     0.0010    0.0002
##     60        1.2655             nan     0.0010    0.0002
##     80        1.2567             nan     0.0010    0.0002
##    100        1.2484             nan     0.0010    0.0002
##    120        1.2402             nan     0.0010    0.0002
##    140        1.2322             nan     0.0010    0.0002
##    160        1.2244             nan     0.0010    0.0002
##    180        1.2170             nan     0.0010    0.0002
##    200        1.2095             nan     0.0010    0.0001
##    220        1.2026             nan     0.0010    0.0001
##    240        1.1957             nan     0.0010    0.0001
##    260        1.1888             nan     0.0010    0.0001
##    280        1.1824             nan     0.0010    0.0001
##    300        1.1761             nan     0.0010    0.0001
##    320        1.1700             nan     0.0010    0.0001
##    340        1.1639             nan     0.0010    0.0001
##    360        1.1580             nan     0.0010    0.0001
##    380        1.1524             nan     0.0010    0.0001
##    400        1.1468             nan     0.0010    0.0001
##    420        1.1414             nan     0.0010    0.0001
##    440        1.1363             nan     0.0010    0.0001
##    460        1.1312             nan     0.0010    0.0001
##    480        1.1263             nan     0.0010    0.0001
##    500        1.1215             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2889             nan     0.0010    0.0002
##     10        1.2884             nan     0.0010    0.0002
##     20        1.2834             nan     0.0010    0.0002
##     40        1.2743             nan     0.0010    0.0002
##     60        1.2653             nan     0.0010    0.0002
##     80        1.2566             nan     0.0010    0.0002
##    100        1.2481             nan     0.0010    0.0002
##    120        1.2398             nan     0.0010    0.0002
##    140        1.2319             nan     0.0010    0.0002
##    160        1.2244             nan     0.0010    0.0002
##    180        1.2171             nan     0.0010    0.0002
##    200        1.2095             nan     0.0010    0.0002
##    220        1.2025             nan     0.0010    0.0001
##    240        1.1957             nan     0.0010    0.0002
##    260        1.1892             nan     0.0010    0.0001
##    280        1.1827             nan     0.0010    0.0001
##    300        1.1765             nan     0.0010    0.0001
##    320        1.1702             nan     0.0010    0.0001
##    340        1.1641             nan     0.0010    0.0001
##    360        1.1584             nan     0.0010    0.0001
##    380        1.1528             nan     0.0010    0.0001
##    400        1.1473             nan     0.0010    0.0001
##    420        1.1418             nan     0.0010    0.0001
##    440        1.1366             nan     0.0010    0.0001
##    460        1.1315             nan     0.0010    0.0001
##    480        1.1264             nan     0.0010    0.0001
##    500        1.1216             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2837             nan     0.0010    0.0002
##     40        1.2744             nan     0.0010    0.0002
##     60        1.2653             nan     0.0010    0.0002
##     80        1.2566             nan     0.0010    0.0002
##    100        1.2480             nan     0.0010    0.0002
##    120        1.2397             nan     0.0010    0.0002
##    140        1.2319             nan     0.0010    0.0002
##    160        1.2241             nan     0.0010    0.0002
##    180        1.2167             nan     0.0010    0.0002
##    200        1.2094             nan     0.0010    0.0002
##    220        1.2023             nan     0.0010    0.0001
##    240        1.1955             nan     0.0010    0.0001
##    260        1.1887             nan     0.0010    0.0002
##    280        1.1824             nan     0.0010    0.0002
##    300        1.1759             nan     0.0010    0.0001
##    320        1.1700             nan     0.0010    0.0001
##    340        1.1641             nan     0.0010    0.0001
##    360        1.1582             nan     0.0010    0.0001
##    380        1.1526             nan     0.0010    0.0001
##    400        1.1472             nan     0.0010    0.0001
##    420        1.1420             nan     0.0010    0.0001
##    440        1.1367             nan     0.0010    0.0001
##    460        1.1317             nan     0.0010    0.0001
##    480        1.1268             nan     0.0010    0.0001
##    500        1.1219             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0003
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2890             nan     0.0010    0.0003
##      9        1.2885             nan     0.0010    0.0002
##     10        1.2880             nan     0.0010    0.0002
##     20        1.2827             nan     0.0010    0.0002
##     40        1.2721             nan     0.0010    0.0002
##     60        1.2617             nan     0.0010    0.0002
##     80        1.2518             nan     0.0010    0.0002
##    100        1.2421             nan     0.0010    0.0002
##    120        1.2328             nan     0.0010    0.0002
##    140        1.2238             nan     0.0010    0.0002
##    160        1.2151             nan     0.0010    0.0002
##    180        1.2067             nan     0.0010    0.0002
##    200        1.1983             nan     0.0010    0.0002
##    220        1.1900             nan     0.0010    0.0002
##    240        1.1824             nan     0.0010    0.0002
##    260        1.1748             nan     0.0010    0.0002
##    280        1.1675             nan     0.0010    0.0001
##    300        1.1602             nan     0.0010    0.0001
##    320        1.1529             nan     0.0010    0.0002
##    340        1.1461             nan     0.0010    0.0001
##    360        1.1392             nan     0.0010    0.0001
##    380        1.1330             nan     0.0010    0.0001
##    400        1.1267             nan     0.0010    0.0002
##    420        1.1204             nan     0.0010    0.0001
##    440        1.1145             nan     0.0010    0.0001
##    460        1.1086             nan     0.0010    0.0001
##    480        1.1031             nan     0.0010    0.0001
##    500        1.0977             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2905             nan     0.0010    0.0003
##      6        1.2899             nan     0.0010    0.0003
##      7        1.2893             nan     0.0010    0.0003
##      8        1.2888             nan     0.0010    0.0002
##      9        1.2883             nan     0.0010    0.0002
##     10        1.2877             nan     0.0010    0.0002
##     20        1.2820             nan     0.0010    0.0003
##     40        1.2710             nan     0.0010    0.0002
##     60        1.2607             nan     0.0010    0.0002
##     80        1.2508             nan     0.0010    0.0002
##    100        1.2413             nan     0.0010    0.0002
##    120        1.2318             nan     0.0010    0.0002
##    140        1.2228             nan     0.0010    0.0002
##    160        1.2140             nan     0.0010    0.0002
##    180        1.2053             nan     0.0010    0.0001
##    200        1.1968             nan     0.0010    0.0002
##    220        1.1888             nan     0.0010    0.0002
##    240        1.1809             nan     0.0010    0.0002
##    260        1.1733             nan     0.0010    0.0002
##    280        1.1657             nan     0.0010    0.0002
##    300        1.1587             nan     0.0010    0.0001
##    320        1.1518             nan     0.0010    0.0001
##    340        1.1452             nan     0.0010    0.0001
##    360        1.1386             nan     0.0010    0.0001
##    380        1.1322             nan     0.0010    0.0001
##    400        1.1260             nan     0.0010    0.0001
##    420        1.1198             nan     0.0010    0.0001
##    440        1.1138             nan     0.0010    0.0001
##    460        1.1083             nan     0.0010    0.0001
##    480        1.1027             nan     0.0010    0.0001
##    500        1.0973             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2895             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0002
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2824             nan     0.0010    0.0002
##     40        1.2714             nan     0.0010    0.0002
##     60        1.2612             nan     0.0010    0.0002
##     80        1.2513             nan     0.0010    0.0002
##    100        1.2417             nan     0.0010    0.0002
##    120        1.2324             nan     0.0010    0.0002
##    140        1.2232             nan     0.0010    0.0002
##    160        1.2144             nan     0.0010    0.0002
##    180        1.2059             nan     0.0010    0.0002
##    200        1.1975             nan     0.0010    0.0002
##    220        1.1895             nan     0.0010    0.0002
##    240        1.1816             nan     0.0010    0.0002
##    260        1.1737             nan     0.0010    0.0001
##    280        1.1662             nan     0.0010    0.0001
##    300        1.1591             nan     0.0010    0.0001
##    320        1.1523             nan     0.0010    0.0001
##    340        1.1455             nan     0.0010    0.0001
##    360        1.1392             nan     0.0010    0.0002
##    380        1.1326             nan     0.0010    0.0001
##    400        1.1264             nan     0.0010    0.0001
##    420        1.1203             nan     0.0010    0.0001
##    440        1.1144             nan     0.0010    0.0001
##    460        1.1085             nan     0.0010    0.0001
##    480        1.1027             nan     0.0010    0.0001
##    500        1.0972             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2577             nan     0.1000    0.0159
##      2        1.2290             nan     0.1000    0.0136
##      3        1.1998             nan     0.1000    0.0110
##      4        1.1799             nan     0.1000    0.0085
##      5        1.1625             nan     0.1000    0.0079
##      6        1.1460             nan     0.1000    0.0074
##      7        1.1297             nan     0.1000    0.0077
##      8        1.1143             nan     0.1000    0.0067
##      9        1.1026             nan     0.1000    0.0055
##     10        1.0907             nan     0.1000    0.0044
##     20        1.0058             nan     0.1000    0.0011
##     40        0.9209             nan     0.1000    0.0000
##     60        0.8771             nan     0.1000   -0.0002
##     80        0.8507             nan     0.1000   -0.0009
##    100        0.8315             nan     0.1000   -0.0005
##    120        0.8187             nan     0.1000   -0.0005
##    140        0.8048             nan     0.1000   -0.0003
##    160        0.7952             nan     0.1000   -0.0021
##    180        0.7886             nan     0.1000   -0.0006
##    200        0.7815             nan     0.1000   -0.0007
##    220        0.7743             nan     0.1000   -0.0016
##    240        0.7673             nan     0.1000   -0.0012
##    260        0.7598             nan     0.1000   -0.0011
##    280        0.7525             nan     0.1000   -0.0005
##    300        0.7454             nan     0.1000   -0.0009
##    320        0.7399             nan     0.1000   -0.0008
##    340        0.7331             nan     0.1000   -0.0009
##    360        0.7264             nan     0.1000   -0.0005
##    380        0.7228             nan     0.1000   -0.0010
##    400        0.7179             nan     0.1000   -0.0005
##    420        0.7125             nan     0.1000   -0.0010
##    440        0.7069             nan     0.1000   -0.0004
##    460        0.7019             nan     0.1000   -0.0012
##    480        0.6970             nan     0.1000   -0.0009
##    500        0.6934             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2579             nan     0.1000    0.0164
##      2        1.2245             nan     0.1000    0.0121
##      3        1.2008             nan     0.1000    0.0099
##      4        1.1756             nan     0.1000    0.0100
##      5        1.1581             nan     0.1000    0.0067
##      6        1.1411             nan     0.1000    0.0052
##      7        1.1253             nan     0.1000    0.0048
##      8        1.1102             nan     0.1000    0.0057
##      9        1.0965             nan     0.1000    0.0046
##     10        1.0828             nan     0.1000    0.0062
##     20        0.9959             nan     0.1000    0.0011
##     40        0.9196             nan     0.1000   -0.0007
##     60        0.8761             nan     0.1000   -0.0002
##     80        0.8481             nan     0.1000   -0.0004
##    100        0.8288             nan     0.1000   -0.0012
##    120        0.8145             nan     0.1000   -0.0014
##    140        0.8025             nan     0.1000   -0.0009
##    160        0.7926             nan     0.1000   -0.0005
##    180        0.7862             nan     0.1000   -0.0007
##    200        0.7783             nan     0.1000   -0.0011
##    220        0.7690             nan     0.1000   -0.0003
##    240        0.7620             nan     0.1000   -0.0008
##    260        0.7563             nan     0.1000   -0.0015
##    280        0.7489             nan     0.1000   -0.0003
##    300        0.7440             nan     0.1000   -0.0008
##    320        0.7390             nan     0.1000   -0.0010
##    340        0.7338             nan     0.1000   -0.0017
##    360        0.7271             nan     0.1000   -0.0010
##    380        0.7238             nan     0.1000   -0.0005
##    400        0.7182             nan     0.1000   -0.0011
##    420        0.7155             nan     0.1000   -0.0002
##    440        0.7115             nan     0.1000   -0.0005
##    460        0.7065             nan     0.1000   -0.0005
##    480        0.7033             nan     0.1000   -0.0004
##    500        0.6982             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2599             nan     0.1000    0.0148
##      2        1.2273             nan     0.1000    0.0163
##      3        1.2031             nan     0.1000    0.0113
##      4        1.1823             nan     0.1000    0.0110
##      5        1.1626             nan     0.1000    0.0085
##      6        1.1444             nan     0.1000    0.0079
##      7        1.1284             nan     0.1000    0.0061
##      8        1.1137             nan     0.1000    0.0062
##      9        1.0989             nan     0.1000    0.0067
##     10        1.0870             nan     0.1000    0.0046
##     20        0.9948             nan     0.1000    0.0015
##     40        0.9211             nan     0.1000    0.0014
##     60        0.8805             nan     0.1000   -0.0006
##     80        0.8539             nan     0.1000   -0.0005
##    100        0.8360             nan     0.1000   -0.0007
##    120        0.8246             nan     0.1000   -0.0009
##    140        0.8148             nan     0.1000   -0.0023
##    160        0.8024             nan     0.1000   -0.0004
##    180        0.7928             nan     0.1000   -0.0007
##    200        0.7849             nan     0.1000   -0.0002
##    220        0.7754             nan     0.1000    0.0000
##    240        0.7685             nan     0.1000   -0.0011
##    260        0.7615             nan     0.1000   -0.0013
##    280        0.7554             nan     0.1000   -0.0010
##    300        0.7503             nan     0.1000   -0.0017
##    320        0.7449             nan     0.1000   -0.0010
##    340        0.7386             nan     0.1000   -0.0007
##    360        0.7306             nan     0.1000   -0.0010
##    380        0.7256             nan     0.1000   -0.0018
##    400        0.7224             nan     0.1000   -0.0009
##    420        0.7179             nan     0.1000   -0.0008
##    440        0.7137             nan     0.1000   -0.0012
##    460        0.7088             nan     0.1000   -0.0003
##    480        0.7049             nan     0.1000   -0.0010
##    500        0.7022             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2457             nan     0.1000    0.0204
##      2        1.2046             nan     0.1000    0.0153
##      3        1.1690             nan     0.1000    0.0147
##      4        1.1418             nan     0.1000    0.0116
##      5        1.1200             nan     0.1000    0.0094
##      6        1.1000             nan     0.1000    0.0087
##      7        1.0834             nan     0.1000    0.0047
##      8        1.0643             nan     0.1000    0.0063
##      9        1.0473             nan     0.1000    0.0066
##     10        1.0285             nan     0.1000    0.0078
##     20        0.9387             nan     0.1000    0.0000
##     40        0.8479             nan     0.1000   -0.0008
##     60        0.8046             nan     0.1000   -0.0008
##     80        0.7664             nan     0.1000   -0.0014
##    100        0.7401             nan     0.1000   -0.0007
##    120        0.7155             nan     0.1000   -0.0026
##    140        0.6955             nan     0.1000   -0.0007
##    160        0.6735             nan     0.1000   -0.0015
##    180        0.6542             nan     0.1000   -0.0011
##    200        0.6340             nan     0.1000   -0.0011
##    220        0.6135             nan     0.1000   -0.0002
##    240        0.5964             nan     0.1000   -0.0014
##    260        0.5806             nan     0.1000   -0.0012
##    280        0.5653             nan     0.1000   -0.0015
##    300        0.5527             nan     0.1000   -0.0011
##    320        0.5390             nan     0.1000   -0.0007
##    340        0.5247             nan     0.1000    0.0005
##    360        0.5107             nan     0.1000   -0.0005
##    380        0.4994             nan     0.1000   -0.0005
##    400        0.4854             nan     0.1000   -0.0008
##    420        0.4759             nan     0.1000   -0.0004
##    440        0.4661             nan     0.1000   -0.0010
##    460        0.4543             nan     0.1000   -0.0007
##    480        0.4433             nan     0.1000   -0.0010
##    500        0.4339             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2449             nan     0.1000    0.0226
##      2        1.2047             nan     0.1000    0.0167
##      3        1.1706             nan     0.1000    0.0160
##      4        1.1417             nan     0.1000    0.0130
##      5        1.1193             nan     0.1000    0.0066
##      6        1.0988             nan     0.1000    0.0067
##      7        1.0763             nan     0.1000    0.0082
##      8        1.0598             nan     0.1000    0.0067
##      9        1.0421             nan     0.1000    0.0086
##     10        1.0280             nan     0.1000    0.0038
##     20        0.9340             nan     0.1000    0.0024
##     40        0.8464             nan     0.1000   -0.0001
##     60        0.7960             nan     0.1000   -0.0033
##     80        0.7628             nan     0.1000   -0.0014
##    100        0.7336             nan     0.1000   -0.0018
##    120        0.7056             nan     0.1000   -0.0003
##    140        0.6830             nan     0.1000   -0.0027
##    160        0.6628             nan     0.1000   -0.0001
##    180        0.6437             nan     0.1000   -0.0002
##    200        0.6241             nan     0.1000   -0.0008
##    220        0.6094             nan     0.1000   -0.0008
##    240        0.5952             nan     0.1000   -0.0014
##    260        0.5799             nan     0.1000   -0.0010
##    280        0.5632             nan     0.1000   -0.0011
##    300        0.5501             nan     0.1000   -0.0014
##    320        0.5338             nan     0.1000   -0.0005
##    340        0.5176             nan     0.1000   -0.0011
##    360        0.5050             nan     0.1000   -0.0006
##    380        0.4918             nan     0.1000   -0.0046
##    400        0.4798             nan     0.1000   -0.0012
##    420        0.4683             nan     0.1000   -0.0005
##    440        0.4582             nan     0.1000   -0.0012
##    460        0.4514             nan     0.1000   -0.0015
##    480        0.4419             nan     0.1000   -0.0008
##    500        0.4313             nan     0.1000    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2466             nan     0.1000    0.0201
##      2        1.2135             nan     0.1000    0.0164
##      3        1.1821             nan     0.1000    0.0155
##      4        1.1481             nan     0.1000    0.0151
##      5        1.1246             nan     0.1000    0.0114
##      6        1.1023             nan     0.1000    0.0079
##      7        1.0835             nan     0.1000    0.0087
##      8        1.0656             nan     0.1000    0.0075
##      9        1.0444             nan     0.1000    0.0069
##     10        1.0288             nan     0.1000    0.0057
##     20        0.9260             nan     0.1000    0.0013
##     40        0.8423             nan     0.1000   -0.0012
##     60        0.7983             nan     0.1000   -0.0014
##     80        0.7594             nan     0.1000   -0.0004
##    100        0.7323             nan     0.1000   -0.0013
##    120        0.7080             nan     0.1000   -0.0004
##    140        0.6841             nan     0.1000   -0.0015
##    160        0.6694             nan     0.1000   -0.0005
##    180        0.6512             nan     0.1000   -0.0009
##    200        0.6342             nan     0.1000   -0.0012
##    220        0.6167             nan     0.1000   -0.0007
##    240        0.5984             nan     0.1000   -0.0015
##    260        0.5829             nan     0.1000   -0.0012
##    280        0.5670             nan     0.1000   -0.0014
##    300        0.5534             nan     0.1000   -0.0014
##    320        0.5426             nan     0.1000   -0.0009
##    340        0.5278             nan     0.1000   -0.0009
##    360        0.5152             nan     0.1000   -0.0009
##    380        0.5037             nan     0.1000   -0.0005
##    400        0.4916             nan     0.1000   -0.0009
##    420        0.4808             nan     0.1000   -0.0009
##    440        0.4686             nan     0.1000   -0.0011
##    460        0.4627             nan     0.1000   -0.0014
##    480        0.4532             nan     0.1000   -0.0007
##    500        0.4419             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2374             nan     0.1000    0.0257
##      2        1.1950             nan     0.1000    0.0188
##      3        1.1509             nan     0.1000    0.0197
##      4        1.1240             nan     0.1000    0.0111
##      5        1.0936             nan     0.1000    0.0081
##      6        1.0696             nan     0.1000    0.0098
##      7        1.0456             nan     0.1000    0.0103
##      8        1.0252             nan     0.1000    0.0071
##      9        1.0029             nan     0.1000    0.0082
##     10        0.9871             nan     0.1000    0.0046
##     20        0.8820             nan     0.1000    0.0009
##     40        0.7909             nan     0.1000    0.0005
##     60        0.7294             nan     0.1000   -0.0020
##     80        0.6867             nan     0.1000   -0.0012
##    100        0.6474             nan     0.1000   -0.0016
##    120        0.6151             nan     0.1000   -0.0006
##    140        0.5843             nan     0.1000   -0.0013
##    160        0.5584             nan     0.1000   -0.0008
##    180        0.5315             nan     0.1000   -0.0011
##    200        0.5053             nan     0.1000   -0.0010
##    220        0.4861             nan     0.1000   -0.0011
##    240        0.4494             nan     0.1000   -0.0005
##    260        0.4315             nan     0.1000   -0.0016
##    280        0.4156             nan     0.1000   -0.0007
##    300        0.4015             nan     0.1000   -0.0009
##    320        0.3845             nan     0.1000   -0.0004
##    340        0.3684             nan     0.1000   -0.0017
##    360        0.3522             nan     0.1000   -0.0005
##    380        0.3389             nan     0.1000   -0.0007
##    400        0.3253             nan     0.1000   -0.0003
##    420        0.3119             nan     0.1000   -0.0004
##    440        0.2997             nan     0.1000   -0.0005
##    460        0.2876             nan     0.1000   -0.0008
##    480        0.2774             nan     0.1000   -0.0003
##    500        0.2681             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2439             nan     0.1000    0.0246
##      2        1.1984             nan     0.1000    0.0221
##      3        1.1573             nan     0.1000    0.0152
##      4        1.1247             nan     0.1000    0.0131
##      5        1.0904             nan     0.1000    0.0151
##      6        1.0648             nan     0.1000    0.0095
##      7        1.0441             nan     0.1000    0.0066
##      8        1.0262             nan     0.1000    0.0058
##      9        1.0088             nan     0.1000    0.0066
##     10        0.9950             nan     0.1000    0.0027
##     20        0.8897             nan     0.1000   -0.0007
##     40        0.7978             nan     0.1000   -0.0022
##     60        0.7389             nan     0.1000   -0.0025
##     80        0.6938             nan     0.1000   -0.0014
##    100        0.6560             nan     0.1000   -0.0012
##    120        0.6185             nan     0.1000   -0.0023
##    140        0.5923             nan     0.1000   -0.0013
##    160        0.5675             nan     0.1000   -0.0020
##    180        0.5425             nan     0.1000   -0.0013
##    200        0.5169             nan     0.1000   -0.0023
##    220        0.4953             nan     0.1000   -0.0012
##    240        0.4724             nan     0.1000   -0.0007
##    260        0.4532             nan     0.1000   -0.0005
##    280        0.4304             nan     0.1000   -0.0003
##    300        0.4088             nan     0.1000   -0.0006
##    320        0.3897             nan     0.1000   -0.0018
##    340        0.3740             nan     0.1000   -0.0008
##    360        0.3569             nan     0.1000   -0.0001
##    380        0.3438             nan     0.1000   -0.0006
##    400        0.3298             nan     0.1000   -0.0005
##    420        0.3176             nan     0.1000   -0.0005
##    440        0.3076             nan     0.1000   -0.0002
##    460        0.2948             nan     0.1000   -0.0002
##    480        0.2833             nan     0.1000   -0.0014
##    500        0.2747             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2411             nan     0.1000    0.0243
##      2        1.2011             nan     0.1000    0.0159
##      3        1.1598             nan     0.1000    0.0185
##      4        1.1309             nan     0.1000    0.0136
##      5        1.1032             nan     0.1000    0.0101
##      6        1.0810             nan     0.1000    0.0077
##      7        1.0551             nan     0.1000    0.0108
##      8        1.0349             nan     0.1000    0.0084
##      9        1.0120             nan     0.1000    0.0053
##     10        0.9975             nan     0.1000    0.0037
##     20        0.8942             nan     0.1000    0.0001
##     40        0.7990             nan     0.1000   -0.0012
##     60        0.7461             nan     0.1000   -0.0007
##     80        0.6957             nan     0.1000   -0.0009
##    100        0.6558             nan     0.1000   -0.0022
##    120        0.6224             nan     0.1000   -0.0014
##    140        0.5926             nan     0.1000   -0.0009
##    160        0.5698             nan     0.1000   -0.0018
##    180        0.5431             nan     0.1000   -0.0016
##    200        0.5210             nan     0.1000   -0.0011
##    220        0.4995             nan     0.1000   -0.0007
##    240        0.4770             nan     0.1000   -0.0012
##    260        0.4562             nan     0.1000   -0.0023
##    280        0.4374             nan     0.1000   -0.0009
##    300        0.4212             nan     0.1000   -0.0018
##    320        0.4023             nan     0.1000   -0.0009
##    340        0.3858             nan     0.1000   -0.0016
##    360        0.3683             nan     0.1000   -0.0009
##    380        0.3579             nan     0.1000   -0.0010
##    400        0.3418             nan     0.1000   -0.0008
##    420        0.3284             nan     0.1000   -0.0008
##    440        0.3183             nan     0.1000   -0.0011
##    460        0.3064             nan     0.1000   -0.0006
##    480        0.2920             nan     0.1000   -0.0001
##    500        0.2788             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2239             nan     0.2000    0.0319
##      2        1.1787             nan     0.2000    0.0230
##      3        1.1484             nan     0.2000    0.0126
##      4        1.1129             nan     0.2000    0.0105
##      5        1.0857             nan     0.2000    0.0123
##      6        1.0632             nan     0.2000    0.0071
##      7        1.0379             nan     0.2000    0.0112
##      8        1.0277             nan     0.2000    0.0003
##      9        1.0194             nan     0.2000    0.0005
##     10        1.0054             nan     0.2000    0.0017
##     20        0.9179             nan     0.2000   -0.0022
##     40        0.8535             nan     0.2000   -0.0015
##     60        0.8200             nan     0.2000   -0.0025
##     80        0.8000             nan     0.2000   -0.0014
##    100        0.7872             nan     0.2000   -0.0046
##    120        0.7703             nan     0.2000   -0.0000
##    140        0.7587             nan     0.2000   -0.0027
##    160        0.7431             nan     0.2000   -0.0010
##    180        0.7325             nan     0.2000   -0.0006
##    200        0.7256             nan     0.2000   -0.0027
##    220        0.7136             nan     0.2000   -0.0034
##    240        0.7028             nan     0.2000   -0.0011
##    260        0.6936             nan     0.2000   -0.0013
##    280        0.6882             nan     0.2000   -0.0035
##    300        0.6806             nan     0.2000   -0.0021
##    320        0.6770             nan     0.2000   -0.0011
##    340        0.6660             nan     0.2000   -0.0012
##    360        0.6569             nan     0.2000   -0.0020
##    380        0.6510             nan     0.2000   -0.0019
##    400        0.6459             nan     0.2000   -0.0004
##    420        0.6392             nan     0.2000   -0.0030
##    440        0.6347             nan     0.2000   -0.0011
##    460        0.6283             nan     0.2000   -0.0004
##    480        0.6245             nan     0.2000   -0.0022
##    500        0.6200             nan     0.2000   -0.0031
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2283             nan     0.2000    0.0308
##      2        1.1914             nan     0.2000    0.0156
##      3        1.1455             nan     0.2000    0.0169
##      4        1.1104             nan     0.2000    0.0145
##      5        1.0788             nan     0.2000    0.0118
##      6        1.0586             nan     0.2000    0.0076
##      7        1.0480             nan     0.2000   -0.0030
##      8        1.0271             nan     0.2000    0.0065
##      9        1.0103             nan     0.2000    0.0052
##     10        0.9926             nan     0.2000    0.0067
##     20        0.9154             nan     0.2000   -0.0017
##     40        0.8499             nan     0.2000   -0.0006
##     60        0.8266             nan     0.2000   -0.0024
##     80        0.8074             nan     0.2000   -0.0026
##    100        0.7832             nan     0.2000   -0.0022
##    120        0.7736             nan     0.2000   -0.0028
##    140        0.7606             nan     0.2000   -0.0009
##    160        0.7465             nan     0.2000   -0.0021
##    180        0.7351             nan     0.2000   -0.0037
##    200        0.7241             nan     0.2000   -0.0017
##    220        0.7193             nan     0.2000   -0.0024
##    240        0.7102             nan     0.2000   -0.0027
##    260        0.6988             nan     0.2000   -0.0014
##    280        0.6910             nan     0.2000   -0.0011
##    300        0.6842             nan     0.2000   -0.0027
##    320        0.6755             nan     0.2000   -0.0020
##    340        0.6701             nan     0.2000   -0.0029
##    360        0.6649             nan     0.2000   -0.0017
##    380        0.6595             nan     0.2000   -0.0033
##    400        0.6544             nan     0.2000   -0.0020
##    420        0.6469             nan     0.2000   -0.0011
##    440        0.6400             nan     0.2000   -0.0017
##    460        0.6350             nan     0.2000   -0.0020
##    480        0.6288             nan     0.2000   -0.0023
##    500        0.6270             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2235             nan     0.2000    0.0285
##      2        1.1772             nan     0.2000    0.0209
##      3        1.1403             nan     0.2000    0.0109
##      4        1.1030             nan     0.2000    0.0156
##      5        1.0832             nan     0.2000    0.0077
##      6        1.0636             nan     0.2000    0.0066
##      7        1.0456             nan     0.2000    0.0074
##      8        1.0236             nan     0.2000    0.0086
##      9        1.0133             nan     0.2000    0.0011
##     10        0.9991             nan     0.2000    0.0041
##     20        0.9040             nan     0.2000    0.0012
##     40        0.8472             nan     0.2000   -0.0008
##     60        0.8205             nan     0.2000   -0.0038
##     80        0.8066             nan     0.2000   -0.0006
##    100        0.7856             nan     0.2000   -0.0018
##    120        0.7713             nan     0.2000   -0.0012
##    140        0.7617             nan     0.2000   -0.0034
##    160        0.7464             nan     0.2000   -0.0007
##    180        0.7388             nan     0.2000   -0.0022
##    200        0.7283             nan     0.2000   -0.0025
##    220        0.7201             nan     0.2000   -0.0023
##    240        0.7110             nan     0.2000   -0.0021
##    260        0.7027             nan     0.2000   -0.0001
##    280        0.6897             nan     0.2000   -0.0018
##    300        0.6814             nan     0.2000   -0.0015
##    320        0.6710             nan     0.2000   -0.0012
##    340        0.6635             nan     0.2000   -0.0012
##    360        0.6571             nan     0.2000   -0.0023
##    380        0.6539             nan     0.2000   -0.0020
##    400        0.6475             nan     0.2000   -0.0018
##    420        0.6431             nan     0.2000   -0.0018
##    440        0.6363             nan     0.2000   -0.0017
##    460        0.6336             nan     0.2000   -0.0002
##    480        0.6273             nan     0.2000   -0.0025
##    500        0.6222             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2043             nan     0.2000    0.0410
##      2        1.1363             nan     0.2000    0.0299
##      3        1.0978             nan     0.2000    0.0124
##      4        1.0496             nan     0.2000    0.0164
##      5        1.0210             nan     0.2000    0.0122
##      6        0.9911             nan     0.2000    0.0060
##      7        0.9748             nan     0.2000    0.0027
##      8        0.9555             nan     0.2000    0.0066
##      9        0.9399             nan     0.2000    0.0039
##     10        0.9273             nan     0.2000    0.0014
##     20        0.8599             nan     0.2000   -0.0002
##     40        0.7827             nan     0.2000   -0.0055
##     60        0.7387             nan     0.2000    0.0012
##     80        0.6927             nan     0.2000   -0.0008
##    100        0.6539             nan     0.2000   -0.0022
##    120        0.6232             nan     0.2000   -0.0030
##    140        0.5938             nan     0.2000   -0.0035
##    160        0.5474             nan     0.2000   -0.0027
##    180        0.5181             nan     0.2000   -0.0042
##    200        0.4997             nan     0.2000   -0.0031
##    220        0.4763             nan     0.2000   -0.0004
##    240        0.4477             nan     0.2000   -0.0036
##    260        0.4274             nan     0.2000   -0.0024
##    280        0.4045             nan     0.2000   -0.0015
##    300        0.3854             nan     0.2000   -0.0008
##    320        0.3671             nan     0.2000   -0.0009
##    340        0.3518             nan     0.2000   -0.0004
##    360        0.3414             nan     0.2000   -0.0010
##    380        0.3318             nan     0.2000   -0.0008
##    400        0.3226             nan     0.2000   -0.0014
##    420        0.3091             nan     0.2000   -0.0028
##    440        0.2953             nan     0.2000   -0.0010
##    460        0.2844             nan     0.2000   -0.0013
##    480        0.2716             nan     0.2000   -0.0016
##    500        0.2645             nan     0.2000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2052             nan     0.2000    0.0312
##      2        1.1387             nan     0.2000    0.0321
##      3        1.0967             nan     0.2000    0.0166
##      4        1.0627             nan     0.2000    0.0115
##      5        1.0379             nan     0.2000    0.0070
##      6        1.0110             nan     0.2000    0.0055
##      7        0.9849             nan     0.2000    0.0094
##      8        0.9743             nan     0.2000   -0.0013
##      9        0.9573             nan     0.2000    0.0032
##     10        0.9413             nan     0.2000    0.0019
##     20        0.8553             nan     0.2000   -0.0023
##     40        0.7765             nan     0.2000   -0.0055
##     60        0.7192             nan     0.2000   -0.0011
##     80        0.6567             nan     0.2000   -0.0017
##    100        0.6213             nan     0.2000   -0.0027
##    120        0.5908             nan     0.2000   -0.0038
##    140        0.5672             nan     0.2000   -0.0033
##    160        0.5317             nan     0.2000   -0.0030
##    180        0.5114             nan     0.2000   -0.0025
##    200        0.4900             nan     0.2000   -0.0026
##    220        0.4704             nan     0.2000   -0.0012
##    240        0.4499             nan     0.2000   -0.0027
##    260        0.4257             nan     0.2000   -0.0009
##    280        0.4086             nan     0.2000   -0.0013
##    300        0.3894             nan     0.2000   -0.0008
##    320        0.3702             nan     0.2000   -0.0006
##    340        0.3554             nan     0.2000   -0.0032
##    360        0.3395             nan     0.2000   -0.0023
##    380        0.3262             nan     0.2000   -0.0026
##    400        0.3112             nan     0.2000   -0.0011
##    420        0.2935             nan     0.2000   -0.0006
##    440        0.2816             nan     0.2000   -0.0009
##    460        0.2737             nan     0.2000   -0.0012
##    480        0.2646             nan     0.2000   -0.0021
##    500        0.2530             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1924             nan     0.2000    0.0403
##      2        1.1270             nan     0.2000    0.0303
##      3        1.0839             nan     0.2000    0.0143
##      4        1.0488             nan     0.2000    0.0149
##      5        1.0175             nan     0.2000    0.0148
##      6        0.9966             nan     0.2000    0.0071
##      7        0.9766             nan     0.2000    0.0068
##      8        0.9524             nan     0.2000    0.0087
##      9        0.9396             nan     0.2000   -0.0002
##     10        0.9213             nan     0.2000    0.0036
##     20        0.8498             nan     0.2000   -0.0008
##     40        0.7668             nan     0.2000   -0.0027
##     60        0.7244             nan     0.2000   -0.0031
##     80        0.6724             nan     0.2000   -0.0020
##    100        0.6419             nan     0.2000   -0.0030
##    120        0.5988             nan     0.2000   -0.0010
##    140        0.5711             nan     0.2000   -0.0004
##    160        0.5392             nan     0.2000   -0.0023
##    180        0.5192             nan     0.2000   -0.0018
##    200        0.4962             nan     0.2000   -0.0023
##    220        0.4714             nan     0.2000   -0.0016
##    240        0.4560             nan     0.2000   -0.0010
##    260        0.4312             nan     0.2000   -0.0013
##    280        0.4111             nan     0.2000   -0.0017
##    300        0.3900             nan     0.2000   -0.0016
##    320        0.3757             nan     0.2000   -0.0010
##    340        0.3624             nan     0.2000   -0.0016
##    360        0.3455             nan     0.2000   -0.0026
##    380        0.3321             nan     0.2000   -0.0006
##    400        0.3171             nan     0.2000   -0.0005
##    420        0.3056             nan     0.2000   -0.0012
##    440        0.2949             nan     0.2000   -0.0013
##    460        0.2852             nan     0.2000   -0.0021
##    480        0.2758             nan     0.2000   -0.0022
##    500        0.2681             nan     0.2000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1925             nan     0.2000    0.0403
##      2        1.1136             nan     0.2000    0.0304
##      3        1.0554             nan     0.2000    0.0221
##      4        1.0103             nan     0.2000    0.0197
##      5        0.9847             nan     0.2000    0.0062
##      6        0.9601             nan     0.2000    0.0089
##      7        0.9317             nan     0.2000    0.0110
##      8        0.9146             nan     0.2000    0.0029
##      9        0.9022             nan     0.2000    0.0009
##     10        0.8896             nan     0.2000    0.0019
##     20        0.8106             nan     0.2000   -0.0025
##     40        0.7106             nan     0.2000   -0.0040
##     60        0.6290             nan     0.2000   -0.0024
##     80        0.5685             nan     0.2000   -0.0047
##    100        0.5188             nan     0.2000   -0.0013
##    120        0.4744             nan     0.2000   -0.0038
##    140        0.4301             nan     0.2000   -0.0020
##    160        0.3912             nan     0.2000   -0.0021
##    180        0.3554             nan     0.2000   -0.0003
##    200        0.3351             nan     0.2000   -0.0040
##    220        0.3115             nan     0.2000   -0.0017
##    240        0.2898             nan     0.2000   -0.0011
##    260        0.2689             nan     0.2000   -0.0018
##    280        0.2464             nan     0.2000   -0.0012
##    300        0.2289             nan     0.2000   -0.0016
##    320        0.2164             nan     0.2000   -0.0004
##    340        0.1996             nan     0.2000   -0.0004
##    360        0.1848             nan     0.2000   -0.0017
##    380        0.1719             nan     0.2000   -0.0003
##    400        0.1625             nan     0.2000   -0.0011
##    420        0.1514             nan     0.2000   -0.0007
##    440        0.1424             nan     0.2000   -0.0007
##    460        0.1352             nan     0.2000   -0.0007
##    480        0.1271             nan     0.2000   -0.0010
##    500        0.1211             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2032             nan     0.2000    0.0359
##      2        1.1263             nan     0.2000    0.0326
##      3        1.0725             nan     0.2000    0.0182
##      4        1.0286             nan     0.2000    0.0157
##      5        1.0027             nan     0.2000    0.0066
##      6        0.9745             nan     0.2000    0.0089
##      7        0.9471             nan     0.2000    0.0075
##      8        0.9207             nan     0.2000    0.0083
##      9        0.8994             nan     0.2000    0.0058
##     10        0.8859             nan     0.2000    0.0016
##     20        0.7949             nan     0.2000   -0.0016
##     40        0.7143             nan     0.2000   -0.0020
##     60        0.6468             nan     0.2000   -0.0006
##     80        0.5879             nan     0.2000   -0.0032
##    100        0.5345             nan     0.2000   -0.0025
##    120        0.4862             nan     0.2000   -0.0012
##    140        0.4431             nan     0.2000   -0.0011
##    160        0.4047             nan     0.2000   -0.0018
##    180        0.3607             nan     0.2000   -0.0020
##    200        0.3350             nan     0.2000   -0.0030
##    220        0.3085             nan     0.2000   -0.0035
##    240        0.2878             nan     0.2000   -0.0022
##    260        0.2688             nan     0.2000   -0.0012
##    280        0.2469             nan     0.2000   -0.0025
##    300        0.2315             nan     0.2000   -0.0020
##    320        0.2179             nan     0.2000   -0.0014
##    340        0.2042             nan     0.2000   -0.0012
##    360        0.1916             nan     0.2000   -0.0025
##    380        0.1791             nan     0.2000   -0.0012
##    400        0.1673             nan     0.2000   -0.0011
##    420        0.1545             nan     0.2000   -0.0009
##    440        0.1451             nan     0.2000   -0.0003
##    460        0.1372             nan     0.2000   -0.0015
##    480        0.1283             nan     0.2000   -0.0008
##    500        0.1204             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1906             nan     0.2000    0.0429
##      2        1.1242             nan     0.2000    0.0234
##      3        1.0717             nan     0.2000    0.0158
##      4        1.0324             nan     0.2000    0.0121
##      5        0.9855             nan     0.2000    0.0214
##      6        0.9584             nan     0.2000    0.0102
##      7        0.9339             nan     0.2000    0.0080
##      8        0.9111             nan     0.2000    0.0067
##      9        0.8983             nan     0.2000   -0.0010
##     10        0.8799             nan     0.2000    0.0032
##     20        0.7868             nan     0.2000   -0.0006
##     40        0.6919             nan     0.2000   -0.0060
##     60        0.6228             nan     0.2000   -0.0041
##     80        0.5621             nan     0.2000   -0.0063
##    100        0.5088             nan     0.2000   -0.0024
##    120        0.4669             nan     0.2000    0.0001
##    140        0.4383             nan     0.2000   -0.0011
##    160        0.4013             nan     0.2000   -0.0021
##    180        0.3727             nan     0.2000   -0.0005
##    200        0.3445             nan     0.2000   -0.0038
##    220        0.3230             nan     0.2000   -0.0021
##    240        0.2994             nan     0.2000   -0.0008
##    260        0.2758             nan     0.2000   -0.0008
##    280        0.2596             nan     0.2000   -0.0019
##    300        0.2361             nan     0.2000   -0.0015
##    320        0.2209             nan     0.2000   -0.0019
##    340        0.2070             nan     0.2000   -0.0008
##    360        0.1912             nan     0.2000   -0.0004
##    380        0.1772             nan     0.2000   -0.0012
##    400        0.1646             nan     0.2000   -0.0015
##    420        0.1528             nan     0.2000   -0.0003
##    440        0.1442             nan     0.2000   -0.0003
##    460        0.1358             nan     0.2000   -0.0008
##    480        0.1255             nan     0.2000   -0.0003
##    500        0.1180             nan     0.2000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2042             nan     0.3000    0.0475
##      2        1.1523             nan     0.3000    0.0201
##      3        1.1060             nan     0.3000    0.0159
##      4        1.0687             nan     0.3000    0.0106
##      5        1.0362             nan     0.3000    0.0130
##      6        1.0012             nan     0.3000    0.0143
##      7        0.9854             nan     0.3000    0.0051
##      8        0.9712             nan     0.3000    0.0018
##      9        0.9483             nan     0.3000    0.0103
##     10        0.9400             nan     0.3000   -0.0006
##     20        0.8690             nan     0.3000   -0.0009
##     40        0.8143             nan     0.3000   -0.0071
##     60        0.7842             nan     0.3000   -0.0029
##     80        0.7698             nan     0.3000   -0.0001
##    100        0.7495             nan     0.3000   -0.0007
##    120        0.7318             nan     0.3000   -0.0033
##    140        0.7128             nan     0.3000    0.0000
##    160        0.7055             nan     0.3000   -0.0039
##    180        0.6916             nan     0.3000    0.0002
##    200        0.6823             nan     0.3000   -0.0047
##    220        0.6754             nan     0.3000   -0.0031
##    240        0.6656             nan     0.3000   -0.0017
##    260        0.6506             nan     0.3000   -0.0021
##    280        0.6414             nan     0.3000   -0.0032
##    300        0.6335             nan     0.3000   -0.0042
##    320        0.6290             nan     0.3000   -0.0032
##    340        0.6200             nan     0.3000   -0.0033
##    360        0.6091             nan     0.3000   -0.0008
##    380        0.5985             nan     0.3000   -0.0012
##    400        0.5970             nan     0.3000   -0.0027
##    420        0.5899             nan     0.3000   -0.0068
##    440        0.5836             nan     0.3000   -0.0004
##    460        0.5737             nan     0.3000   -0.0010
##    480        0.5671             nan     0.3000   -0.0022
##    500        0.5618             nan     0.3000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1943             nan     0.3000    0.0441
##      2        1.1438             nan     0.3000    0.0221
##      3        1.0993             nan     0.3000    0.0174
##      4        1.0556             nan     0.3000    0.0100
##      5        1.0237             nan     0.3000    0.0156
##      6        0.9963             nan     0.3000    0.0073
##      7        0.9806             nan     0.3000    0.0051
##      8        0.9676             nan     0.3000    0.0036
##      9        0.9461             nan     0.3000    0.0061
##     10        0.9380             nan     0.3000    0.0016
##     20        0.8652             nan     0.3000   -0.0020
##     40        0.8194             nan     0.3000   -0.0010
##     60        0.7829             nan     0.3000   -0.0048
##     80        0.7682             nan     0.3000   -0.0026
##    100        0.7555             nan     0.3000   -0.0013
##    120        0.7358             nan     0.3000   -0.0026
##    140        0.7214             nan     0.3000   -0.0011
##    160        0.7060             nan     0.3000   -0.0013
##    180        0.7000             nan     0.3000   -0.0050
##    200        0.6791             nan     0.3000   -0.0041
##    220        0.6702             nan     0.3000   -0.0038
##    240        0.6606             nan     0.3000   -0.0028
##    260        0.6518             nan     0.3000   -0.0010
##    280        0.6357             nan     0.3000   -0.0027
##    300        0.6319             nan     0.3000   -0.0028
##    320        0.6241             nan     0.3000   -0.0026
##    340        0.6190             nan     0.3000   -0.0022
##    360        0.6058             nan     0.3000   -0.0020
##    380        0.6023             nan     0.3000   -0.0042
##    400        0.5941             nan     0.3000   -0.0025
##    420        0.5874             nan     0.3000   -0.0018
##    440        0.5786             nan     0.3000   -0.0034
##    460        0.5676             nan     0.3000   -0.0014
##    480        0.5642             nan     0.3000   -0.0026
##    500        0.5561             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2061             nan     0.3000    0.0464
##      2        1.1432             nan     0.3000    0.0319
##      3        1.0981             nan     0.3000    0.0144
##      4        1.0538             nan     0.3000    0.0125
##      5        1.0357             nan     0.3000    0.0061
##      6        1.0239             nan     0.3000   -0.0040
##      7        0.9923             nan     0.3000    0.0135
##      8        0.9751             nan     0.3000    0.0074
##      9        0.9643             nan     0.3000   -0.0029
##     10        0.9469             nan     0.3000    0.0054
##     20        0.8832             nan     0.3000   -0.0024
##     40        0.8387             nan     0.3000   -0.0052
##     60        0.8055             nan     0.3000   -0.0015
##     80        0.7809             nan     0.3000   -0.0018
##    100        0.7685             nan     0.3000   -0.0033
##    120        0.7457             nan     0.3000   -0.0043
##    140        0.7300             nan     0.3000   -0.0043
##    160        0.7145             nan     0.3000   -0.0034
##    180        0.7026             nan     0.3000   -0.0023
##    200        0.6860             nan     0.3000   -0.0004
##    220        0.6718             nan     0.3000   -0.0016
##    240        0.6656             nan     0.3000   -0.0035
##    260        0.6561             nan     0.3000   -0.0026
##    280        0.6412             nan     0.3000   -0.0025
##    300        0.6374             nan     0.3000   -0.0040
##    320        0.6283             nan     0.3000   -0.0035
##    340        0.6199             nan     0.3000   -0.0004
##    360        0.6153             nan     0.3000   -0.0030
##    380        0.6079             nan     0.3000   -0.0046
##    400        0.6046             nan     0.3000   -0.0021
##    420        0.5946             nan     0.3000   -0.0029
##    440        0.5878             nan     0.3000   -0.0037
##    460        0.5779             nan     0.3000   -0.0003
##    480        0.5703             nan     0.3000   -0.0026
##    500        0.5670             nan     0.3000   -0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1672             nan     0.3000    0.0595
##      2        1.0987             nan     0.3000    0.0297
##      3        1.0432             nan     0.3000    0.0100
##      4        1.0038             nan     0.3000    0.0128
##      5        0.9783             nan     0.3000    0.0051
##      6        0.9548             nan     0.3000    0.0041
##      7        0.9315             nan     0.3000    0.0018
##      8        0.9208             nan     0.3000    0.0015
##      9        0.9024             nan     0.3000    0.0021
##     10        0.8976             nan     0.3000   -0.0093
##     20        0.8117             nan     0.3000   -0.0020
##     40        0.7211             nan     0.3000   -0.0010
##     60        0.6660             nan     0.3000   -0.0021
##     80        0.6150             nan     0.3000   -0.0015
##    100        0.5700             nan     0.3000   -0.0060
##    120        0.5152             nan     0.3000   -0.0058
##    140        0.4755             nan     0.3000   -0.0017
##    160        0.4503             nan     0.3000   -0.0041
##    180        0.4173             nan     0.3000   -0.0021
##    200        0.3860             nan     0.3000   -0.0049
##    220        0.3628             nan     0.3000   -0.0007
##    240        0.3364             nan     0.3000   -0.0005
##    260        0.3097             nan     0.3000   -0.0018
##    280        0.2906             nan     0.3000   -0.0011
##    300        0.2722             nan     0.3000   -0.0018
##    320        0.2591             nan     0.3000   -0.0008
##    340        0.2455             nan     0.3000   -0.0023
##    360        0.2332             nan     0.3000   -0.0008
##    380        0.2240             nan     0.3000   -0.0019
##    400        0.2100             nan     0.3000   -0.0005
##    420        0.2040             nan     0.3000   -0.0001
##    440        0.1946             nan     0.3000   -0.0009
##    460        0.1860             nan     0.3000   -0.0017
##    480        0.1730             nan     0.3000   -0.0016
##    500        0.1641             nan     0.3000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1593             nan     0.3000    0.0502
##      2        1.0833             nan     0.3000    0.0364
##      3        1.0407             nan     0.3000    0.0142
##      4        1.0000             nan     0.3000    0.0157
##      5        0.9641             nan     0.3000    0.0073
##      6        0.9406             nan     0.3000    0.0040
##      7        0.9177             nan     0.3000    0.0093
##      8        0.9003             nan     0.3000    0.0016
##      9        0.8917             nan     0.3000   -0.0066
##     10        0.8843             nan     0.3000   -0.0051
##     20        0.7986             nan     0.3000   -0.0032
##     40        0.7144             nan     0.3000   -0.0009
##     60        0.6534             nan     0.3000   -0.0009
##     80        0.6247             nan     0.3000   -0.0033
##    100        0.5794             nan     0.3000   -0.0032
##    120        0.5491             nan     0.3000   -0.0040
##    140        0.5037             nan     0.3000   -0.0024
##    160        0.4749             nan     0.3000   -0.0021
##    180        0.4359             nan     0.3000   -0.0014
##    200        0.4201             nan     0.3000   -0.0039
##    220        0.3880             nan     0.3000   -0.0020
##    240        0.3682             nan     0.3000   -0.0022
##    260        0.3459             nan     0.3000   -0.0023
##    280        0.3207             nan     0.3000   -0.0008
##    300        0.3050             nan     0.3000   -0.0009
##    320        0.2844             nan     0.3000   -0.0017
##    340        0.2648             nan     0.3000   -0.0015
##    360        0.2500             nan     0.3000   -0.0018
##    380        0.2342             nan     0.3000   -0.0027
##    400        0.2203             nan     0.3000   -0.0019
##    420        0.2062             nan     0.3000   -0.0008
##    440        0.1957             nan     0.3000   -0.0015
##    460        0.1850             nan     0.3000   -0.0003
##    480        0.1754             nan     0.3000   -0.0005
##    500        0.1667             nan     0.3000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1743             nan     0.3000    0.0547
##      2        1.1062             nan     0.3000    0.0287
##      3        1.0504             nan     0.3000    0.0268
##      4        1.0045             nan     0.3000    0.0126
##      5        0.9728             nan     0.3000    0.0145
##      6        0.9476             nan     0.3000    0.0069
##      7        0.9236             nan     0.3000    0.0038
##      8        0.9032             nan     0.3000    0.0063
##      9        0.8905             nan     0.3000    0.0005
##     10        0.8775             nan     0.3000   -0.0013
##     20        0.8154             nan     0.3000   -0.0023
##     40        0.7271             nan     0.3000   -0.0025
##     60        0.6722             nan     0.3000   -0.0034
##     80        0.6114             nan     0.3000   -0.0029
##    100        0.5718             nan     0.3000   -0.0039
##    120        0.5353             nan     0.3000   -0.0019
##    140        0.4929             nan     0.3000   -0.0031
##    160        0.4623             nan     0.3000   -0.0033
##    180        0.4363             nan     0.3000   -0.0026
##    200        0.4109             nan     0.3000   -0.0024
##    220        0.3771             nan     0.3000   -0.0008
##    240        0.3606             nan     0.3000   -0.0037
##    260        0.3366             nan     0.3000   -0.0032
##    280        0.3191             nan     0.3000   -0.0017
##    300        0.2976             nan     0.3000   -0.0007
##    320        0.2780             nan     0.3000   -0.0033
##    340        0.2649             nan     0.3000   -0.0114
##    360        0.2514             nan     0.3000   -0.0022
##    380        0.2433             nan     0.3000   -0.0029
##    400        0.2259             nan     0.3000   -0.0024
##    420        0.2125             nan     0.3000   -0.0013
##    440        0.2006             nan     0.3000   -0.0013
##    460        0.1921             nan     0.3000   -0.0011
##    480        0.1833             nan     0.3000   -0.0019
##    500        0.1733             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1468             nan     0.3000    0.0664
##      2        1.0552             nan     0.3000    0.0391
##      3        0.9990             nan     0.3000    0.0213
##      4        0.9556             nan     0.3000    0.0142
##      5        0.9301             nan     0.3000    0.0086
##      6        0.9011             nan     0.3000    0.0061
##      7        0.8775             nan     0.3000    0.0029
##      8        0.8604             nan     0.3000   -0.0020
##      9        0.8480             nan     0.3000   -0.0034
##     10        0.8282             nan     0.3000    0.0020
##     20        0.7472             nan     0.3000   -0.0061
##     40        0.6452             nan     0.3000   -0.0022
##     60        0.5631             nan     0.3000   -0.0003
##     80        0.4964             nan     0.3000   -0.0060
##    100           inf             nan     0.3000       nan
##    120           inf             nan     0.3000       nan
##    140           inf             nan     0.3000       nan
##    160           inf             nan     0.3000       nan
##    180           inf             nan     0.3000       nan
##    200           inf             nan     0.3000       nan
##    220           inf             nan     0.3000       nan
##    240           inf             nan     0.3000       nan
##    260           inf             nan     0.3000       nan
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1526             nan     0.3000    0.0604
##      2        1.0599             nan     0.3000    0.0339
##      3        1.0071             nan     0.3000    0.0188
##      4        0.9620             nan     0.3000    0.0119
##      5        0.9280             nan     0.3000    0.0108
##      6        0.9074             nan     0.3000   -0.0058
##      7        0.8799             nan     0.3000    0.0015
##      8        0.8619             nan     0.3000    0.0019
##      9        0.8499             nan     0.3000   -0.0046
##     10        0.8404             nan     0.3000   -0.0036
##     20        0.7590             nan     0.3000    0.0007
##     40        0.6422             nan     0.3000   -0.0050
##     60        0.5499             nan     0.3000   -0.0054
##     80        0.4860             nan     0.3000   -0.0018
##    100        0.4111             nan     0.3000   -0.0046
##    120        0.3623             nan     0.3000   -0.0040
##    140        0.3174             nan     0.3000   -0.0022
##    160        0.2858             nan     0.3000   -0.0023
##    180        0.2582             nan     0.3000   -0.0027
##    200        0.2329             nan     0.3000   -0.0023
##    220        0.2089             nan     0.3000   -0.0018
##    240        0.1909             nan     0.3000   -0.0020
##    260        0.1756             nan     0.3000   -0.0012
##    280        0.1583             nan     0.3000   -0.0015
##    300        0.1433             nan     0.3000   -0.0000
##    320        0.1278             nan     0.3000   -0.0021
##    340        0.1154             nan     0.3000   -0.0011
##    360        0.1050             nan     0.3000   -0.0004
##    380        0.0987             nan     0.3000   -0.0012
##    400        0.0887             nan     0.3000   -0.0008
##    420        0.0809             nan     0.3000   -0.0011
##    440        0.0749             nan     0.3000   -0.0012
##    460        0.0686             nan     0.3000   -0.0002
##    480        0.0635             nan     0.3000   -0.0003
##    500        0.0589             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1628             nan     0.3000    0.0596
##      2        1.0736             nan     0.3000    0.0375
##      3        1.0220             nan     0.3000    0.0117
##      4        0.9727             nan     0.3000    0.0187
##      5        0.9355             nan     0.3000    0.0114
##      6        0.9162             nan     0.3000   -0.0052
##      7        0.8936             nan     0.3000    0.0019
##      8        0.8909             nan     0.3000   -0.0112
##      9        0.8729             nan     0.3000   -0.0011
##     10        0.8635             nan     0.3000   -0.0083
##     20        0.7835             nan     0.3000   -0.0050
##     40        0.6677             nan     0.3000   -0.0048
##     60        0.5718             nan     0.3000   -0.0059
##     80        0.5054             nan     0.3000   -0.0002
##    100        0.4488             nan     0.3000   -0.0031
##    120        0.4087             nan     0.3000   -0.0010
##    140        0.3607             nan     0.3000   -0.0028
##    160        0.3114             nan     0.3000   -0.0019
##    180        0.2813             nan     0.3000   -0.0033
##    200        0.2478             nan     0.3000   -0.0019
##    220        0.2267             nan     0.3000   -0.0005
##    240        0.2020             nan     0.3000   -0.0017
##    260        0.1842             nan     0.3000   -0.0012
##    280        0.1675             nan     0.3000   -0.0016
##    300        0.1497             nan     0.3000   -0.0004
##    320        0.1338             nan     0.3000   -0.0008
##    340        0.1213             nan     0.3000   -0.0010
##    360        0.1105             nan     0.3000   -0.0009
##    380        0.1009             nan     0.3000   -0.0011
##    400        0.0923             nan     0.3000   -0.0014
##    420        0.0838             nan     0.3000   -0.0002
##    440        0.0766             nan     0.3000   -0.0005
##    460        0.0683             nan     0.3000   -0.0004
##    480        0.0623             nan     0.3000   -0.0007
##    500        0.0577             nan     0.3000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1523             nan     0.5000    0.0704
##      2        1.0840             nan     0.5000    0.0263
##      3        1.0362             nan     0.5000    0.0221
##      4        0.9858             nan     0.5000    0.0195
##      5        0.9747             nan     0.5000   -0.0052
##      6        0.9660             nan     0.5000   -0.0036
##      7        0.9443             nan     0.5000    0.0027
##      8        0.9261             nan     0.5000    0.0051
##      9        0.9100             nan     0.5000    0.0039
##     10        0.9070             nan     0.5000   -0.0083
##     20        0.8518             nan     0.5000   -0.0050
##     40        0.7982             nan     0.5000   -0.0045
##     60        0.7822             nan     0.5000   -0.0099
##     80        0.7468             nan     0.5000   -0.0063
##    100        0.7205             nan     0.5000   -0.0042
##    120        0.7074             nan     0.5000   -0.0135
##    140        0.6816             nan     0.5000   -0.0075
##    160        0.6700             nan     0.5000   -0.0069
##    180        0.6615             nan     0.5000   -0.0099
##    200        0.6463             nan     0.5000   -0.0023
##    220        0.6379             nan     0.5000    0.0029
##    240        0.6192             nan     0.5000   -0.0036
##    260        0.6045             nan     0.5000   -0.0028
##    280        0.5943             nan     0.5000   -0.0038
##    300        0.5939             nan     0.5000   -0.0035
##    320        0.5752             nan     0.5000   -0.0012
##    340        0.5638             nan     0.5000   -0.0039
##    360        0.5637             nan     0.5000   -0.0064
##    380        0.5540             nan     0.5000   -0.0035
##    400        0.5480             nan     0.5000   -0.0010
##    420        0.5337             nan     0.5000   -0.0036
##    440        0.5348             nan     0.5000   -0.0072
##    460        0.5177             nan     0.5000   -0.0035
##    480        0.5102             nan     0.5000   -0.0055
##    500        0.5010             nan     0.5000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1418             nan     0.5000    0.0607
##      2        1.0793             nan     0.5000    0.0230
##      3        1.0287             nan     0.5000    0.0209
##      4        0.9895             nan     0.5000    0.0209
##      5        0.9710             nan     0.5000    0.0051
##      6        0.9609             nan     0.5000   -0.0018
##      7        0.9415             nan     0.5000    0.0079
##      8        0.9364             nan     0.5000   -0.0063
##      9        0.9285             nan     0.5000   -0.0019
##     10        0.9166             nan     0.5000    0.0033
##     20        0.8512             nan     0.5000   -0.0026
##     40        0.8006             nan     0.5000   -0.0088
##     60        0.7766             nan     0.5000   -0.0131
##     80        0.7477             nan     0.5000   -0.0066
##    100        0.7147             nan     0.5000   -0.0068
##    120        0.6973             nan     0.5000    0.0004
##    140        0.6772             nan     0.5000   -0.0031
##    160        0.6545             nan     0.5000   -0.0056
##    180        0.6283             nan     0.5000   -0.0042
##    200        0.6157             nan     0.5000   -0.0039
##    220        0.6057             nan     0.5000   -0.0039
##    240        0.5932             nan     0.5000   -0.0062
##    260        0.5733             nan     0.5000   -0.0074
##    280        0.5580             nan     0.5000   -0.0028
##    300        0.5466             nan     0.5000   -0.0020
##    320        0.5457             nan     0.5000   -0.0070
##    340        0.5300             nan     0.5000   -0.0038
##    360        0.5264             nan     0.5000   -0.0129
##    380        0.5194             nan     0.5000   -0.0036
##    400        0.5136             nan     0.5000   -0.0107
##    420        0.5022             nan     0.5000   -0.0040
##    440        0.4935             nan     0.5000   -0.0022
##    460        0.4792             nan     0.5000   -0.0028
##    480        0.4797             nan     0.5000   -0.0025
##    500        0.4714             nan     0.5000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1533             nan     0.5000    0.0645
##      2        1.0902             nan     0.5000    0.0143
##      3        1.0355             nan     0.5000    0.0244
##      4        1.0113             nan     0.5000    0.0110
##      5        0.9690             nan     0.5000    0.0121
##      6        0.9457             nan     0.5000    0.0086
##      7        0.9403             nan     0.5000   -0.0061
##      8        0.9246             nan     0.5000    0.0082
##      9        0.9143             nan     0.5000    0.0033
##     10        0.9058             nan     0.5000   -0.0082
##     20        0.8644             nan     0.5000   -0.0066
##     40        0.8211             nan     0.5000   -0.0020
##     60        0.7803             nan     0.5000   -0.0016
##     80        0.7583             nan     0.5000   -0.0063
##    100        0.7417             nan     0.5000   -0.0147
##    120        0.7140             nan     0.5000   -0.0015
##    140        0.7060             nan     0.5000   -0.0068
##    160        0.6719             nan     0.5000   -0.0000
##    180        0.6557             nan     0.5000    0.0002
##    200        0.6515             nan     0.5000   -0.0005
##    220        0.6352             nan     0.5000   -0.0072
##    240        0.6117             nan     0.5000   -0.0016
##    260        0.5944             nan     0.5000   -0.0047
##    280        0.5804             nan     0.5000   -0.0034
##    300        0.5705             nan     0.5000   -0.0027
##    320        0.5617             nan     0.5000   -0.0018
##    340        0.5448             nan     0.5000   -0.0079
##    360        0.5370             nan     0.5000   -0.0061
##    380        0.5295             nan     0.5000   -0.0033
##    400        0.5230             nan     0.5000   -0.0040
##    420        0.5113             nan     0.5000   -0.0042
##    440        0.4989             nan     0.5000   -0.0041
##    460        0.4962             nan     0.5000   -0.0050
##    480        0.4891             nan     0.5000   -0.0023
##    500        0.4897             nan     0.5000   -0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1260             nan     0.5000    0.0637
##      2        1.0534             nan     0.5000    0.0362
##      3        1.0144             nan     0.5000   -0.0001
##      4        0.9595             nan     0.5000    0.0254
##      5        0.9416             nan     0.5000    0.0020
##      6        0.9218             nan     0.5000    0.0010
##      7        0.9096             nan     0.5000   -0.0076
##      8        0.9062             nan     0.5000   -0.0142
##      9        0.8820             nan     0.5000    0.0028
##     10        0.8714             nan     0.5000   -0.0036
##     20        0.7920             nan     0.5000   -0.0164
##     40        0.7028             nan     0.5000   -0.0082
##     60        0.6293             nan     0.5000   -0.0176
##     80        0.5658             nan     0.5000   -0.0075
##    100        0.5153             nan     0.5000   -0.0065
##    120        0.4833             nan     0.5000   -0.0011
##    140        6.3036             nan     0.5000   -0.0079
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000   -0.0001
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000    0.0002
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1169             nan     0.5000    0.0753
##      2        1.0221             nan     0.5000    0.0435
##      3        0.9848             nan     0.5000    0.0066
##      4        0.9550             nan     0.5000    0.0035
##      5        0.9228             nan     0.5000    0.0008
##      6        0.9038             nan     0.5000   -0.0022
##      7        0.8946             nan     0.5000   -0.0086
##      8        0.8765             nan     0.5000    0.0024
##      9        0.8613             nan     0.5000   -0.0083
##     10        0.8510             nan     0.5000   -0.0012
##     20        0.8128             nan     0.5000   -0.0092
##     40        0.6925             nan     0.5000   -0.0138
##     60        0.6251             nan     0.5000   -0.0029
##     80        0.5455             nan     0.5000    0.0030
##    100        0.4994             nan     0.5000   -0.0017
##    120        0.4586             nan     0.5000   -0.0031
##    140        0.4710             nan     0.5000   -0.0033
##    160        0.4232             nan     0.5000   -0.0033
##    180        0.3818             nan     0.5000    0.0000
##    200        0.3474             nan     0.5000   -0.0019
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1236             nan     0.5000    0.0586
##      2        1.0318             nan     0.5000    0.0296
##      3        0.9745             nan     0.5000    0.0163
##      4        0.9439             nan     0.5000    0.0090
##      5        0.9267             nan     0.5000   -0.0069
##      6        0.9011             nan     0.5000    0.0029
##      7        0.8905             nan     0.5000   -0.0049
##      8        0.8765             nan     0.5000   -0.0085
##      9        0.8583             nan     0.5000    0.0003
##     10        0.8454             nan     0.5000    0.0017
##     20        0.8124             nan     0.5000   -0.0166
##     40        0.6928             nan     0.5000   -0.0148
##     60        0.5995             nan     0.5000   -0.0015
##     80        0.5367             nan     0.5000   -0.0055
##    100        0.4913             nan     0.5000   -0.0041
##    120        0.4383             nan     0.5000   -0.0020
##    140        0.3953             nan     0.5000   -0.0068
##    160        0.3474             nan     0.5000   -0.0046
##    180        0.3046             nan     0.5000   -0.0020
##    200        0.2814             nan     0.5000   -0.0013
##    220        0.2487             nan     0.5000   -0.0024
##    240        0.2213             nan     0.5000   -0.0021
##    260        0.1953             nan     0.5000   -0.0001
##    280        0.1789             nan     0.5000   -0.0015
##    300        0.1643             nan     0.5000   -0.0031
##    320        0.1493             nan     0.5000   -0.0019
##    340        0.1383             nan     0.5000   -0.0032
##    360        0.1296             nan     0.5000   -0.0014
##    380        0.1200             nan     0.5000   -0.0023
##    400        0.1084             nan     0.5000   -0.0010
##    420        0.0999             nan     0.5000   -0.0007
##    440        0.0915             nan     0.5000   -0.0007
##    460        0.0865             nan     0.5000   -0.0019
##    480        0.0766             nan     0.5000   -0.0004
##    500        0.0709             nan     0.5000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0767             nan     0.5000    0.0957
##      2        0.9825             nan     0.5000    0.0356
##      3        0.9535             nan     0.5000   -0.0049
##      4        0.9234             nan     0.5000   -0.0135
##      5        0.8881             nan     0.5000    0.0047
##      6        0.8790             nan     0.5000   -0.0101
##      7        0.8558             nan     0.5000   -0.0052
##      8        0.8346             nan     0.5000   -0.0016
##      9        0.8264             nan     0.5000   -0.0089
##     10        0.8085             nan     0.5000   -0.0088
##     20        0.7798             nan     0.5000   -0.0189
##     40        0.6201             nan     0.5000   -0.0149
##     60        0.4674             nan     0.5000   -0.0101
##     80        0.3606             nan     0.5000   -0.0034
##    100        0.2820             nan     0.5000   -0.0030
##    120        0.2343             nan     0.5000   -0.0063
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000   -0.0012
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0857             nan     0.5000    0.0888
##      2        1.0038             nan     0.5000    0.0166
##      3        0.9626             nan     0.5000    0.0067
##      4        0.9176             nan     0.5000    0.0145
##      5        0.8976             nan     0.5000   -0.0018
##      6        0.8654             nan     0.5000    0.0095
##      7        0.8409             nan     0.5000    0.0054
##      8        0.8394             nan     0.5000   -0.0221
##      9        0.8189             nan     0.5000    0.0042
##     10        0.8058             nan     0.5000   -0.0102
##     20        0.7344             nan     0.5000   -0.0163
##     40        3.2705             nan     0.5000   -0.0086
##     60           inf             nan     0.5000       nan
##     80      207.9548             nan     0.5000   -0.0012
##    100      207.9103             nan     0.5000   -0.0096
##    120      207.8613             nan     0.5000   -0.0040
##    140      207.8522             nan     0.5000   -0.0108
##    160      207.8261             nan     0.5000   -0.0041
##    180      207.8477             nan     0.5000   -0.0029
##    200      207.8418             nan     0.5000   -0.0119
##    220      207.8285             nan     0.5000   -0.0073
##    240      207.8107             nan     0.5000   -0.0041
##    260      213.7601             nan     0.5000   -0.0015
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0961             nan     0.5000    0.0903
##      2        1.0039             nan     0.5000    0.0375
##      3        0.9536             nan     0.5000    0.0122
##      4        0.9207             nan     0.5000   -0.0022
##      5        0.8823             nan     0.5000    0.0101
##      6        0.8557             nan     0.5000    0.0079
##      7        0.8450             nan     0.5000   -0.0076
##      8        0.8365             nan     0.5000   -0.0111
##      9        0.8208             nan     0.5000   -0.0042
##     10        0.8070             nan     0.5000   -0.0063
##     20        0.7014             nan     0.5000   -0.0141
##     40        0.5825             nan     0.5000   -0.0111
##     60        0.4665             nan     0.5000   -0.0031
##     80        0.3921             nan     0.5000   -0.0148
##    100        0.3276             nan     0.5000   -0.0027
##    120        0.2674             nan     0.5000   -0.0020
##    140        0.2254             nan     0.5000   -0.0029
##    160        0.1908             nan     0.5000   -0.0039
##    180        0.1656             nan     0.5000   -0.0019
##    200        0.1399             nan     0.5000   -0.0010
##    220        0.1210             nan     0.5000    0.0000
##    240        0.1027             nan     0.5000   -0.0013
##    260        0.0879             nan     0.5000   -0.0015
##    280        0.0738             nan     0.5000   -0.0014
##    300        0.0643             nan     0.5000   -0.0013
##    320        0.0545             nan     0.5000   -0.0001
##    340        0.0480             nan     0.5000   -0.0010
##    360        0.0433             nan     0.5000   -0.0011
##    380        0.0385             nan     0.5000   -0.0002
##    400        0.0339             nan     0.5000   -0.0001
##    420        0.0295             nan     0.5000   -0.0007
##    440        0.0258             nan     0.5000   -0.0002
##    460        0.0230             nan     0.5000   -0.0003
##    480        0.0206             nan     0.5000   -0.0002
##    500        0.0183             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1116             nan     1.0000    0.0824
##      2        1.0445             nan     1.0000    0.0310
##      3        0.9954             nan     1.0000    0.0095
##      4        0.9868             nan     1.0000   -0.0113
##      5        0.9783             nan     1.0000   -0.0067
##      6        0.9430             nan     1.0000    0.0142
##      7        0.9285             nan     1.0000   -0.0092
##      8        0.9434             nan     1.0000   -0.0322
##      9        0.9440             nan     1.0000   -0.0134
##     10        0.9557             nan     1.0000   -0.0228
##     20        0.9453             nan     1.0000    0.0156
##     40        0.8512             nan     1.0000   -0.0007
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1110             nan     1.0000    0.0836
##      2        1.0590             nan     1.0000    0.0018
##      3        1.0146             nan     1.0000    0.0147
##      4        0.9797             nan     1.0000    0.0036
##      5        0.9782             nan     1.0000   -0.0207
##      6        0.9654             nan     1.0000   -0.0100
##      7        0.9571             nan     1.0000   -0.0160
##      8        0.9443             nan     1.0000    0.0022
##      9        0.9294             nan     1.0000    0.0032
##     10        0.9490             nan     1.0000   -0.0420
##     20        1.6920             nan     1.0000   -0.7357
##     40 11172874824984.1309             nan     1.0000   -0.0195
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1202             nan     1.0000    0.0609
##      2        1.0518             nan     1.0000    0.0140
##      3        0.9826             nan     1.0000    0.0348
##      4        0.9664             nan     1.0000    0.0016
##      5        0.9374             nan     1.0000   -0.0103
##      6        0.9540             nan     1.0000   -0.0256
##      7        0.9295             nan     1.0000    0.0053
##      8        0.9391             nan     1.0000   -0.0252
##      9        0.9458             nan     1.0000   -0.0207
##     10        0.9524             nan     1.0000   -0.0315
##     20        0.9166             nan     1.0000   -0.0327
##     40 80297868352.9160             nan     1.0000   -0.0212
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0514             nan     1.0000    0.1097
##      2        1.0170             nan     1.0000   -0.0311
##      3        0.9656             nan     1.0000    0.0171
##      4        0.9523             nan     1.0000   -0.0106
##      5        0.9618             nan     1.0000   -0.0398
##      6        0.9538             nan     1.0000   -0.0202
##      7        0.9552             nan     1.0000   -0.0291
##      8        0.9477             nan     1.0000   -0.0141
##      9        0.9567             nan     1.0000   -0.0413
##     10        0.9716             nan     1.0000   -0.0602
##     20        0.9391             nan     1.0000   -0.0626
##     40        0.9278             nan     1.0000   -0.0644
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0526             nan     1.0000    0.1003
##      2        0.9848             nan     1.0000    0.0182
##      3        0.9651             nan     1.0000   -0.0158
##      4        0.9703             nan     1.0000   -0.0407
##      5        0.9456             nan     1.0000   -0.0090
##      6        0.9221             nan     1.0000   -0.0082
##      7        0.9176             nan     1.0000   -0.0203
##      8        0.9008             nan     1.0000   -0.0117
##      9        1.0421             nan     1.0000   -0.1560
##     10        1.2935             nan     1.0000   -0.3296
##     20   275001.8140             nan     1.0000   -0.0451
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0573             nan     1.0000    0.1231
##      2        0.9832             nan     1.0000    0.0245
##      3        0.9501             nan     1.0000   -0.0063
##      4        0.9667             nan     1.0000   -0.0430
##      5        0.9514             nan     1.0000   -0.0022
##      6        0.9419             nan     1.0000   -0.0248
##      7        0.9641             nan     1.0000   -0.0642
##      8        0.9634             nan     1.0000   -0.0348
##      9        0.9688             nan     1.0000   -0.0324
##     10        0.9394             nan     1.0000   -0.0052
##     20        0.8747             nan     1.0000   -0.0506
##     40        1.9035             nan     1.0000   -0.0009
##     60 78210123774891.4531             nan     1.0000   -3.2459
##     80 78210123774892.3594             nan     1.0000   -0.0210
##    100 78210123774922.4219             nan     1.0000    0.1194
##    120 78210123835107.1406             nan     1.0000 -60098.9314
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0796             nan     1.0000    0.0523
##      2        1.0059             nan     1.0000   -0.0062
##      3        0.9606             nan     1.0000    0.0078
##      4        0.9283             nan     1.0000   -0.0077
##      5        0.9192             nan     1.0000   -0.0223
##      6        0.9392             nan     1.0000   -0.0689
##      7        0.9133             nan     1.0000   -0.0417
##      8        1.1763             nan     1.0000   -0.3099
##      9        1.1816             nan     1.0000   -0.0368
##     10        1.1537             nan     1.0000   -0.0126
##     20        4.0216             nan     1.0000   -2.7921
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0163             nan     1.0000    0.1231
##      2        0.9453             nan     1.0000    0.0186
##      3        0.9612             nan     1.0000   -0.0618
##      4        0.9313             nan     1.0000   -0.0134
##      5        0.9028             nan     1.0000   -0.0248
##      6        0.8789             nan     1.0000   -0.0273
##      7        0.8702             nan     1.0000   -0.0342
##      8        0.8619             nan     1.0000   -0.0262
##      9        0.8577             nan     1.0000   -0.0242
##     10        0.8674             nan     1.0000   -0.0653
##     20        1.6924             nan     1.0000   -0.0568
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0015             nan     1.0000    0.1490
##      2        0.9260             nan     1.0000    0.0029
##      3        0.9414             nan     1.0000   -0.0716
##      4        0.9520             nan     1.0000   -0.0420
##      5        0.9515             nan     1.0000   -0.0600
##      6        0.9439             nan     1.0000   -0.0347
##      7        0.8952             nan     1.0000   -0.0063
##      8        0.8809             nan     1.0000   -0.0256
##      9        0.8827             nan     1.0000   -0.0390
##     10        1.0275             nan     1.0000   -0.1530
##     20 29353799.6357             nan     1.0000   -0.0184
##     40 29353799.5020             nan     1.0000    0.0194
##     60 29353800.9091             nan     1.0000   -0.2188
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0001
##      3        1.2922             nan     0.0010    0.0001
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2863             nan     0.0010    0.0001
##     40        1.2794             nan     0.0010    0.0001
##     60        1.2731             nan     0.0010    0.0002
##     80        1.2667             nan     0.0010    0.0002
##    100        1.2607             nan     0.0010    0.0001
##    120        1.2547             nan     0.0010    0.0001
##    140        1.2488             nan     0.0010    0.0001
##    160        1.2433             nan     0.0010    0.0001
##    180        1.2380             nan     0.0010    0.0001
##    200        1.2329             nan     0.0010    0.0001
##    220        1.2280             nan     0.0010    0.0001
##    240        1.2230             nan     0.0010    0.0001
##    260        1.2184             nan     0.0010    0.0001
##    280        1.2139             nan     0.0010    0.0001
##    300        1.2095             nan     0.0010    0.0001
##    320        1.2050             nan     0.0010    0.0001
##    340        1.2007             nan     0.0010    0.0001
##    360        1.1966             nan     0.0010    0.0001
##    380        1.1925             nan     0.0010    0.0001
##    400        1.1887             nan     0.0010    0.0001
##    420        1.1847             nan     0.0010    0.0001
##    440        1.1810             nan     0.0010    0.0001
##    460        1.1773             nan     0.0010    0.0001
##    480        1.1737             nan     0.0010    0.0001
##    500        1.1701             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0001
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0001
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2862             nan     0.0010    0.0001
##     40        1.2796             nan     0.0010    0.0002
##     60        1.2729             nan     0.0010    0.0001
##     80        1.2668             nan     0.0010    0.0002
##    100        1.2607             nan     0.0010    0.0002
##    120        1.2550             nan     0.0010    0.0001
##    140        1.2492             nan     0.0010    0.0001
##    160        1.2438             nan     0.0010    0.0001
##    180        1.2385             nan     0.0010    0.0001
##    200        1.2330             nan     0.0010    0.0001
##    220        1.2281             nan     0.0010    0.0001
##    240        1.2233             nan     0.0010    0.0001
##    260        1.2185             nan     0.0010    0.0001
##    280        1.2137             nan     0.0010    0.0001
##    300        1.2092             nan     0.0010    0.0001
##    320        1.2048             nan     0.0010    0.0001
##    340        1.2005             nan     0.0010    0.0001
##    360        1.1963             nan     0.0010    0.0001
##    380        1.1923             nan     0.0010    0.0001
##    400        1.1883             nan     0.0010    0.0001
##    420        1.1845             nan     0.0010    0.0001
##    440        1.1807             nan     0.0010    0.0001
##    460        1.1770             nan     0.0010    0.0001
##    480        1.1732             nan     0.0010    0.0001
##    500        1.1695             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0001
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2862             nan     0.0010    0.0002
##     40        1.2794             nan     0.0010    0.0002
##     60        1.2728             nan     0.0010    0.0001
##     80        1.2664             nan     0.0010    0.0001
##    100        1.2604             nan     0.0010    0.0001
##    120        1.2542             nan     0.0010    0.0001
##    140        1.2484             nan     0.0010    0.0001
##    160        1.2432             nan     0.0010    0.0001
##    180        1.2379             nan     0.0010    0.0001
##    200        1.2328             nan     0.0010    0.0001
##    220        1.2276             nan     0.0010    0.0001
##    240        1.2226             nan     0.0010    0.0001
##    260        1.2178             nan     0.0010    0.0001
##    280        1.2132             nan     0.0010    0.0001
##    300        1.2088             nan     0.0010    0.0001
##    320        1.2042             nan     0.0010    0.0001
##    340        1.1999             nan     0.0010    0.0001
##    360        1.1958             nan     0.0010    0.0001
##    380        1.1919             nan     0.0010    0.0001
##    400        1.1879             nan     0.0010    0.0001
##    420        1.1840             nan     0.0010    0.0001
##    440        1.1802             nan     0.0010    0.0001
##    460        1.1766             nan     0.0010    0.0001
##    480        1.1729             nan     0.0010    0.0001
##    500        1.1694             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2902             nan     0.0010    0.0002
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2843             nan     0.0010    0.0002
##     40        1.2752             nan     0.0010    0.0002
##     60        1.2663             nan     0.0010    0.0002
##     80        1.2580             nan     0.0010    0.0002
##    100        1.2496             nan     0.0010    0.0001
##    120        1.2417             nan     0.0010    0.0001
##    140        1.2341             nan     0.0010    0.0002
##    160        1.2268             nan     0.0010    0.0001
##    180        1.2194             nan     0.0010    0.0002
##    200        1.2124             nan     0.0010    0.0001
##    220        1.2055             nan     0.0010    0.0002
##    240        1.1991             nan     0.0010    0.0001
##    260        1.1927             nan     0.0010    0.0002
##    280        1.1865             nan     0.0010    0.0001
##    300        1.1804             nan     0.0010    0.0001
##    320        1.1746             nan     0.0010    0.0001
##    340        1.1687             nan     0.0010    0.0001
##    360        1.1634             nan     0.0010    0.0001
##    380        1.1579             nan     0.0010    0.0001
##    400        1.1525             nan     0.0010    0.0001
##    420        1.1470             nan     0.0010    0.0001
##    440        1.1419             nan     0.0010    0.0001
##    460        1.1369             nan     0.0010    0.0001
##    480        1.1320             nan     0.0010    0.0001
##    500        1.1273             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2921             nan     0.0010    0.0002
##      4        1.2916             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2907             nan     0.0010    0.0002
##      7        1.2902             nan     0.0010    0.0002
##      8        1.2898             nan     0.0010    0.0002
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2843             nan     0.0010    0.0002
##     40        1.2752             nan     0.0010    0.0002
##     60        1.2665             nan     0.0010    0.0002
##     80        1.2579             nan     0.0010    0.0002
##    100        1.2499             nan     0.0010    0.0002
##    120        1.2420             nan     0.0010    0.0002
##    140        1.2344             nan     0.0010    0.0002
##    160        1.2269             nan     0.0010    0.0001
##    180        1.2196             nan     0.0010    0.0002
##    200        1.2127             nan     0.0010    0.0001
##    220        1.2059             nan     0.0010    0.0001
##    240        1.1994             nan     0.0010    0.0001
##    260        1.1931             nan     0.0010    0.0002
##    280        1.1871             nan     0.0010    0.0001
##    300        1.1810             nan     0.0010    0.0001
##    320        1.1751             nan     0.0010    0.0001
##    340        1.1694             nan     0.0010    0.0001
##    360        1.1638             nan     0.0010    0.0001
##    380        1.1582             nan     0.0010    0.0001
##    400        1.1529             nan     0.0010    0.0001
##    420        1.1476             nan     0.0010    0.0001
##    440        1.1426             nan     0.0010    0.0001
##    460        1.1376             nan     0.0010    0.0001
##    480        1.1326             nan     0.0010    0.0001
##    500        1.1281             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2840             nan     0.0010    0.0002
##     40        1.2748             nan     0.0010    0.0002
##     60        1.2661             nan     0.0010    0.0002
##     80        1.2577             nan     0.0010    0.0002
##    100        1.2496             nan     0.0010    0.0001
##    120        1.2418             nan     0.0010    0.0002
##    140        1.2342             nan     0.0010    0.0002
##    160        1.2267             nan     0.0010    0.0002
##    180        1.2194             nan     0.0010    0.0002
##    200        1.2124             nan     0.0010    0.0001
##    220        1.2056             nan     0.0010    0.0002
##    240        1.1992             nan     0.0010    0.0002
##    260        1.1928             nan     0.0010    0.0001
##    280        1.1866             nan     0.0010    0.0001
##    300        1.1805             nan     0.0010    0.0001
##    320        1.1745             nan     0.0010    0.0001
##    340        1.1687             nan     0.0010    0.0001
##    360        1.1629             nan     0.0010    0.0001
##    380        1.1576             nan     0.0010    0.0001
##    400        1.1522             nan     0.0010    0.0001
##    420        1.1470             nan     0.0010    0.0001
##    440        1.1419             nan     0.0010    0.0001
##    460        1.1369             nan     0.0010    0.0001
##    480        1.1321             nan     0.0010    0.0001
##    500        1.1273             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0003
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2895             nan     0.0010    0.0003
##      8        1.2889             nan     0.0010    0.0002
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2828             nan     0.0010    0.0002
##     40        1.2722             nan     0.0010    0.0002
##     60        1.2620             nan     0.0010    0.0002
##     80        1.2524             nan     0.0010    0.0002
##    100        1.2428             nan     0.0010    0.0002
##    120        1.2336             nan     0.0010    0.0002
##    140        1.2247             nan     0.0010    0.0002
##    160        1.2158             nan     0.0010    0.0002
##    180        1.2075             nan     0.0010    0.0002
##    200        1.1994             nan     0.0010    0.0002
##    220        1.1920             nan     0.0010    0.0002
##    240        1.1844             nan     0.0010    0.0001
##    260        1.1769             nan     0.0010    0.0002
##    280        1.1697             nan     0.0010    0.0001
##    300        1.1626             nan     0.0010    0.0001
##    320        1.1556             nan     0.0010    0.0002
##    340        1.1492             nan     0.0010    0.0001
##    360        1.1429             nan     0.0010    0.0001
##    380        1.1366             nan     0.0010    0.0001
##    400        1.1305             nan     0.0010    0.0001
##    420        1.1246             nan     0.0010    0.0001
##    440        1.1187             nan     0.0010    0.0001
##    460        1.1131             nan     0.0010    0.0001
##    480        1.1073             nan     0.0010    0.0001
##    500        1.1021             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2895             nan     0.0010    0.0002
##      8        1.2890             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0002
##     10        1.2880             nan     0.0010    0.0003
##     20        1.2828             nan     0.0010    0.0002
##     40        1.2723             nan     0.0010    0.0003
##     60        1.2620             nan     0.0010    0.0002
##     80        1.2521             nan     0.0010    0.0002
##    100        1.2426             nan     0.0010    0.0002
##    120        1.2332             nan     0.0010    0.0002
##    140        1.2244             nan     0.0010    0.0001
##    160        1.2158             nan     0.0010    0.0002
##    180        1.2074             nan     0.0010    0.0002
##    200        1.1993             nan     0.0010    0.0001
##    220        1.1912             nan     0.0010    0.0002
##    240        1.1837             nan     0.0010    0.0001
##    260        1.1763             nan     0.0010    0.0002
##    280        1.1690             nan     0.0010    0.0002
##    300        1.1621             nan     0.0010    0.0002
##    320        1.1553             nan     0.0010    0.0002
##    340        1.1487             nan     0.0010    0.0001
##    360        1.1423             nan     0.0010    0.0001
##    380        1.1361             nan     0.0010    0.0001
##    400        1.1300             nan     0.0010    0.0001
##    420        1.1237             nan     0.0010    0.0001
##    440        1.1181             nan     0.0010    0.0001
##    460        1.1122             nan     0.0010    0.0001
##    480        1.1067             nan     0.0010    0.0001
##    500        1.1015             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0003
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2895             nan     0.0010    0.0003
##      8        1.2888             nan     0.0010    0.0003
##      9        1.2883             nan     0.0010    0.0002
##     10        1.2877             nan     0.0010    0.0002
##     20        1.2824             nan     0.0010    0.0002
##     40        1.2719             nan     0.0010    0.0002
##     60        1.2617             nan     0.0010    0.0002
##     80        1.2517             nan     0.0010    0.0002
##    100        1.2425             nan     0.0010    0.0002
##    120        1.2330             nan     0.0010    0.0002
##    140        1.2240             nan     0.0010    0.0002
##    160        1.2153             nan     0.0010    0.0002
##    180        1.2071             nan     0.0010    0.0002
##    200        1.1991             nan     0.0010    0.0002
##    220        1.1911             nan     0.0010    0.0002
##    240        1.1835             nan     0.0010    0.0002
##    260        1.1762             nan     0.0010    0.0002
##    280        1.1689             nan     0.0010    0.0002
##    300        1.1623             nan     0.0010    0.0001
##    320        1.1554             nan     0.0010    0.0001
##    340        1.1487             nan     0.0010    0.0001
##    360        1.1422             nan     0.0010    0.0001
##    380        1.1360             nan     0.0010    0.0001
##    400        1.1300             nan     0.0010    0.0001
##    420        1.1240             nan     0.0010    0.0001
##    440        1.1182             nan     0.0010    0.0001
##    460        1.1124             nan     0.0010    0.0001
##    480        1.1069             nan     0.0010    0.0001
##    500        1.1016             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2576             nan     0.1000    0.0167
##      2        1.2271             nan     0.1000    0.0118
##      3        1.2029             nan     0.1000    0.0101
##      4        1.1832             nan     0.1000    0.0099
##      5        1.1649             nan     0.1000    0.0070
##      6        1.1466             nan     0.1000    0.0073
##      7        1.1337             nan     0.1000    0.0048
##      8        1.1194             nan     0.1000    0.0052
##      9        1.1046             nan     0.1000    0.0058
##     10        1.0940             nan     0.1000    0.0037
##     20        1.0073             nan     0.1000    0.0017
##     40        0.9201             nan     0.1000    0.0004
##     60        0.8772             nan     0.1000   -0.0009
##     80        0.8476             nan     0.1000   -0.0003
##    100        0.8271             nan     0.1000   -0.0007
##    120        0.8126             nan     0.1000   -0.0005
##    140        0.8007             nan     0.1000   -0.0015
##    160        0.7921             nan     0.1000   -0.0013
##    180        0.7790             nan     0.1000   -0.0003
##    200        0.7704             nan     0.1000   -0.0023
##    220        0.7638             nan     0.1000   -0.0001
##    240        0.7560             nan     0.1000   -0.0008
##    260        0.7502             nan     0.1000   -0.0018
##    280        0.7436             nan     0.1000   -0.0006
##    300        0.7368             nan     0.1000   -0.0003
##    320        0.7315             nan     0.1000   -0.0007
##    340        0.7253             nan     0.1000   -0.0006
##    360        0.7193             nan     0.1000   -0.0010
##    380        0.7127             nan     0.1000   -0.0009
##    400        0.7064             nan     0.1000   -0.0012
##    420        0.7031             nan     0.1000   -0.0007
##    440        0.6988             nan     0.1000   -0.0013
##    460        0.6958             nan     0.1000   -0.0004
##    480        0.6924             nan     0.1000   -0.0021
##    500        0.6867             nan     0.1000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2593             nan     0.1000    0.0145
##      2        1.2273             nan     0.1000    0.0151
##      3        1.2031             nan     0.1000    0.0112
##      4        1.1859             nan     0.1000    0.0086
##      5        1.1683             nan     0.1000    0.0069
##      6        1.1498             nan     0.1000    0.0068
##      7        1.1335             nan     0.1000    0.0044
##      8        1.1220             nan     0.1000    0.0034
##      9        1.1077             nan     0.1000    0.0063
##     10        1.0956             nan     0.1000    0.0055
##     20        1.0017             nan     0.1000    0.0013
##     40        0.9203             nan     0.1000    0.0003
##     60        0.8806             nan     0.1000   -0.0006
##     80        0.8484             nan     0.1000   -0.0012
##    100        0.8280             nan     0.1000   -0.0013
##    120        0.8121             nan     0.1000   -0.0011
##    140        0.7993             nan     0.1000   -0.0013
##    160        0.7904             nan     0.1000   -0.0012
##    180        0.7815             nan     0.1000   -0.0012
##    200        0.7712             nan     0.1000   -0.0010
##    220        0.7637             nan     0.1000   -0.0009
##    240        0.7573             nan     0.1000   -0.0010
##    260        0.7497             nan     0.1000   -0.0005
##    280        0.7443             nan     0.1000   -0.0006
##    300        0.7365             nan     0.1000   -0.0009
##    320        0.7333             nan     0.1000   -0.0027
##    340        0.7257             nan     0.1000   -0.0010
##    360        0.7195             nan     0.1000   -0.0012
##    380        0.7141             nan     0.1000   -0.0020
##    400        0.7090             nan     0.1000   -0.0006
##    420        0.7041             nan     0.1000   -0.0007
##    440        0.6999             nan     0.1000   -0.0006
##    460        0.6946             nan     0.1000   -0.0011
##    480        0.6912             nan     0.1000   -0.0012
##    500        0.6892             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2593             nan     0.1000    0.0160
##      2        1.2314             nan     0.1000    0.0137
##      3        1.2088             nan     0.1000    0.0104
##      4        1.1914             nan     0.1000    0.0066
##      5        1.1701             nan     0.1000    0.0102
##      6        1.1523             nan     0.1000    0.0083
##      7        1.1351             nan     0.1000    0.0067
##      8        1.1241             nan     0.1000    0.0047
##      9        1.1092             nan     0.1000    0.0064
##     10        1.0990             nan     0.1000    0.0032
##     20        1.0091             nan     0.1000    0.0019
##     40        0.9221             nan     0.1000    0.0014
##     60        0.8759             nan     0.1000   -0.0004
##     80        0.8496             nan     0.1000   -0.0006
##    100        0.8294             nan     0.1000   -0.0012
##    120        0.8165             nan     0.1000   -0.0013
##    140        0.8027             nan     0.1000   -0.0008
##    160        0.7914             nan     0.1000   -0.0013
##    180        0.7823             nan     0.1000   -0.0007
##    200        0.7734             nan     0.1000   -0.0015
##    220        0.7657             nan     0.1000   -0.0010
##    240        0.7592             nan     0.1000   -0.0003
##    260        0.7543             nan     0.1000   -0.0005
##    280        0.7488             nan     0.1000   -0.0010
##    300        0.7446             nan     0.1000   -0.0012
##    320        0.7377             nan     0.1000   -0.0012
##    340        0.7306             nan     0.1000   -0.0004
##    360        0.7246             nan     0.1000   -0.0002
##    380        0.7198             nan     0.1000   -0.0007
##    400        0.7155             nan     0.1000   -0.0018
##    420        0.7104             nan     0.1000   -0.0012
##    440        0.7064             nan     0.1000   -0.0007
##    460        0.7014             nan     0.1000   -0.0012
##    480        0.6968             nan     0.1000   -0.0008
##    500        0.6908             nan     0.1000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2462             nan     0.1000    0.0222
##      2        1.2075             nan     0.1000    0.0161
##      3        1.1733             nan     0.1000    0.0156
##      4        1.1486             nan     0.1000    0.0102
##      5        1.1224             nan     0.1000    0.0100
##      6        1.0985             nan     0.1000    0.0099
##      7        1.0756             nan     0.1000    0.0074
##      8        1.0610             nan     0.1000    0.0046
##      9        1.0451             nan     0.1000    0.0053
##     10        1.0295             nan     0.1000    0.0065
##     20        0.9318             nan     0.1000    0.0020
##     40        0.8499             nan     0.1000   -0.0006
##     60        0.7969             nan     0.1000   -0.0004
##     80        0.7642             nan     0.1000   -0.0012
##    100        0.7351             nan     0.1000   -0.0008
##    120        0.7067             nan     0.1000   -0.0014
##    140        0.6836             nan     0.1000   -0.0020
##    160        0.6596             nan     0.1000   -0.0011
##    180        0.6400             nan     0.1000   -0.0003
##    200        0.6216             nan     0.1000   -0.0013
##    220        0.6080             nan     0.1000    0.0004
##    240        0.5916             nan     0.1000   -0.0010
##    260        0.5745             nan     0.1000    0.0002
##    280        0.5610             nan     0.1000   -0.0004
##    300        0.5465             nan     0.1000   -0.0009
##    320        0.5341             nan     0.1000   -0.0011
##    340        0.5202             nan     0.1000   -0.0012
##    360        0.5056             nan     0.1000   -0.0007
##    380        0.4918             nan     0.1000   -0.0013
##    400        0.4783             nan     0.1000   -0.0003
##    420        0.4684             nan     0.1000   -0.0005
##    440        0.4597             nan     0.1000   -0.0011
##    460        0.4488             nan     0.1000   -0.0005
##    480        0.4377             nan     0.1000   -0.0012
##    500        0.4289             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2437             nan     0.1000    0.0214
##      2        1.2081             nan     0.1000    0.0156
##      3        1.1749             nan     0.1000    0.0142
##      4        1.1472             nan     0.1000    0.0110
##      5        1.1228             nan     0.1000    0.0088
##      6        1.0990             nan     0.1000    0.0098
##      7        1.0817             nan     0.1000    0.0061
##      8        1.0644             nan     0.1000    0.0070
##      9        1.0492             nan     0.1000    0.0043
##     10        1.0351             nan     0.1000    0.0047
##     20        0.9313             nan     0.1000    0.0018
##     40        0.8439             nan     0.1000   -0.0010
##     60        0.7982             nan     0.1000   -0.0004
##     80        0.7543             nan     0.1000   -0.0006
##    100        0.7272             nan     0.1000   -0.0003
##    120        0.7049             nan     0.1000   -0.0017
##    140        0.6818             nan     0.1000   -0.0018
##    160        0.6630             nan     0.1000   -0.0005
##    180        0.6438             nan     0.1000   -0.0014
##    200        0.6232             nan     0.1000   -0.0009
##    220        0.6044             nan     0.1000   -0.0008
##    240        0.5887             nan     0.1000   -0.0015
##    260        0.5706             nan     0.1000   -0.0011
##    280        0.5557             nan     0.1000   -0.0006
##    300        0.5410             nan     0.1000   -0.0004
##    320        0.5264             nan     0.1000   -0.0011
##    340        0.5158             nan     0.1000   -0.0006
##    360        0.5044             nan     0.1000   -0.0004
##    380        0.4897             nan     0.1000   -0.0017
##    400        0.4779             nan     0.1000   -0.0008
##    420        0.4681             nan     0.1000   -0.0013
##    440        0.4598             nan     0.1000   -0.0014
##    460        0.4464             nan     0.1000   -0.0005
##    480        0.4374             nan     0.1000   -0.0005
##    500        0.4283             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2434             nan     0.1000    0.0237
##      2        1.2075             nan     0.1000    0.0152
##      3        1.1752             nan     0.1000    0.0140
##      4        1.1486             nan     0.1000    0.0112
##      5        1.1248             nan     0.1000    0.0106
##      6        1.1076             nan     0.1000    0.0066
##      7        1.0840             nan     0.1000    0.0095
##      8        1.0709             nan     0.1000    0.0049
##      9        1.0569             nan     0.1000    0.0056
##     10        1.0428             nan     0.1000    0.0057
##     20        0.9407             nan     0.1000    0.0012
##     40        0.8462             nan     0.1000    0.0008
##     60        0.7966             nan     0.1000   -0.0017
##     80        0.7595             nan     0.1000   -0.0020
##    100        0.7278             nan     0.1000   -0.0023
##    120        0.7027             nan     0.1000   -0.0001
##    140        0.6786             nan     0.1000   -0.0015
##    160        0.6559             nan     0.1000   -0.0006
##    180        0.6375             nan     0.1000   -0.0021
##    200        0.6216             nan     0.1000   -0.0005
##    220        0.6037             nan     0.1000   -0.0012
##    240        0.5825             nan     0.1000   -0.0006
##    260        0.5655             nan     0.1000   -0.0009
##    280        0.5505             nan     0.1000   -0.0010
##    300        0.5384             nan     0.1000   -0.0011
##    320        0.5283             nan     0.1000   -0.0008
##    340        0.5117             nan     0.1000   -0.0010
##    360        0.4987             nan     0.1000   -0.0005
##    380        0.4870             nan     0.1000   -0.0008
##    400        0.4748             nan     0.1000   -0.0014
##    420        0.4624             nan     0.1000   -0.0007
##    440        0.4532             nan     0.1000   -0.0008
##    460        0.4444             nan     0.1000   -0.0011
##    480        0.4326             nan     0.1000   -0.0002
##    500        0.4225             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2415             nan     0.1000    0.0223
##      2        1.1976             nan     0.1000    0.0184
##      3        1.1649             nan     0.1000    0.0121
##      4        1.1275             nan     0.1000    0.0170
##      5        1.0966             nan     0.1000    0.0108
##      6        1.0689             nan     0.1000    0.0119
##      7        1.0501             nan     0.1000    0.0068
##      8        1.0316             nan     0.1000    0.0083
##      9        1.0092             nan     0.1000    0.0078
##     10        0.9947             nan     0.1000    0.0031
##     20        0.8865             nan     0.1000    0.0022
##     40        0.7911             nan     0.1000   -0.0019
##     60        0.7327             nan     0.1000   -0.0013
##     80        0.6851             nan     0.1000   -0.0007
##    100        0.6445             nan     0.1000   -0.0024
##    120        0.6097             nan     0.1000   -0.0002
##    140        0.5777             nan     0.1000   -0.0021
##    160        0.5490             nan     0.1000   -0.0009
##    180        0.5220             nan     0.1000   -0.0017
##    200        0.4974             nan     0.1000   -0.0003
##    220        0.4747             nan     0.1000   -0.0009
##    240        0.4547             nan     0.1000   -0.0017
##    260        0.4334             nan     0.1000   -0.0004
##    280        0.4179             nan     0.1000   -0.0007
##    300        0.3999             nan     0.1000   -0.0006
##    320        0.3814             nan     0.1000   -0.0008
##    340        0.3667             nan     0.1000   -0.0011
##    360        0.3530             nan     0.1000   -0.0014
##    380        0.3416             nan     0.1000   -0.0012
##    400        0.3291             nan     0.1000   -0.0004
##    420        0.3171             nan     0.1000   -0.0011
##    440        0.3039             nan     0.1000   -0.0008
##    460        0.2930             nan     0.1000   -0.0009
##    480        0.2826             nan     0.1000   -0.0004
##    500        0.2736             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2404             nan     0.1000    0.0250
##      2        1.1938             nan     0.1000    0.0193
##      3        1.1569             nan     0.1000    0.0130
##      4        1.1219             nan     0.1000    0.0145
##      5        1.0947             nan     0.1000    0.0110
##      6        1.0730             nan     0.1000    0.0086
##      7        1.0483             nan     0.1000    0.0084
##      8        1.0262             nan     0.1000    0.0094
##      9        1.0089             nan     0.1000    0.0057
##     10        0.9909             nan     0.1000    0.0061
##     20        0.8878             nan     0.1000    0.0008
##     40        0.7905             nan     0.1000   -0.0004
##     60        0.7330             nan     0.1000   -0.0022
##     80        0.6905             nan     0.1000   -0.0013
##    100        0.6529             nan     0.1000   -0.0006
##    120        0.6143             nan     0.1000   -0.0006
##    140        0.5833             nan     0.1000   -0.0016
##    160        0.5491             nan     0.1000   -0.0012
##    180        0.5193             nan     0.1000   -0.0009
##    200        0.4940             nan     0.1000   -0.0009
##    220        0.4722             nan     0.1000   -0.0001
##    240        0.4463             nan     0.1000   -0.0006
##    260        0.4278             nan     0.1000   -0.0005
##    280        0.4084             nan     0.1000   -0.0007
##    300        0.3912             nan     0.1000   -0.0010
##    320        0.3808             nan     0.1000   -0.0016
##    340        0.3649             nan     0.1000   -0.0011
##    360        0.3497             nan     0.1000   -0.0005
##    380        0.3367             nan     0.1000   -0.0008
##    400        0.3246             nan     0.1000   -0.0003
##    420        0.3134             nan     0.1000   -0.0013
##    440        0.3020             nan     0.1000   -0.0005
##    460        0.2897             nan     0.1000   -0.0003
##    480        0.2779             nan     0.1000   -0.0008
##    500        0.2680             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2328             nan     0.1000    0.0259
##      2        1.1934             nan     0.1000    0.0160
##      3        1.1601             nan     0.1000    0.0142
##      4        1.1327             nan     0.1000    0.0067
##      5        1.1063             nan     0.1000    0.0115
##      6        1.0807             nan     0.1000    0.0104
##      7        1.0566             nan     0.1000    0.0092
##      8        1.0413             nan     0.1000    0.0027
##      9        1.0202             nan     0.1000    0.0074
##     10        1.0022             nan     0.1000    0.0060
##     20        0.9020             nan     0.1000    0.0005
##     40        0.8024             nan     0.1000   -0.0011
##     60        0.7452             nan     0.1000   -0.0017
##     80        0.6976             nan     0.1000   -0.0009
##    100        0.6581             nan     0.1000   -0.0024
##    120        0.6257             nan     0.1000   -0.0010
##    140        0.5903             nan     0.1000   -0.0009
##    160        0.5622             nan     0.1000   -0.0014
##    180        0.5365             nan     0.1000   -0.0016
##    200        0.5097             nan     0.1000   -0.0007
##    220        0.4928             nan     0.1000   -0.0006
##    240        0.4648             nan     0.1000   -0.0001
##    260        0.4455             nan     0.1000   -0.0003
##    280        0.4291             nan     0.1000   -0.0015
##    300        0.4100             nan     0.1000   -0.0016
##    320        0.3947             nan     0.1000   -0.0013
##    340        0.3801             nan     0.1000   -0.0011
##    360        0.3667             nan     0.1000   -0.0014
##    380        0.3498             nan     0.1000   -0.0007
##    400        0.3365             nan     0.1000   -0.0006
##    420        0.3212             nan     0.1000   -0.0005
##    440        0.3072             nan     0.1000   -0.0015
##    460        0.2960             nan     0.1000   -0.0002
##    480        0.2839             nan     0.1000   -0.0004
##    500        0.2735             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2356             nan     0.2000    0.0266
##      2        1.1848             nan     0.2000    0.0230
##      3        1.1462             nan     0.2000    0.0136
##      4        1.1219             nan     0.2000    0.0071
##      5        1.0966             nan     0.2000    0.0104
##      6        1.0698             nan     0.2000    0.0109
##      7        1.0512             nan     0.2000    0.0058
##      8        1.0309             nan     0.2000    0.0085
##      9        1.0201             nan     0.2000    0.0022
##     10        1.0045             nan     0.2000    0.0048
##     20        0.9214             nan     0.2000    0.0000
##     40        0.8641             nan     0.2000    0.0007
##     60        0.8190             nan     0.2000    0.0012
##     80        0.7922             nan     0.2000   -0.0039
##    100        0.7783             nan     0.2000   -0.0029
##    120        0.7614             nan     0.2000   -0.0007
##    140        0.7530             nan     0.2000   -0.0001
##    160        0.7349             nan     0.2000   -0.0006
##    180        0.7259             nan     0.2000   -0.0029
##    200        0.7128             nan     0.2000   -0.0029
##    220        0.7007             nan     0.2000   -0.0016
##    240        0.6906             nan     0.2000   -0.0025
##    260        0.6829             nan     0.2000   -0.0015
##    280        0.6732             nan     0.2000   -0.0019
##    300        0.6664             nan     0.2000   -0.0015
##    320        0.6620             nan     0.2000   -0.0009
##    340        0.6541             nan     0.2000   -0.0010
##    360        0.6469             nan     0.2000   -0.0027
##    380        0.6411             nan     0.2000   -0.0016
##    400        0.6361             nan     0.2000   -0.0018
##    420        0.6331             nan     0.2000   -0.0019
##    440        0.6308             nan     0.2000   -0.0032
##    460        0.6227             nan     0.2000   -0.0011
##    480        0.6150             nan     0.2000   -0.0008
##    500        0.6096             nan     0.2000   -0.0031
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2273             nan     0.2000    0.0298
##      2        1.1779             nan     0.2000    0.0215
##      3        1.1492             nan     0.2000    0.0115
##      4        1.1222             nan     0.2000    0.0112
##      5        1.0979             nan     0.2000    0.0111
##      6        1.0759             nan     0.2000    0.0061
##      7        1.0524             nan     0.2000    0.0102
##      8        1.0348             nan     0.2000    0.0061
##      9        1.0180             nan     0.2000    0.0058
##     10        1.0056             nan     0.2000    0.0032
##     20        0.9252             nan     0.2000   -0.0019
##     40        0.8487             nan     0.2000   -0.0028
##     60        0.8153             nan     0.2000   -0.0020
##     80        0.7924             nan     0.2000   -0.0002
##    100        0.7731             nan     0.2000   -0.0011
##    120        0.7589             nan     0.2000   -0.0011
##    140        0.7455             nan     0.2000   -0.0053
##    160        0.7310             nan     0.2000   -0.0013
##    180        0.7216             nan     0.2000   -0.0014
##    200        0.7064             nan     0.2000   -0.0003
##    220        0.6987             nan     0.2000   -0.0039
##    240        0.6880             nan     0.2000   -0.0021
##    260        0.6830             nan     0.2000   -0.0033
##    280        0.6742             nan     0.2000   -0.0038
##    300        0.6694             nan     0.2000   -0.0008
##    320        0.6608             nan     0.2000   -0.0016
##    340        0.6533             nan     0.2000   -0.0021
##    360        0.6468             nan     0.2000   -0.0005
##    380        0.6386             nan     0.2000   -0.0017
##    400        0.6313             nan     0.2000   -0.0010
##    420        0.6281             nan     0.2000   -0.0028
##    440        0.6211             nan     0.2000   -0.0014
##    460        0.6169             nan     0.2000   -0.0018
##    480        0.6122             nan     0.2000   -0.0030
##    500        0.6082             nan     0.2000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2303             nan     0.2000    0.0305
##      2        1.1835             nan     0.2000    0.0200
##      3        1.1525             nan     0.2000    0.0119
##      4        1.1249             nan     0.2000    0.0128
##      5        1.0937             nan     0.2000    0.0105
##      6        1.0744             nan     0.2000    0.0065
##      7        1.0530             nan     0.2000    0.0109
##      8        1.0331             nan     0.2000    0.0071
##      9        1.0204             nan     0.2000    0.0014
##     10        1.0072             nan     0.2000    0.0007
##     20        0.9175             nan     0.2000    0.0001
##     40        0.8511             nan     0.2000   -0.0005
##     60        0.8148             nan     0.2000   -0.0016
##     80        0.7924             nan     0.2000   -0.0012
##    100        0.7791             nan     0.2000    0.0000
##    120        0.7617             nan     0.2000   -0.0020
##    140        0.7495             nan     0.2000   -0.0013
##    160        0.7344             nan     0.2000   -0.0015
##    180        0.7212             nan     0.2000   -0.0014
##    200        0.7115             nan     0.2000   -0.0015
##    220        0.7035             nan     0.2000   -0.0013
##    240        0.6957             nan     0.2000   -0.0025
##    260        0.6874             nan     0.2000   -0.0020
##    280        0.6832             nan     0.2000   -0.0014
##    300        0.6735             nan     0.2000   -0.0013
##    320        0.6634             nan     0.2000   -0.0022
##    340        0.6561             nan     0.2000   -0.0019
##    360        0.6488             nan     0.2000   -0.0020
##    380        0.6405             nan     0.2000   -0.0011
##    400        0.6328             nan     0.2000   -0.0009
##    420        0.6246             nan     0.2000   -0.0028
##    440        0.6200             nan     0.2000   -0.0032
##    460        0.6173             nan     0.2000   -0.0029
##    480        0.6124             nan     0.2000   -0.0007
##    500        0.6086             nan     0.2000   -0.0028
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2025             nan     0.2000    0.0398
##      2        1.1367             nan     0.2000    0.0247
##      3        1.0847             nan     0.2000    0.0224
##      4        1.0504             nan     0.2000    0.0120
##      5        1.0243             nan     0.2000    0.0096
##      6        0.9991             nan     0.2000    0.0062
##      7        0.9756             nan     0.2000    0.0048
##      8        0.9630             nan     0.2000    0.0023
##      9        0.9456             nan     0.2000    0.0046
##     10        0.9323             nan     0.2000    0.0031
##     20        0.8367             nan     0.2000   -0.0032
##     40        0.7598             nan     0.2000   -0.0039
##     60        0.7023             nan     0.2000   -0.0005
##     80        0.6539             nan     0.2000   -0.0019
##    100        0.6148             nan     0.2000   -0.0017
##    120        0.5801             nan     0.2000   -0.0019
##    140        0.5558             nan     0.2000   -0.0032
##    160        0.5203             nan     0.2000   -0.0020
##    180        0.4980             nan     0.2000   -0.0021
##    200        0.4739             nan     0.2000   -0.0002
##    220        0.4529             nan     0.2000   -0.0016
##    240        0.4346             nan     0.2000   -0.0014
##    260        0.4178             nan     0.2000   -0.0015
##    280        0.3990             nan     0.2000   -0.0009
##    300        0.3859             nan     0.2000   -0.0013
##    320        0.3714             nan     0.2000   -0.0013
##    340        0.3564             nan     0.2000   -0.0033
##    360        0.3455             nan     0.2000   -0.0001
##    380        0.3309             nan     0.2000   -0.0015
##    400        0.3187             nan     0.2000   -0.0008
##    420        0.3048             nan     0.2000   -0.0007
##    440        0.2901             nan     0.2000   -0.0004
##    460        0.2801             nan     0.2000   -0.0012
##    480        0.2694             nan     0.2000   -0.0004
##    500        0.2586             nan     0.2000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2182             nan     0.2000    0.0336
##      2        1.1512             nan     0.2000    0.0327
##      3        1.1059             nan     0.2000    0.0188
##      4        1.0682             nan     0.2000    0.0152
##      5        1.0334             nan     0.2000    0.0171
##      6        1.0089             nan     0.2000    0.0078
##      7        0.9844             nan     0.2000    0.0064
##      8        0.9706             nan     0.2000    0.0044
##      9        0.9551             nan     0.2000    0.0026
##     10        0.9393             nan     0.2000    0.0048
##     20        0.8571             nan     0.2000   -0.0025
##     40        0.7628             nan     0.2000   -0.0052
##     60        0.7050             nan     0.2000   -0.0022
##     80        0.6449             nan     0.2000   -0.0004
##    100        0.6049             nan     0.2000   -0.0010
##    120        0.5770             nan     0.2000   -0.0044
##    140        0.5389             nan     0.2000   -0.0020
##    160        0.5102             nan     0.2000   -0.0016
##    180        0.4874             nan     0.2000   -0.0020
##    200        0.4676             nan     0.2000   -0.0012
##    220        0.4540             nan     0.2000   -0.0027
##    240        0.4378             nan     0.2000   -0.0036
##    260        0.4168             nan     0.2000   -0.0016
##    280        0.4029             nan     0.2000   -0.0019
##    300        0.3855             nan     0.2000   -0.0036
##    320        0.3703             nan     0.2000   -0.0029
##    340        0.3559             nan     0.2000   -0.0007
##    360        0.3389             nan     0.2000   -0.0002
##    380        0.3254             nan     0.2000   -0.0008
##    400        0.3144             nan     0.2000   -0.0008
##    420        0.3001             nan     0.2000   -0.0008
##    440        0.2898             nan     0.2000   -0.0008
##    460        0.2774             nan     0.2000   -0.0010
##    480        0.2662             nan     0.2000   -0.0004
##    500        0.2612             nan     0.2000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2094             nan     0.2000    0.0342
##      2        1.1450             nan     0.2000    0.0234
##      3        1.1026             nan     0.2000    0.0190
##      4        1.0650             nan     0.2000    0.0130
##      5        1.0327             nan     0.2000    0.0105
##      6        1.0118             nan     0.2000    0.0026
##      7        0.9926             nan     0.2000    0.0027
##      8        0.9713             nan     0.2000    0.0067
##      9        0.9485             nan     0.2000    0.0059
##     10        0.9322             nan     0.2000    0.0032
##     20        0.8501             nan     0.2000   -0.0022
##     40        0.7628             nan     0.2000   -0.0008
##     60        0.7034             nan     0.2000   -0.0031
##     80        0.6661             nan     0.2000   -0.0014
##    100        0.6281             nan     0.2000   -0.0008
##    120        0.6008             nan     0.2000   -0.0035
##    140        0.5717             nan     0.2000   -0.0018
##    160        0.5458             nan     0.2000   -0.0040
##    180        0.5244             nan     0.2000   -0.0014
##    200        0.5016             nan     0.2000   -0.0022
##    220        0.4832             nan     0.2000   -0.0034
##    240        0.4573             nan     0.2000   -0.0025
##    260        0.4385             nan     0.2000   -0.0018
##    280        0.4224             nan     0.2000   -0.0011
##    300        0.3995             nan     0.2000   -0.0016
##    320        0.3827             nan     0.2000   -0.0005
##    340        0.3704             nan     0.2000   -0.0021
##    360        0.3496             nan     0.2000   -0.0014
##    380        0.3341             nan     0.2000   -0.0010
##    400        0.3199             nan     0.2000   -0.0019
##    420        0.3051             nan     0.2000   -0.0005
##    440        0.2934             nan     0.2000    0.0001
##    460        0.2827             nan     0.2000   -0.0006
##    480        0.2742             nan     0.2000   -0.0006
##    500        0.2610             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1927             nan     0.2000    0.0472
##      2        1.1304             nan     0.2000    0.0232
##      3        1.0794             nan     0.2000    0.0249
##      4        1.0331             nan     0.2000    0.0177
##      5        0.9953             nan     0.2000    0.0113
##      6        0.9685             nan     0.2000    0.0056
##      7        0.9465             nan     0.2000    0.0051
##      8        0.9246             nan     0.2000    0.0055
##      9        0.9063             nan     0.2000    0.0028
##     10        0.8932             nan     0.2000    0.0021
##     20        0.8026             nan     0.2000   -0.0013
##     40        0.6931             nan     0.2000   -0.0038
##     60        0.6244             nan     0.2000   -0.0002
##     80        0.5654             nan     0.2000   -0.0029
##    100        0.5178             nan     0.2000   -0.0019
##    120        0.4737             nan     0.2000   -0.0021
##    140        0.4302             nan     0.2000   -0.0014
##    160        0.3943             nan     0.2000   -0.0030
##    180        0.3597             nan     0.2000   -0.0003
##    200        0.3338             nan     0.2000   -0.0024
##    220        0.3096             nan     0.2000   -0.0018
##    240        0.2873             nan     0.2000   -0.0014
##    260        0.2678             nan     0.2000   -0.0013
##    280        0.2476             nan     0.2000   -0.0016
##    300        0.2299             nan     0.2000   -0.0011
##    320        0.2146             nan     0.2000   -0.0015
##    340        0.1991             nan     0.2000   -0.0011
##    360        0.1875             nan     0.2000   -0.0002
##    380        0.1761             nan     0.2000   -0.0005
##    400        0.1663             nan     0.2000   -0.0013
##    420        0.1536             nan     0.2000   -0.0005
##    440        0.1439             nan     0.2000   -0.0008
##    460        0.1351             nan     0.2000   -0.0005
##    480        0.1250             nan     0.2000   -0.0006
##    500        0.1180             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1836             nan     0.2000    0.0426
##      2        1.1141             nan     0.2000    0.0245
##      3        1.0643             nan     0.2000    0.0149
##      4        1.0198             nan     0.2000    0.0172
##      5        0.9794             nan     0.2000    0.0044
##      6        0.9560             nan     0.2000    0.0070
##      7        0.9322             nan     0.2000    0.0082
##      8        0.9155             nan     0.2000   -0.0001
##      9        0.8991             nan     0.2000    0.0018
##     10        0.8879             nan     0.2000    0.0005
##     20        0.7908             nan     0.2000   -0.0040
##     40        0.6965             nan     0.2000   -0.0049
##     60        0.6288             nan     0.2000   -0.0032
##     80        0.5603             nan     0.2000   -0.0005
##    100        0.5231             nan     0.2000   -0.0035
##    120        0.4790             nan     0.2000   -0.0022
##    140        0.4392             nan     0.2000   -0.0014
##    160        0.4081             nan     0.2000   -0.0014
##    180        0.3766             nan     0.2000   -0.0006
##    200        0.3486             nan     0.2000    0.0001
##    220        0.3214             nan     0.2000   -0.0026
##    240        0.2980             nan     0.2000   -0.0015
##    260        0.2739             nan     0.2000   -0.0008
##    280        0.2555             nan     0.2000   -0.0020
##    300        0.2428             nan     0.2000   -0.0013
##    320        0.2238             nan     0.2000   -0.0013
##    340        0.2100             nan     0.2000   -0.0014
##    360        0.1984             nan     0.2000   -0.0025
##    380        0.1841             nan     0.2000   -0.0011
##    400        0.1734             nan     0.2000   -0.0008
##    420        0.1626             nan     0.2000   -0.0017
##    440        0.1524             nan     0.2000   -0.0010
##    460        0.1434             nan     0.2000   -0.0010
##    480        0.1356             nan     0.2000   -0.0002
##    500        0.1271             nan     0.2000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1982             nan     0.2000    0.0437
##      2        1.1204             nan     0.2000    0.0323
##      3        1.0654             nan     0.2000    0.0182
##      4        1.0240             nan     0.2000    0.0180
##      5        0.9955             nan     0.2000    0.0082
##      6        0.9729             nan     0.2000    0.0046
##      7        0.9520             nan     0.2000    0.0027
##      8        0.9332             nan     0.2000    0.0061
##      9        0.9168             nan     0.2000    0.0017
##     10        0.8974             nan     0.2000    0.0028
##     20        0.8081             nan     0.2000   -0.0021
##     40        0.7104             nan     0.2000   -0.0012
##     60        0.6356             nan     0.2000   -0.0036
##     80        0.5851             nan     0.2000   -0.0070
##    100        0.5313             nan     0.2000   -0.0031
##    120        0.4880             nan     0.2000   -0.0015
##    140        0.4502             nan     0.2000   -0.0025
##    160        0.4104             nan     0.2000   -0.0015
##    180        0.3730             nan     0.2000   -0.0022
##    200        0.3414             nan     0.2000   -0.0005
##    220        0.3156             nan     0.2000   -0.0004
##    240        0.2884             nan     0.2000   -0.0020
##    260        0.2723             nan     0.2000   -0.0013
##    280        0.2506             nan     0.2000   -0.0026
##    300        0.2301             nan     0.2000   -0.0011
##    320        0.2163             nan     0.2000   -0.0020
##    340        0.2052             nan     0.2000   -0.0006
##    360        0.1927             nan     0.2000   -0.0008
##    380        0.1800             nan     0.2000   -0.0012
##    400        0.1708             nan     0.2000   -0.0008
##    420        0.1599             nan     0.2000   -0.0007
##    440        0.1506             nan     0.2000   -0.0013
##    460        0.1408             nan     0.2000   -0.0007
##    480        0.1328             nan     0.2000   -0.0014
##    500        0.1248             nan     0.2000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2094             nan     0.3000    0.0392
##      2        1.1574             nan     0.3000    0.0278
##      3        1.1193             nan     0.3000    0.0159
##      4        1.0891             nan     0.3000    0.0128
##      5        1.0536             nan     0.3000    0.0154
##      6        1.0145             nan     0.3000    0.0107
##      7        1.0027             nan     0.3000   -0.0003
##      8        0.9829             nan     0.3000    0.0074
##      9        0.9665             nan     0.3000    0.0061
##     10        0.9604             nan     0.3000   -0.0011
##     20        0.8915             nan     0.3000   -0.0001
##     40        0.8290             nan     0.3000   -0.0014
##     60        0.7930             nan     0.3000   -0.0038
##     80        0.7665             nan     0.3000   -0.0036
##    100        0.7442             nan     0.3000   -0.0020
##    120        0.7246             nan     0.3000   -0.0037
##    140        0.7168             nan     0.3000   -0.0061
##    160        0.7004             nan     0.3000   -0.0061
##    180        0.6806             nan     0.3000   -0.0022
##    200        0.6677             nan     0.3000   -0.0006
##    220        0.6628             nan     0.3000   -0.0032
##    240        0.6528             nan     0.3000   -0.0023
##    260        0.6414             nan     0.3000   -0.0007
##    280        0.6354             nan     0.3000   -0.0033
##    300        0.6227             nan     0.3000   -0.0036
##    320        0.6144             nan     0.3000   -0.0044
##    340        0.6055             nan     0.3000   -0.0023
##    360        0.5986             nan     0.3000   -0.0031
##    380        0.5916             nan     0.3000   -0.0038
##    400        0.5833             nan     0.3000   -0.0017
##    420        0.5783             nan     0.3000   -0.0015
##    440        0.5716             nan     0.3000   -0.0015
##    460        0.5640             nan     0.3000   -0.0031
##    480        0.5590             nan     0.3000   -0.0028
##    500        0.5528             nan     0.3000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1996             nan     0.3000    0.0337
##      2        1.1518             nan     0.3000    0.0242
##      3        1.1092             nan     0.3000    0.0194
##      4        1.0751             nan     0.3000    0.0146
##      5        1.0514             nan     0.3000    0.0077
##      6        1.0327             nan     0.3000    0.0012
##      7        1.0036             nan     0.3000    0.0148
##      8        0.9887             nan     0.3000    0.0044
##      9        0.9728             nan     0.3000    0.0066
##     10        0.9586             nan     0.3000    0.0031
##     20        0.8847             nan     0.3000    0.0003
##     40        0.8289             nan     0.3000   -0.0007
##     60        0.7986             nan     0.3000   -0.0045
##     80        0.7750             nan     0.3000   -0.0018
##    100        0.7544             nan     0.3000   -0.0070
##    120        0.7293             nan     0.3000   -0.0027
##    140        0.7183             nan     0.3000   -0.0021
##    160        0.7015             nan     0.3000   -0.0021
##    180        0.6908             nan     0.3000   -0.0029
##    200        0.6829             nan     0.3000   -0.0034
##    220        0.6699             nan     0.3000   -0.0018
##    240        0.6538             nan     0.3000   -0.0003
##    260        0.6417             nan     0.3000   -0.0016
##    280        0.6310             nan     0.3000   -0.0016
##    300        0.6262             nan     0.3000    0.0007
##    320        0.6207             nan     0.3000   -0.0027
##    340        0.6083             nan     0.3000   -0.0007
##    360        0.6022             nan     0.3000   -0.0011
##    380        0.5947             nan     0.3000   -0.0033
##    400        0.5855             nan     0.3000   -0.0011
##    420        0.5759             nan     0.3000   -0.0015
##    440        0.5701             nan     0.3000   -0.0021
##    460        0.5608             nan     0.3000   -0.0028
##    480        0.5635             nan     0.3000   -0.0037
##    500        0.5538             nan     0.3000   -0.0037
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1927             nan     0.3000    0.0471
##      2        1.1362             nan     0.3000    0.0254
##      3        1.0970             nan     0.3000    0.0159
##      4        1.0614             nan     0.3000    0.0128
##      5        1.0367             nan     0.3000    0.0075
##      6        1.0089             nan     0.3000    0.0157
##      7        0.9943             nan     0.3000    0.0014
##      8        0.9749             nan     0.3000    0.0074
##      9        0.9651             nan     0.3000    0.0029
##     10        0.9520             nan     0.3000    0.0040
##     20        0.8885             nan     0.3000   -0.0050
##     40        0.8136             nan     0.3000   -0.0021
##     60        0.7767             nan     0.3000   -0.0036
##     80        0.7503             nan     0.3000   -0.0015
##    100        0.7432             nan     0.3000   -0.0007
##    120        0.7259             nan     0.3000   -0.0003
##    140        0.7050             nan     0.3000   -0.0037
##    160        0.6910             nan     0.3000   -0.0019
##    180        0.6798             nan     0.3000   -0.0028
##    200        0.6712             nan     0.3000   -0.0005
##    220        0.6647             nan     0.3000   -0.0029
##    240        0.6558             nan     0.3000   -0.0030
##    260        0.6512             nan     0.3000   -0.0034
##    280        0.6463             nan     0.3000   -0.0042
##    300        0.6314             nan     0.3000   -0.0044
##    320        0.6233             nan     0.3000   -0.0014
##    340        0.6170             nan     0.3000   -0.0022
##    360        0.6088             nan     0.3000   -0.0016
##    380        0.5972             nan     0.3000   -0.0009
##    400        0.5902             nan     0.3000   -0.0019
##    420        0.5893             nan     0.3000   -0.0032
##    440        0.5817             nan     0.3000   -0.0018
##    460        0.5771             nan     0.3000   -0.0046
##    480        0.5697             nan     0.3000   -0.0014
##    500        0.5631             nan     0.3000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1619             nan     0.3000    0.0593
##      2        1.0927             nan     0.3000    0.0284
##      3        1.0346             nan     0.3000    0.0245
##      4        1.0007             nan     0.3000    0.0092
##      5        0.9618             nan     0.3000    0.0158
##      6        0.9399             nan     0.3000    0.0055
##      7        0.9172             nan     0.3000    0.0037
##      8        0.9059             nan     0.3000   -0.0015
##      9        0.8930             nan     0.3000    0.0005
##     10        0.8779             nan     0.3000   -0.0010
##     20        0.7928             nan     0.3000    0.0003
##     40        0.7211             nan     0.3000   -0.0085
##     60        0.6482             nan     0.3000   -0.0040
##     80        0.6019             nan     0.3000   -0.0027
##    100        0.5671             nan     0.3000   -0.0039
##    120        0.5272             nan     0.3000   -0.0040
##    140        0.4991             nan     0.3000   -0.0039
##    160        0.4861             nan     0.3000   -0.0043
##    180        0.4469             nan     0.3000   -0.0055
##    200        0.4123             nan     0.3000   -0.0036
##    220        0.3870             nan     0.3000   -0.0023
##    240        0.3597             nan     0.3000   -0.0009
##    260        0.3316             nan     0.3000   -0.0026
##    280        0.3136             nan     0.3000   -0.0033
##    300        0.3005             nan     0.3000   -0.0017
##    320        0.2843             nan     0.3000   -0.0004
##    340        0.2661             nan     0.3000   -0.0022
##    360        0.2531             nan     0.3000   -0.0026
##    380        0.2361             nan     0.3000   -0.0025
##    400        0.2249             nan     0.3000   -0.0014
##    420        0.2124             nan     0.3000   -0.0019
##    440        0.2036             nan     0.3000   -0.0000
##    460        0.1891             nan     0.3000   -0.0013
##    480        0.1797             nan     0.3000   -0.0010
##    500        0.1700             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1770             nan     0.3000    0.0504
##      2        1.0977             nan     0.3000    0.0350
##      3        1.0429             nan     0.3000    0.0194
##      4        1.0043             nan     0.3000    0.0156
##      5        0.9687             nan     0.3000    0.0093
##      6        0.9449             nan     0.3000    0.0102
##      7        0.9242             nan     0.3000    0.0039
##      8        0.9097             nan     0.3000    0.0053
##      9        0.8993             nan     0.3000    0.0000
##     10        0.8889             nan     0.3000    0.0017
##     20        0.7966             nan     0.3000   -0.0044
##     40        0.7124             nan     0.3000   -0.0034
##     60        0.6545             nan     0.3000   -0.0045
##     80        0.6035             nan     0.3000   -0.0040
##    100        0.5531             nan     0.3000   -0.0018
##    120        0.5161             nan     0.3000   -0.0039
##    140        0.4773             nan     0.3000   -0.0019
##    160        0.4422             nan     0.3000   -0.0042
##    180        0.4128             nan     0.3000   -0.0052
##    200        0.3948             nan     0.3000   -0.0037
##    220        0.3669             nan     0.3000   -0.0021
##    240        0.3429             nan     0.3000   -0.0018
##    260        0.3192             nan     0.3000   -0.0009
##    280        0.2954             nan     0.3000   -0.0006
##    300        0.2806             nan     0.3000   -0.0014
##    320        0.2654             nan     0.3000   -0.0005
##    340        0.2519             nan     0.3000   -0.0021
##    360        0.2438             nan     0.3000   -0.0012
##    380        0.2294             nan     0.3000   -0.0013
##    400        0.2172             nan     0.3000   -0.0024
##    420        0.2091             nan     0.3000   -0.0015
##    440        0.1959             nan     0.3000   -0.0019
##    460        0.1852             nan     0.3000   -0.0008
##    480        0.1770             nan     0.3000   -0.0010
##    500        0.1706             nan     0.3000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1756             nan     0.3000    0.0469
##      2        1.0993             nan     0.3000    0.0359
##      3        1.0379             nan     0.3000    0.0230
##      4        1.0069             nan     0.3000    0.0010
##      5        0.9772             nan     0.3000    0.0074
##      6        0.9610             nan     0.3000    0.0006
##      7        0.9370             nan     0.3000    0.0091
##      8        0.9160             nan     0.3000    0.0036
##      9        0.9079             nan     0.3000   -0.0006
##     10        0.8918             nan     0.3000    0.0035
##     20        0.8087             nan     0.3000   -0.0020
##     40        0.7124             nan     0.3000   -0.0013
##     60        0.6539             nan     0.3000   -0.0021
##     80        0.6055             nan     0.3000   -0.0018
##    100        0.5562             nan     0.3000   -0.0039
##    120        0.5254             nan     0.3000   -0.0068
##    140        0.4963             nan     0.3000   -0.0038
##    160        0.4705             nan     0.3000   -0.0056
##    180        0.4317             nan     0.3000   -0.0035
##    200        0.4013             nan     0.3000   -0.0004
##    220        0.3709             nan     0.3000   -0.0013
##    240        0.3537             nan     0.3000   -0.0012
##    260        0.3317             nan     0.3000   -0.0034
##    280        0.3135             nan     0.3000   -0.0017
##    300        0.2955             nan     0.3000   -0.0025
##    320        0.2789             nan     0.3000   -0.0028
##    340        0.2625             nan     0.3000   -0.0003
##    360        0.2493             nan     0.3000   -0.0007
##    380        0.2344             nan     0.3000   -0.0005
##    400        0.2183             nan     0.3000   -0.0017
##    420        0.2050             nan     0.3000   -0.0023
##    440        0.1966             nan     0.3000   -0.0005
##    460        0.1857             nan     0.3000   -0.0014
##    480        0.1735             nan     0.3000   -0.0022
##    500        0.1635             nan     0.3000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1746             nan     0.3000    0.0511
##      2        1.0903             nan     0.3000    0.0308
##      3        1.0274             nan     0.3000    0.0260
##      4        0.9835             nan     0.3000    0.0140
##      5        0.9424             nan     0.3000    0.0147
##      6        0.9171             nan     0.3000    0.0055
##      7        0.8897             nan     0.3000    0.0102
##      8        0.8677             nan     0.3000    0.0036
##      9        0.8553             nan     0.3000   -0.0018
##     10        0.8476             nan     0.3000   -0.0132
##     20        0.7537             nan     0.3000   -0.0053
##     40        0.6341             nan     0.3000   -0.0045
##     60        0.5513             nan     0.3000   -0.0047
##     80        0.4832             nan     0.3000   -0.0004
##    100        0.4220             nan     0.3000   -0.0019
##    120        0.3679             nan     0.3000   -0.0035
##    140        0.3293             nan     0.3000   -0.0019
##    160        0.2912             nan     0.3000   -0.0028
##    180        0.2605             nan     0.3000   -0.0015
##    200        0.2391             nan     0.3000   -0.0005
##    220        0.2155             nan     0.3000   -0.0016
##    240        0.1949             nan     0.3000   -0.0021
##    260        0.1772             nan     0.3000   -0.0015
##    280        0.1620             nan     0.3000   -0.0013
##    300        0.1502             nan     0.3000   -0.0016
##    320        0.1345             nan     0.3000   -0.0009
##    340        0.1249             nan     0.3000   -0.0008
##    360        0.1162             nan     0.3000   -0.0012
##    380        0.1048             nan     0.3000   -0.0008
##    400        0.0966             nan     0.3000   -0.0010
##    420        0.0887             nan     0.3000   -0.0002
##    440        0.0822             nan     0.3000   -0.0003
##    460        0.0769             nan     0.3000   -0.0007
##    480        0.0709             nan     0.3000    0.0000
##    500        0.0652             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1651             nan     0.3000    0.0484
##      2        1.0938             nan     0.3000    0.0191
##      3        1.0263             nan     0.3000    0.0282
##      4        0.9755             nan     0.3000    0.0215
##      5        0.9461             nan     0.3000    0.0028
##      6        0.9109             nan     0.3000    0.0079
##      7        0.8836             nan     0.3000    0.0062
##      8        0.8678             nan     0.3000    0.0008
##      9        0.8517             nan     0.3000   -0.0005
##     10        0.8385             nan     0.3000    0.0004
##     20        0.7405             nan     0.3000   -0.0042
##     40        0.6498             nan     0.3000   -0.0039
##     60        0.5462             nan     0.3000   -0.0068
##     80        0.4676             nan     0.3000   -0.0024
##    100        0.4070             nan     0.3000   -0.0028
##    120        0.3602             nan     0.3000   -0.0021
##    140        0.3188             nan     0.3000   -0.0019
##    160        0.2890             nan     0.3000   -0.0018
##    180        0.2572             nan     0.3000   -0.0002
##    200        0.2303             nan     0.3000   -0.0017
##    220        0.2074             nan     0.3000   -0.0028
##    240        0.1872             nan     0.3000   -0.0007
##    260        0.1665             nan     0.3000   -0.0019
##    280        0.1498             nan     0.3000   -0.0014
##    300        0.1370             nan     0.3000   -0.0022
##    320        0.1236             nan     0.3000   -0.0010
##    340        0.1106             nan     0.3000   -0.0005
##    360        0.1026             nan     0.3000   -0.0001
##    380        0.0945             nan     0.3000   -0.0004
##    400        0.0899             nan     0.3000   -0.0011
##    420        0.0834             nan     0.3000   -0.0004
##    440        0.0763             nan     0.3000   -0.0007
##    460        0.0701             nan     0.3000   -0.0000
##    480        0.0643             nan     0.3000   -0.0012
##    500        0.0591             nan     0.3000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1568             nan     0.3000    0.0642
##      2        1.0733             nan     0.3000    0.0402
##      3        1.0061             nan     0.3000    0.0269
##      4        0.9629             nan     0.3000    0.0165
##      5        0.9283             nan     0.3000    0.0137
##      6        0.9108             nan     0.3000    0.0009
##      7        0.8855             nan     0.3000    0.0063
##      8        0.8680             nan     0.3000   -0.0020
##      9        0.8498             nan     0.3000    0.0028
##     10        0.8369             nan     0.3000   -0.0026
##     20        0.7494             nan     0.3000   -0.0026
##     40        0.6391             nan     0.3000   -0.0065
##     60        0.5659             nan     0.3000   -0.0038
##     80        0.4871             nan     0.3000   -0.0065
##    100        0.4380             nan     0.3000   -0.0017
##    120        0.3905             nan     0.3000   -0.0044
##    140        0.3412             nan     0.3000   -0.0013
##    160        0.3020             nan     0.3000   -0.0016
##    180        0.2682             nan     0.3000   -0.0033
##    200        0.2417             nan     0.3000   -0.0011
##    220        0.2204             nan     0.3000   -0.0021
##    240        0.1977             nan     0.3000   -0.0032
##    260        0.1783             nan     0.3000   -0.0015
##    280        0.1602             nan     0.3000   -0.0004
##    300        0.1469             nan     0.3000   -0.0012
##    320        0.1337             nan     0.3000   -0.0008
##    340        0.1208             nan     0.3000   -0.0002
##    360        0.1116             nan     0.3000   -0.0010
##    380        0.1021             nan     0.3000   -0.0007
##    400        0.0951             nan     0.3000   -0.0001
##    420        0.0878             nan     0.3000   -0.0006
##    440        0.0821             nan     0.3000   -0.0006
##    460        0.0758             nan     0.3000   -0.0017
##    480        0.0693             nan     0.3000   -0.0004
##    500        0.0649             nan     0.3000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1681             nan     0.5000    0.0502
##      2        1.1141             nan     0.5000    0.0222
##      3        1.0657             nan     0.5000    0.0217
##      4        1.0455             nan     0.5000   -0.0041
##      5        1.0131             nan     0.5000    0.0136
##      6        0.9674             nan     0.5000    0.0176
##      7        0.9478             nan     0.5000    0.0025
##      8        0.9361             nan     0.5000    0.0032
##      9        0.9196             nan     0.5000    0.0029
##     10        0.9055             nan     0.5000    0.0032
##     20        0.8609             nan     0.5000   -0.0026
##     40        0.7906             nan     0.5000   -0.0095
##     60        0.7676             nan     0.5000   -0.0101
##     80        0.7370             nan     0.5000   -0.0078
##    100        0.7170             nan     0.5000   -0.0097
##    120        0.6922             nan     0.5000   -0.0006
##    140        0.6812             nan     0.5000   -0.0063
##    160        0.6632             nan     0.5000   -0.0064
##    180        0.6466             nan     0.5000   -0.0036
##    200        0.6293             nan     0.5000   -0.0018
##    220        0.6236             nan     0.5000   -0.0068
##    240        0.6043             nan     0.5000   -0.0045
##    260        0.5886             nan     0.5000   -0.0048
##    280        0.5751             nan     0.5000   -0.0073
##    300        0.5559             nan     0.5000   -0.0047
##    320        0.5577             nan     0.5000   -0.0053
##    340        0.5358             nan     0.5000   -0.0052
##    360        0.5316             nan     0.5000   -0.0064
##    380        0.5239             nan     0.5000   -0.0047
##    400        0.5158             nan     0.5000   -0.0051
##    420        0.5125             nan     0.5000   -0.0029
##    440        0.5065             nan     0.5000   -0.0068
##    460        0.5094             nan     0.5000    0.0004
##    480        0.4948             nan     0.5000   -0.0062
##    500        0.4842             nan     0.5000   -0.0042
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1776             nan     0.5000    0.0376
##      2        1.1078             nan     0.5000    0.0339
##      3        1.0509             nan     0.5000    0.0203
##      4        0.9983             nan     0.5000    0.0219
##      5        0.9723             nan     0.5000    0.0083
##      6        0.9575             nan     0.5000   -0.0056
##      7        0.9315             nan     0.5000    0.0103
##      8        0.9218             nan     0.5000   -0.0011
##      9        0.9075             nan     0.5000    0.0017
##     10        0.9017             nan     0.5000   -0.0002
##     20        0.8497             nan     0.5000   -0.0061
##     40        0.8027             nan     0.5000    0.0030
##     60        0.7620             nan     0.5000   -0.0064
##     80        0.7327             nan     0.5000   -0.0047
##    100        0.7158             nan     0.5000   -0.0050
##    120        0.7003             nan     0.5000   -0.0011
##    140        0.6710             nan     0.5000   -0.0056
##    160        0.6649             nan     0.5000   -0.0062
##    180        0.6522             nan     0.5000   -0.0171
##    200        0.6350             nan     0.5000   -0.0023
##    220        0.6194             nan     0.5000   -0.0065
##    240        0.6069             nan     0.5000   -0.0032
##    260        0.5984             nan     0.5000   -0.0062
##    280        0.5786             nan     0.5000   -0.0049
##    300        0.5704             nan     0.5000   -0.0057
##    320        0.5593             nan     0.5000   -0.0046
##    340        0.5487             nan     0.5000   -0.0068
##    360        0.5391             nan     0.5000   -0.0012
##    380        0.5413             nan     0.5000   -0.0084
##    400        0.5232             nan     0.5000   -0.0032
##    420        0.5139             nan     0.5000   -0.0038
##    440        0.5101             nan     0.5000   -0.0043
##    460        0.5021             nan     0.5000   -0.0044
##    480        0.4880             nan     0.5000   -0.0059
##    500        0.4811             nan     0.5000   -0.0047
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1690             nan     0.5000    0.0688
##      2        1.1043             nan     0.5000    0.0347
##      3        1.0523             nan     0.5000    0.0237
##      4        1.0002             nan     0.5000    0.0231
##      5        0.9705             nan     0.5000    0.0092
##      6        0.9636             nan     0.5000   -0.0040
##      7        0.9445             nan     0.5000    0.0033
##      8        0.9348             nan     0.5000   -0.0081
##      9        0.9279             nan     0.5000   -0.0046
##     10        0.9146             nan     0.5000   -0.0076
##     20        0.8558             nan     0.5000   -0.0121
##     40        0.7852             nan     0.5000   -0.0076
##     60        0.7534             nan     0.5000   -0.0049
##     80        0.7334             nan     0.5000   -0.0062
##    100        0.6997             nan     0.5000   -0.0036
##    120        0.6723             nan     0.5000   -0.0031
##    140        0.6488             nan     0.5000   -0.0064
##    160        0.6483             nan     0.5000   -0.0006
##    180        0.6355             nan     0.5000   -0.0022
##    200        0.6279             nan     0.5000   -0.0040
##    220        0.6160             nan     0.5000   -0.0110
##    240        0.6076             nan     0.5000   -0.0076
##    260        0.5902             nan     0.5000   -0.0080
##    280        0.5791             nan     0.5000   -0.0055
##    300        0.5694             nan     0.5000   -0.0036
##    320        0.5593             nan     0.5000   -0.0020
##    340        0.5527             nan     0.5000   -0.0044
##    360        0.5394             nan     0.5000   -0.0071
##    380        0.5287             nan     0.5000   -0.0037
##    400        0.5266             nan     0.5000   -0.0041
##    420        0.5202             nan     0.5000   -0.0034
##    440        0.5091             nan     0.5000   -0.0031
##    460        0.5025             nan     0.5000   -0.0041
##    480        0.4943             nan     0.5000   -0.0077
##    500        0.4939             nan     0.5000   -0.0072
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1433             nan     0.5000    0.0576
##      2        1.0071             nan     0.5000    0.0532
##      3        0.9725             nan     0.5000    0.0051
##      4        0.9394             nan     0.5000    0.0056
##      5        0.8932             nan     0.5000    0.0097
##      6        0.8782             nan     0.5000   -0.0008
##      7        0.8617             nan     0.5000    0.0020
##      8        0.8421             nan     0.5000    0.0057
##      9        0.8278             nan     0.5000   -0.0066
##     10        0.8253             nan     0.5000   -0.0107
##     20        0.7640             nan     0.5000   -0.0091
##     40        3.0840             nan     0.5000   -0.0063
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000   -0.0001
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000   -0.0001
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1285             nan     0.5000    0.0611
##      2        1.0443             nan     0.5000    0.0375
##      3        0.9803             nan     0.5000    0.0212
##      4        0.9516             nan     0.5000    0.0020
##      5        0.9217             nan     0.5000    0.0081
##      6        0.8961             nan     0.5000    0.0062
##      7        0.8825             nan     0.5000   -0.0035
##      8        0.8688             nan     0.5000   -0.0018
##      9        0.8586             nan     0.5000   -0.0036
##     10        0.8463             nan     0.5000    0.0000
##     20        0.7621             nan     0.5000   -0.0066
##     40        0.6833             nan     0.5000   -0.0011
##     60        0.6122             nan     0.5000   -0.0106
##     80        0.5578             nan     0.5000   -0.0132
##    100        0.5013             nan     0.5000   -0.0073
##    120        0.4452             nan     0.5000   -0.0056
##    140        0.4025             nan     0.5000   -0.0068
##    160        0.3629             nan     0.5000   -0.0043
##    180        0.3197             nan     0.5000   -0.0060
##    200        0.2834             nan     0.5000   -0.0058
##    220        0.2566             nan     0.5000   -0.0052
##    240        0.2318             nan     0.5000   -0.0047
##    260        0.2086             nan     0.5000   -0.0026
##    280        0.1846             nan     0.5000   -0.0023
##    300        0.1694             nan     0.5000   -0.0010
##    320        0.1558             nan     0.5000   -0.0009
##    340        0.1389             nan     0.5000   -0.0014
##    360        0.1256             nan     0.5000   -0.0014
##    380        0.1147             nan     0.5000   -0.0008
##    400        0.1099             nan     0.5000   -0.0009
##    420        0.0985             nan     0.5000   -0.0011
##    440        0.0924             nan     0.5000   -0.0024
##    460        0.0837             nan     0.5000   -0.0007
##    480        0.0786             nan     0.5000   -0.0007
##    500        0.0703             nan     0.5000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1264             nan     0.5000    0.0773
##      2        1.0521             nan     0.5000    0.0315
##      3        0.9976             nan     0.5000    0.0095
##      4        0.9736             nan     0.5000   -0.0023
##      5        0.9406             nan     0.5000    0.0028
##      6        0.9012             nan     0.5000    0.0119
##      7        0.8857             nan     0.5000    0.0012
##      8        0.8733             nan     0.5000    0.0003
##      9        0.8716             nan     0.5000   -0.0087
##     10        0.8580             nan     0.5000   -0.0031
##     20        0.7891             nan     0.5000   -0.0092
##     40        0.6899             nan     0.5000   -0.0079
##     60        0.6437             nan     0.5000   -0.0080
##     80        0.5544             nan     0.5000   -0.0103
##    100        0.4777             nan     0.5000   -0.0154
##    120        0.4381             nan     0.5000   -0.0058
##    140        0.3779             nan     0.5000   -0.0022
##    160        0.3300             nan     0.5000   -0.0034
##    180        0.2871             nan     0.5000   -0.0055
##    200        0.2512             nan     0.5000   -0.0012
##    220        0.2197             nan     0.5000   -0.0055
##    240        0.1986             nan     0.5000   -0.0016
##    260        0.1802             nan     0.5000   -0.0020
##    280        0.1631             nan     0.5000   -0.0010
##    300        0.1448             nan     0.5000   -0.0024
##    320        0.1348             nan     0.5000   -0.0044
##    340        0.1243             nan     0.5000   -0.0006
##    360        0.1129             nan     0.5000   -0.0015
##    380        0.1021             nan     0.5000   -0.0013
##    400        0.0937             nan     0.5000   -0.0008
##    420        0.0887             nan     0.5000   -0.0025
##    440        0.0835             nan     0.5000   -0.0014
##    460        0.0774             nan     0.5000   -0.0013
##    480        0.0715             nan     0.5000   -0.0002
##    500        0.0667             nan     0.5000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0777             nan     0.5000    0.1007
##      2        0.9925             nan     0.5000    0.0111
##      3        0.9436             nan     0.5000    0.0089
##      4        0.8974             nan     0.5000    0.0109
##      5        0.8602             nan     0.5000    0.0038
##      6        0.8363             nan     0.5000   -0.0088
##      7        0.8090             nan     0.5000    0.0032
##      8        0.7974             nan     0.5000   -0.0046
##      9        0.7825             nan     0.5000   -0.0023
##     10        0.7765             nan     0.5000   -0.0173
##     20        0.6808             nan     0.5000   -0.0107
##     40        0.6264             nan     0.5000   -0.0039
##     60        0.6045             nan     0.5000    0.0541
##     80  1378802.6476             nan     0.5000   -0.0053
##    100  1378802.4358             nan     0.5000   -0.0141
##    120  1378802.1941             nan     0.5000   -0.0033
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0737             nan     0.5000    0.0979
##      2        0.9931             nan     0.5000    0.0230
##      3        0.9475             nan     0.5000    0.0051
##      4        0.9224             nan     0.5000   -0.0065
##      5        0.9004             nan     0.5000   -0.0057
##      6        0.8873             nan     0.5000   -0.0101
##      7        0.8633             nan     0.5000    0.0003
##      8        0.8311             nan     0.5000    0.0050
##      9        0.8208             nan     0.5000   -0.0125
##     10        0.8008             nan     0.5000   -0.0024
##     20        0.7112             nan     0.5000   -0.0046
##     40        0.5883             nan     0.5000   -0.0185
##     60        0.4657             nan     0.5000   -0.0148
##     80        0.3774             nan     0.5000   -0.0070
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0865             nan     0.5000    0.1017
##      2        1.0104             nan     0.5000    0.0216
##      3        0.9440             nan     0.5000    0.0151
##      4        0.9158             nan     0.5000    0.0027
##      5        0.8933             nan     0.5000   -0.0015
##      6        0.8688             nan     0.5000   -0.0011
##      7        0.8451             nan     0.5000    0.0028
##      8        0.8339             nan     0.5000   -0.0078
##      9        0.8131             nan     0.5000   -0.0097
##     10        0.8095             nan     0.5000   -0.0126
##     20        0.7033             nan     0.5000   -0.0117
##     40        0.5925             nan     0.5000   -0.0055
##     60        0.4974             nan     0.5000   -0.0119
##     80        0.3875             nan     0.5000   -0.0019
##    100        0.3021             nan     0.5000   -0.0025
##    120        0.2339             nan     0.5000   -0.0051
##    140        0.1936             nan     0.5000   -0.0029
##    160        0.1596             nan     0.5000    0.0005
##    180        0.1391             nan     0.5000   -0.0025
##    200        0.1193             nan     0.5000   -0.0016
##    220        0.0999             nan     0.5000   -0.0006
##    240        0.0863             nan     0.5000   -0.0006
##    260        0.0742             nan     0.5000   -0.0005
##    280        0.0652             nan     0.5000   -0.0012
##    300        0.0574             nan     0.5000   -0.0006
##    320        0.0507             nan     0.5000   -0.0003
##    340        0.0440             nan     0.5000   -0.0003
##    360        0.0378             nan     0.5000   -0.0004
##    380        0.0336             nan     0.5000   -0.0003
##    400        0.0298             nan     0.5000   -0.0007
##    420        0.0258             nan     0.5000   -0.0006
##    440        0.0230             nan     0.5000   -0.0000
##    460        0.0207             nan     0.5000   -0.0005
##    480        0.0174             nan     0.5000   -0.0003
##    500        0.0156             nan     0.5000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1190             nan     1.0000    0.0729
##      2        1.0328             nan     1.0000    0.0366
##      3        0.9880             nan     1.0000    0.0155
##      4        0.9440             nan     1.0000    0.0156
##      5        0.9357             nan     1.0000   -0.0213
##      6        0.9283             nan     1.0000   -0.0091
##      7        0.9245             nan     1.0000   -0.0161
##      8        0.9385             nan     1.0000   -0.0398
##      9        0.9202             nan     1.0000    0.0040
##     10        0.9094             nan     1.0000   -0.0092
##     20        0.9241             nan     1.0000   -0.0132
##     40      135.1166             nan     1.0000    0.0024
##     60      135.1219             nan     1.0000   -0.0020
##     80      135.0764             nan     1.0000   -0.0164
##    100      135.0942             nan     1.0000   -0.0359
##    120      135.0272             nan     1.0000   -0.0000
##    140      135.0309             nan     1.0000   -0.0096
##    160      135.0119             nan     1.0000    0.0007
##    180           inf             nan     1.0000    0.0002
##    200           inf             nan     1.0000   -0.0391
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1210             nan     1.0000    0.0659
##      2        1.0628             nan     1.0000    0.0081
##      3        0.9852             nan     1.0000    0.0267
##      4        0.9590             nan     1.0000   -0.0038
##      5        0.9374             nan     1.0000    0.0004
##      6        0.9267             nan     1.0000   -0.0049
##      7        0.9216             nan     1.0000   -0.0066
##      8        0.9188             nan     1.0000   -0.0140
##      9        0.9021             nan     1.0000   -0.0022
##     10        0.8720             nan     1.0000    0.0031
##     20        0.8436             nan     1.0000   -0.0012
##     40        0.8135             nan     1.0000    0.0029
##     60        0.7851             nan     1.0000   -0.0197
##     80        0.7855             nan     1.0000   -0.0162
##    100        0.7744             nan     1.0000   -0.0123
##    120        0.9004             nan     1.0000   -0.1397
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380 2169605031634222834246622688202.0000             nan     1.0000    0.0003
##    400 2169605031634222834246622688202.0000             nan     1.0000   -0.0036
##    420 2169605031634222834246622688202.0000             nan     1.0000    0.0000
##    440 2169605031634222834246622688202.0000             nan     1.0000    0.0001
##    460 2169605031634222834246622688202.0000             nan     1.0000   -0.0055
##    480 2169605031634222834246622688202.0000             nan     1.0000    0.0014
##    500 2169605031634222834246622688202.0000             nan     1.0000    0.0098
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1370             nan     1.0000    0.0796
##      2        1.0679             nan     1.0000    0.0121
##      3        1.0229             nan     1.0000    0.0097
##      4        1.0075             nan     1.0000   -0.0106
##      5        0.9598             nan     1.0000    0.0174
##      6        0.9707             nan     1.0000   -0.0280
##      7        0.9487             nan     1.0000    0.0024
##      8        0.9450             nan     1.0000   -0.0227
##      9        0.9438             nan     1.0000   -0.0143
##     10        0.9340             nan     1.0000    0.0006
##     20        0.9048             nan     1.0000   -0.0029
##     40        0.8699             nan     1.0000   -0.0233
##     60        1.0284             nan     1.0000   -0.0051
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180 7669239749916452864.0000             nan     1.0000   -0.0149
##    200 7669239749916452864.0000             nan     1.0000   -0.0003
##    220 7669239749916452864.0000             nan     1.0000   -0.0182
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0903             nan     1.0000    0.0895
##      2        1.0564             nan     1.0000   -0.0230
##      3        0.9713             nan     1.0000    0.0120
##      4        0.9568             nan     1.0000   -0.0159
##      5        0.9464             nan     1.0000   -0.0084
##      6        0.9089             nan     1.0000   -0.0051
##      7        0.9121             nan     1.0000   -0.0241
##      8        0.8920             nan     1.0000   -0.0042
##      9        0.8918             nan     1.0000   -0.0324
##     10        0.9123             nan     1.0000   -0.0612
##     20        1.4548             nan     1.0000   -0.0105
##     40        1.3525             nan     1.0000   -0.0066
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0648             nan     1.0000    0.0843
##      2        0.9616             nan     1.0000    0.0450
##      3        0.9257             nan     1.0000   -0.0011
##      4        0.9196             nan     1.0000   -0.0168
##      5        0.9031             nan     1.0000   -0.0067
##      6        1.2230             nan     1.0000   -0.3541
##      7        1.1689             nan     1.0000    0.0311
##      8        1.1567             nan     1.0000   -0.0182
##      9        1.1550             nan     1.0000   -0.0285
##     10        1.0201             nan     1.0000   -0.0141
##     20        1.3511             nan     1.0000   -0.0403
##     40        0.8200             nan     1.0000   -0.0023
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0419             nan     1.0000    0.1262
##      2        0.9804             nan     1.0000   -0.0060
##      3        0.9445             nan     1.0000   -0.0128
##      4        0.9224             nan     1.0000   -0.0159
##      5        0.9170             nan     1.0000   -0.0199
##      6        0.9090             nan     1.0000   -0.0124
##      7        0.8876             nan     1.0000   -0.0039
##      8        0.8749             nan     1.0000   -0.0079
##      9        0.8727             nan     1.0000   -0.0176
##     10        0.9009             nan     1.0000   -0.0045
##     20        1.4943             nan     1.0000   -0.1105
##     40        2.4397             nan     1.0000   -0.0344
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0737             nan     1.0000    0.0619
##      2        0.9953             nan     1.0000    0.0046
##      3        0.9415             nan     1.0000    0.0062
##      4        0.9495             nan     1.0000   -0.0482
##      5        0.9516             nan     1.0000   -0.0329
##      6        0.9362             nan     1.0000   -0.0336
##      7        1.0935             nan     1.0000   -0.1691
##      8           inf             nan     1.0000      -inf
##      9           inf             nan     1.0000       nan
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0846             nan     1.0000    0.0576
##      2        0.9543             nan     1.0000    0.0495
##      3        0.9289             nan     1.0000   -0.0278
##      4        0.9101             nan     1.0000   -0.0186
##      5        0.9033             nan     1.0000   -0.0300
##      6        0.8816             nan     1.0000   -0.0110
##      7        0.8520             nan     1.0000   -0.0171
##      8        0.8622             nan     1.0000   -0.0423
##      9        0.8958             nan     1.0000   -0.0660
##     10        0.9135             nan     1.0000   -0.0596
##     20 2482861072219.3682             nan     1.0000   -0.0692
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0465             nan     1.0000    0.0949
##      2        1.0135             nan     1.0000   -0.0355
##      3        0.9602             nan     1.0000   -0.0131
##      4        0.9319             nan     1.0000   -0.0140
##      5        1.0531             nan     1.0000   -0.1228
##      6        1.4485             nan     1.0000   -0.3662
##      7        1.4523             nan     1.0000   -0.0522
##      8           inf             nan     1.0000      -inf
##      9           inf             nan     1.0000       nan
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2921             nan     0.0010    0.0002
##      4        1.2917             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2906             nan     0.0010    0.0002
##      8        1.2902             nan     0.0010    0.0001
##      9        1.2898             nan     0.0010    0.0002
##     10        1.2895             nan     0.0010    0.0002
##     20        1.2858             nan     0.0010    0.0002
##     40        1.2789             nan     0.0010    0.0002
##     60        1.2720             nan     0.0010    0.0002
##     80        1.2655             nan     0.0010    0.0002
##    100        1.2591             nan     0.0010    0.0001
##    120        1.2529             nan     0.0010    0.0001
##    140        1.2471             nan     0.0010    0.0001
##    160        1.2414             nan     0.0010    0.0001
##    180        1.2357             nan     0.0010    0.0001
##    200        1.2301             nan     0.0010    0.0001
##    220        1.2249             nan     0.0010    0.0001
##    240        1.2198             nan     0.0010    0.0001
##    260        1.2147             nan     0.0010    0.0001
##    280        1.2099             nan     0.0010    0.0001
##    300        1.2052             nan     0.0010    0.0001
##    320        1.2008             nan     0.0010    0.0001
##    340        1.1966             nan     0.0010    0.0001
##    360        1.1922             nan     0.0010    0.0001
##    380        1.1878             nan     0.0010    0.0001
##    400        1.1838             nan     0.0010    0.0001
##    420        1.1798             nan     0.0010    0.0001
##    440        1.1758             nan     0.0010    0.0001
##    460        1.1720             nan     0.0010    0.0001
##    480        1.1682             nan     0.0010    0.0001
##    500        1.1646             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2791             nan     0.0010    0.0001
##     60        1.2723             nan     0.0010    0.0001
##     80        1.2658             nan     0.0010    0.0001
##    100        1.2594             nan     0.0010    0.0001
##    120        1.2533             nan     0.0010    0.0001
##    140        1.2476             nan     0.0010    0.0001
##    160        1.2416             nan     0.0010    0.0001
##    180        1.2361             nan     0.0010    0.0001
##    200        1.2308             nan     0.0010    0.0001
##    220        1.2254             nan     0.0010    0.0001
##    240        1.2203             nan     0.0010    0.0001
##    260        1.2154             nan     0.0010    0.0001
##    280        1.2107             nan     0.0010    0.0001
##    300        1.2061             nan     0.0010    0.0001
##    320        1.2018             nan     0.0010    0.0001
##    340        1.1974             nan     0.0010    0.0001
##    360        1.1931             nan     0.0010    0.0001
##    380        1.1887             nan     0.0010    0.0001
##    400        1.1847             nan     0.0010    0.0001
##    420        1.1807             nan     0.0010    0.0001
##    440        1.1768             nan     0.0010    0.0001
##    460        1.1728             nan     0.0010    0.0001
##    480        1.1691             nan     0.0010    0.0001
##    500        1.1653             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0001
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0001
##     20        1.2861             nan     0.0010    0.0001
##     40        1.2791             nan     0.0010    0.0001
##     60        1.2723             nan     0.0010    0.0001
##     80        1.2658             nan     0.0010    0.0001
##    100        1.2594             nan     0.0010    0.0001
##    120        1.2533             nan     0.0010    0.0001
##    140        1.2475             nan     0.0010    0.0001
##    160        1.2416             nan     0.0010    0.0001
##    180        1.2362             nan     0.0010    0.0001
##    200        1.2305             nan     0.0010    0.0001
##    220        1.2252             nan     0.0010    0.0001
##    240        1.2202             nan     0.0010    0.0001
##    260        1.2152             nan     0.0010    0.0001
##    280        1.2102             nan     0.0010    0.0001
##    300        1.2055             nan     0.0010    0.0001
##    320        1.2009             nan     0.0010    0.0001
##    340        1.1965             nan     0.0010    0.0001
##    360        1.1922             nan     0.0010    0.0001
##    380        1.1881             nan     0.0010    0.0001
##    400        1.1841             nan     0.0010    0.0001
##    420        1.1801             nan     0.0010    0.0001
##    440        1.1762             nan     0.0010    0.0001
##    460        1.1725             nan     0.0010    0.0001
##    480        1.1687             nan     0.0010    0.0001
##    500        1.1651             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0003
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2837             nan     0.0010    0.0002
##     40        1.2746             nan     0.0010    0.0002
##     60        1.2656             nan     0.0010    0.0002
##     80        1.2569             nan     0.0010    0.0002
##    100        1.2486             nan     0.0010    0.0002
##    120        1.2406             nan     0.0010    0.0002
##    140        1.2329             nan     0.0010    0.0002
##    160        1.2253             nan     0.0010    0.0001
##    180        1.2179             nan     0.0010    0.0001
##    200        1.2110             nan     0.0010    0.0002
##    220        1.2040             nan     0.0010    0.0002
##    240        1.1974             nan     0.0010    0.0001
##    260        1.1909             nan     0.0010    0.0001
##    280        1.1845             nan     0.0010    0.0001
##    300        1.1784             nan     0.0010    0.0001
##    320        1.1725             nan     0.0010    0.0001
##    340        1.1666             nan     0.0010    0.0001
##    360        1.1609             nan     0.0010    0.0001
##    380        1.1554             nan     0.0010    0.0001
##    400        1.1497             nan     0.0010    0.0001
##    420        1.1445             nan     0.0010    0.0001
##    440        1.1393             nan     0.0010    0.0001
##    460        1.1341             nan     0.0010    0.0001
##    480        1.1291             nan     0.0010    0.0001
##    500        1.1245             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2839             nan     0.0010    0.0002
##     40        1.2749             nan     0.0010    0.0002
##     60        1.2659             nan     0.0010    0.0002
##     80        1.2574             nan     0.0010    0.0002
##    100        1.2490             nan     0.0010    0.0002
##    120        1.2409             nan     0.0010    0.0002
##    140        1.2331             nan     0.0010    0.0002
##    160        1.2256             nan     0.0010    0.0002
##    180        1.2183             nan     0.0010    0.0002
##    200        1.2113             nan     0.0010    0.0002
##    220        1.2045             nan     0.0010    0.0002
##    240        1.1977             nan     0.0010    0.0001
##    260        1.1913             nan     0.0010    0.0001
##    280        1.1850             nan     0.0010    0.0001
##    300        1.1788             nan     0.0010    0.0001
##    320        1.1727             nan     0.0010    0.0001
##    340        1.1669             nan     0.0010    0.0001
##    360        1.1613             nan     0.0010    0.0001
##    380        1.1560             nan     0.0010    0.0001
##    400        1.1505             nan     0.0010    0.0001
##    420        1.1452             nan     0.0010    0.0001
##    440        1.1401             nan     0.0010    0.0001
##    460        1.1352             nan     0.0010    0.0001
##    480        1.1303             nan     0.0010    0.0001
##    500        1.1256             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2841             nan     0.0010    0.0002
##     40        1.2749             nan     0.0010    0.0002
##     60        1.2661             nan     0.0010    0.0002
##     80        1.2575             nan     0.0010    0.0002
##    100        1.2494             nan     0.0010    0.0002
##    120        1.2414             nan     0.0010    0.0002
##    140        1.2337             nan     0.0010    0.0002
##    160        1.2262             nan     0.0010    0.0002
##    180        1.2190             nan     0.0010    0.0002
##    200        1.2117             nan     0.0010    0.0002
##    220        1.2048             nan     0.0010    0.0002
##    240        1.1981             nan     0.0010    0.0001
##    260        1.1917             nan     0.0010    0.0001
##    280        1.1852             nan     0.0010    0.0001
##    300        1.1790             nan     0.0010    0.0002
##    320        1.1730             nan     0.0010    0.0001
##    340        1.1671             nan     0.0010    0.0001
##    360        1.1615             nan     0.0010    0.0001
##    380        1.1559             nan     0.0010    0.0001
##    400        1.1505             nan     0.0010    0.0001
##    420        1.1452             nan     0.0010    0.0001
##    440        1.1402             nan     0.0010    0.0001
##    460        1.1351             nan     0.0010    0.0001
##    480        1.1301             nan     0.0010    0.0001
##    500        1.1255             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2895             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0002
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0002
##     20        1.2825             nan     0.0010    0.0003
##     40        1.2720             nan     0.0010    0.0003
##     60        1.2622             nan     0.0010    0.0002
##     80        1.2524             nan     0.0010    0.0002
##    100        1.2426             nan     0.0010    0.0002
##    120        1.2335             nan     0.0010    0.0002
##    140        1.2247             nan     0.0010    0.0002
##    160        1.2161             nan     0.0010    0.0002
##    180        1.2077             nan     0.0010    0.0002
##    200        1.1996             nan     0.0010    0.0002
##    220        1.1918             nan     0.0010    0.0002
##    240        1.1842             nan     0.0010    0.0002
##    260        1.1768             nan     0.0010    0.0002
##    280        1.1695             nan     0.0010    0.0001
##    300        1.1625             nan     0.0010    0.0001
##    320        1.1558             nan     0.0010    0.0001
##    340        1.1492             nan     0.0010    0.0001
##    360        1.1429             nan     0.0010    0.0001
##    380        1.1368             nan     0.0010    0.0001
##    400        1.1307             nan     0.0010    0.0001
##    420        1.1250             nan     0.0010    0.0001
##    440        1.1191             nan     0.0010    0.0001
##    460        1.1132             nan     0.0010    0.0001
##    480        1.1078             nan     0.0010    0.0001
##    500        1.1026             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0003
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2894             nan     0.0010    0.0003
##      8        1.2889             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0003
##     20        1.2825             nan     0.0010    0.0002
##     40        1.2719             nan     0.0010    0.0003
##     60        1.2616             nan     0.0010    0.0002
##     80        1.2517             nan     0.0010    0.0002
##    100        1.2421             nan     0.0010    0.0002
##    120        1.2330             nan     0.0010    0.0002
##    140        1.2244             nan     0.0010    0.0002
##    160        1.2160             nan     0.0010    0.0002
##    180        1.2077             nan     0.0010    0.0002
##    200        1.1997             nan     0.0010    0.0002
##    220        1.1917             nan     0.0010    0.0002
##    240        1.1841             nan     0.0010    0.0002
##    260        1.1767             nan     0.0010    0.0001
##    280        1.1696             nan     0.0010    0.0001
##    300        1.1626             nan     0.0010    0.0001
##    320        1.1558             nan     0.0010    0.0001
##    340        1.1492             nan     0.0010    0.0001
##    360        1.1428             nan     0.0010    0.0001
##    380        1.1364             nan     0.0010    0.0001
##    400        1.1305             nan     0.0010    0.0001
##    420        1.1247             nan     0.0010    0.0001
##    440        1.1189             nan     0.0010    0.0001
##    460        1.1132             nan     0.0010    0.0001
##    480        1.1076             nan     0.0010    0.0001
##    500        1.1022             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0003
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2886             nan     0.0010    0.0002
##     10        1.2881             nan     0.0010    0.0003
##     20        1.2827             nan     0.0010    0.0003
##     40        1.2723             nan     0.0010    0.0003
##     60        1.2618             nan     0.0010    0.0002
##     80        1.2522             nan     0.0010    0.0002
##    100        1.2428             nan     0.0010    0.0002
##    120        1.2335             nan     0.0010    0.0002
##    140        1.2244             nan     0.0010    0.0002
##    160        1.2159             nan     0.0010    0.0002
##    180        1.2077             nan     0.0010    0.0002
##    200        1.1997             nan     0.0010    0.0002
##    220        1.1918             nan     0.0010    0.0002
##    240        1.1843             nan     0.0010    0.0001
##    260        1.1771             nan     0.0010    0.0002
##    280        1.1699             nan     0.0010    0.0001
##    300        1.1631             nan     0.0010    0.0002
##    320        1.1565             nan     0.0010    0.0001
##    340        1.1501             nan     0.0010    0.0001
##    360        1.1437             nan     0.0010    0.0001
##    380        1.1373             nan     0.0010    0.0001
##    400        1.1312             nan     0.0010    0.0001
##    420        1.1251             nan     0.0010    0.0001
##    440        1.1192             nan     0.0010    0.0001
##    460        1.1138             nan     0.0010    0.0001
##    480        1.1082             nan     0.0010    0.0001
##    500        1.1029             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2539             nan     0.1000    0.0153
##      2        1.2213             nan     0.1000    0.0143
##      3        1.1972             nan     0.1000    0.0104
##      4        1.1798             nan     0.1000    0.0072
##      5        1.1601             nan     0.1000    0.0090
##      6        1.1451             nan     0.1000    0.0067
##      7        1.1277             nan     0.1000    0.0073
##      8        1.1129             nan     0.1000    0.0067
##      9        1.0991             nan     0.1000    0.0051
##     10        1.0853             nan     0.1000    0.0043
##     20        1.0029             nan     0.1000    0.0012
##     40        0.9222             nan     0.1000   -0.0011
##     60        0.8884             nan     0.1000   -0.0011
##     80        0.8643             nan     0.1000   -0.0000
##    100        0.8468             nan     0.1000   -0.0005
##    120        0.8369             nan     0.1000   -0.0009
##    140        0.8280             nan     0.1000   -0.0013
##    160        0.8181             nan     0.1000   -0.0020
##    180        0.8084             nan     0.1000   -0.0012
##    200        0.7964             nan     0.1000   -0.0005
##    220        0.7874             nan     0.1000   -0.0006
##    240        0.7798             nan     0.1000   -0.0010
##    260        0.7727             nan     0.1000   -0.0015
##    280        0.7674             nan     0.1000   -0.0009
##    300        0.7624             nan     0.1000   -0.0009
##    320        0.7552             nan     0.1000   -0.0009
##    340        0.7495             nan     0.1000   -0.0008
##    360        0.7462             nan     0.1000   -0.0010
##    380        0.7409             nan     0.1000   -0.0012
##    400        0.7349             nan     0.1000   -0.0016
##    420        0.7289             nan     0.1000   -0.0016
##    440        0.7249             nan     0.1000   -0.0007
##    460        0.7191             nan     0.1000   -0.0006
##    480        0.7155             nan     0.1000   -0.0009
##    500        0.7110             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2583             nan     0.1000    0.0161
##      2        1.2280             nan     0.1000    0.0154
##      3        1.2053             nan     0.1000    0.0119
##      4        1.1865             nan     0.1000    0.0066
##      5        1.1688             nan     0.1000    0.0088
##      6        1.1500             nan     0.1000    0.0083
##      7        1.1339             nan     0.1000    0.0073
##      8        1.1191             nan     0.1000    0.0048
##      9        1.1057             nan     0.1000    0.0057
##     10        1.0951             nan     0.1000    0.0040
##     20        1.0016             nan     0.1000    0.0022
##     40        0.9245             nan     0.1000   -0.0001
##     60        0.8883             nan     0.1000   -0.0010
##     80        0.8671             nan     0.1000   -0.0009
##    100        0.8459             nan     0.1000   -0.0001
##    120        0.8322             nan     0.1000   -0.0011
##    140        0.8178             nan     0.1000   -0.0001
##    160        0.8052             nan     0.1000   -0.0011
##    180        0.7966             nan     0.1000   -0.0012
##    200        0.7904             nan     0.1000   -0.0003
##    220        0.7828             nan     0.1000   -0.0010
##    240        0.7761             nan     0.1000   -0.0004
##    260        0.7676             nan     0.1000   -0.0014
##    280        0.7623             nan     0.1000   -0.0014
##    300        0.7578             nan     0.1000   -0.0013
##    320        0.7511             nan     0.1000   -0.0015
##    340        0.7435             nan     0.1000   -0.0006
##    360        0.7386             nan     0.1000   -0.0006
##    380        0.7333             nan     0.1000   -0.0002
##    400        0.7318             nan     0.1000   -0.0011
##    420        0.7257             nan     0.1000   -0.0009
##    440        0.7224             nan     0.1000   -0.0015
##    460        0.7176             nan     0.1000   -0.0009
##    480        0.7139             nan     0.1000   -0.0009
##    500        0.7091             nan     0.1000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2566             nan     0.1000    0.0164
##      2        1.2237             nan     0.1000    0.0153
##      3        1.2005             nan     0.1000    0.0097
##      4        1.1756             nan     0.1000    0.0100
##      5        1.1574             nan     0.1000    0.0077
##      6        1.1408             nan     0.1000    0.0066
##      7        1.1252             nan     0.1000    0.0061
##      8        1.1136             nan     0.1000    0.0039
##      9        1.1008             nan     0.1000    0.0044
##     10        1.0913             nan     0.1000    0.0038
##     20        1.0033             nan     0.1000    0.0008
##     40        0.9235             nan     0.1000   -0.0002
##     60        0.8863             nan     0.1000   -0.0002
##     80        0.8603             nan     0.1000   -0.0006
##    100        0.8395             nan     0.1000   -0.0001
##    120        0.8249             nan     0.1000   -0.0020
##    140        0.8128             nan     0.1000   -0.0008
##    160        0.8044             nan     0.1000   -0.0013
##    180        0.7961             nan     0.1000   -0.0004
##    200        0.7888             nan     0.1000   -0.0015
##    220        0.7815             nan     0.1000   -0.0010
##    240        0.7744             nan     0.1000   -0.0007
##    260        0.7690             nan     0.1000   -0.0007
##    280        0.7619             nan     0.1000   -0.0009
##    300        0.7576             nan     0.1000   -0.0011
##    320        0.7510             nan     0.1000   -0.0012
##    340        0.7468             nan     0.1000   -0.0008
##    360        0.7420             nan     0.1000   -0.0001
##    380        0.7366             nan     0.1000   -0.0009
##    400        0.7329             nan     0.1000   -0.0016
##    420        0.7276             nan     0.1000   -0.0007
##    440        0.7226             nan     0.1000   -0.0004
##    460        0.7181             nan     0.1000   -0.0006
##    480        0.7140             nan     0.1000   -0.0003
##    500        0.7096             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2445             nan     0.1000    0.0219
##      2        1.2040             nan     0.1000    0.0173
##      3        1.1690             nan     0.1000    0.0152
##      4        1.1425             nan     0.1000    0.0111
##      5        1.1178             nan     0.1000    0.0114
##      6        1.0985             nan     0.1000    0.0052
##      7        1.0771             nan     0.1000    0.0087
##      8        1.0596             nan     0.1000    0.0057
##      9        1.0455             nan     0.1000    0.0054
##     10        1.0298             nan     0.1000    0.0061
##     20        0.9388             nan     0.1000    0.0018
##     40        0.8641             nan     0.1000   -0.0005
##     60        0.8151             nan     0.1000   -0.0016
##     80        0.7787             nan     0.1000   -0.0011
##    100        0.7498             nan     0.1000   -0.0013
##    120        0.7211             nan     0.1000   -0.0018
##    140        0.7040             nan     0.1000   -0.0004
##    160        0.6825             nan     0.1000   -0.0005
##    180        0.6593             nan     0.1000   -0.0004
##    200        0.6417             nan     0.1000   -0.0004
##    220        0.6264             nan     0.1000   -0.0021
##    240        0.6114             nan     0.1000   -0.0003
##    260        0.5981             nan     0.1000   -0.0009
##    280        0.5801             nan     0.1000   -0.0019
##    300        0.5677             nan     0.1000   -0.0008
##    320        0.5508             nan     0.1000   -0.0012
##    340        0.5371             nan     0.1000   -0.0014
##    360        0.5246             nan     0.1000   -0.0002
##    380        0.5134             nan     0.1000   -0.0006
##    400        0.5030             nan     0.1000   -0.0016
##    420        0.4930             nan     0.1000   -0.0008
##    440        0.4811             nan     0.1000   -0.0022
##    460        0.4713             nan     0.1000   -0.0011
##    480        0.4608             nan     0.1000   -0.0015
##    500        0.4491             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2476             nan     0.1000    0.0222
##      2        1.2108             nan     0.1000    0.0169
##      3        1.1741             nan     0.1000    0.0155
##      4        1.1461             nan     0.1000    0.0120
##      5        1.1244             nan     0.1000    0.0099
##      6        1.1010             nan     0.1000    0.0092
##      7        1.0807             nan     0.1000    0.0087
##      8        1.0618             nan     0.1000    0.0085
##      9        1.0456             nan     0.1000    0.0054
##     10        1.0338             nan     0.1000    0.0044
##     20        0.9454             nan     0.1000    0.0007
##     40        0.8622             nan     0.1000   -0.0006
##     60        0.8155             nan     0.1000    0.0002
##     80        0.7850             nan     0.1000   -0.0017
##    100        0.7636             nan     0.1000   -0.0023
##    120        0.7386             nan     0.1000   -0.0012
##    140        0.7175             nan     0.1000   -0.0012
##    160        0.6958             nan     0.1000   -0.0011
##    180        0.6751             nan     0.1000   -0.0031
##    200        0.6561             nan     0.1000   -0.0002
##    220        0.6411             nan     0.1000   -0.0019
##    240        0.6273             nan     0.1000   -0.0017
##    260        0.6129             nan     0.1000   -0.0007
##    280        0.5972             nan     0.1000   -0.0007
##    300        0.5843             nan     0.1000   -0.0009
##    320        0.5665             nan     0.1000   -0.0010
##    340        0.5554             nan     0.1000   -0.0012
##    360        0.5448             nan     0.1000   -0.0009
##    380        0.5303             nan     0.1000   -0.0009
##    400        0.5204             nan     0.1000   -0.0017
##    420        0.5094             nan     0.1000   -0.0006
##    440        0.4958             nan     0.1000   -0.0003
##    460        0.4874             nan     0.1000   -0.0011
##    480        0.4753             nan     0.1000   -0.0016
##    500        0.4659             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2503             nan     0.1000    0.0194
##      2        1.2084             nan     0.1000    0.0160
##      3        1.1743             nan     0.1000    0.0154
##      4        1.1444             nan     0.1000    0.0124
##      5        1.1158             nan     0.1000    0.0098
##      6        1.0933             nan     0.1000    0.0082
##      7        1.0758             nan     0.1000    0.0068
##      8        1.0579             nan     0.1000    0.0076
##      9        1.0413             nan     0.1000    0.0074
##     10        1.0264             nan     0.1000    0.0060
##     20        0.9354             nan     0.1000    0.0022
##     40        0.8603             nan     0.1000   -0.0010
##     60        0.8180             nan     0.1000   -0.0014
##     80        0.7864             nan     0.1000   -0.0017
##    100        0.7579             nan     0.1000   -0.0023
##    120        0.7326             nan     0.1000   -0.0016
##    140        0.7087             nan     0.1000   -0.0010
##    160        0.6877             nan     0.1000   -0.0008
##    180        0.6664             nan     0.1000   -0.0012
##    200        0.6496             nan     0.1000   -0.0013
##    220        0.6298             nan     0.1000   -0.0016
##    240        0.6119             nan     0.1000   -0.0011
##    260        0.5978             nan     0.1000   -0.0004
##    280        0.5856             nan     0.1000   -0.0022
##    300        0.5700             nan     0.1000   -0.0006
##    320        0.5572             nan     0.1000   -0.0015
##    340        0.5475             nan     0.1000   -0.0022
##    360        0.5318             nan     0.1000   -0.0012
##    380        0.5182             nan     0.1000   -0.0006
##    400        0.5062             nan     0.1000   -0.0013
##    420        0.4955             nan     0.1000   -0.0020
##    440        0.4852             nan     0.1000   -0.0014
##    460        0.4754             nan     0.1000   -0.0008
##    480        0.4658             nan     0.1000   -0.0008
##    500        0.4568             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2367             nan     0.1000    0.0261
##      2        1.1973             nan     0.1000    0.0172
##      3        1.1605             nan     0.1000    0.0164
##      4        1.1266             nan     0.1000    0.0136
##      5        1.0992             nan     0.1000    0.0105
##      6        1.0761             nan     0.1000    0.0079
##      7        1.0537             nan     0.1000    0.0077
##      8        1.0343             nan     0.1000    0.0086
##      9        1.0164             nan     0.1000    0.0050
##     10        1.0027             nan     0.1000    0.0017
##     20        0.9008             nan     0.1000   -0.0035
##     40        0.8060             nan     0.1000   -0.0001
##     60        0.7541             nan     0.1000   -0.0017
##     80        0.7111             nan     0.1000   -0.0024
##    100        0.6675             nan     0.1000   -0.0003
##    120        0.6284             nan     0.1000   -0.0014
##    140        0.5950             nan     0.1000   -0.0022
##    160        0.5700             nan     0.1000   -0.0002
##    180        0.5496             nan     0.1000   -0.0011
##    200        0.5220             nan     0.1000   -0.0023
##    220        0.4939             nan     0.1000   -0.0011
##    240        0.4750             nan     0.1000   -0.0012
##    260        0.4557             nan     0.1000   -0.0015
##    280        0.4339             nan     0.1000   -0.0011
##    300        0.4198             nan     0.1000   -0.0013
##    320        0.4075             nan     0.1000   -0.0019
##    340        0.3919             nan     0.1000   -0.0014
##    360        0.3762             nan     0.1000   -0.0005
##    380        0.3633             nan     0.1000   -0.0017
##    400        0.3484             nan     0.1000   -0.0006
##    420        0.3348             nan     0.1000   -0.0004
##    440        0.3239             nan     0.1000   -0.0016
##    460        0.3123             nan     0.1000   -0.0008
##    480        0.3022             nan     0.1000   -0.0008
##    500        0.2929             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2415             nan     0.1000    0.0200
##      2        1.1984             nan     0.1000    0.0182
##      3        1.1583             nan     0.1000    0.0184
##      4        1.1267             nan     0.1000    0.0123
##      5        1.0999             nan     0.1000    0.0131
##      6        1.0761             nan     0.1000    0.0098
##      7        1.0546             nan     0.1000    0.0071
##      8        1.0357             nan     0.1000    0.0063
##      9        1.0168             nan     0.1000    0.0072
##     10        1.0002             nan     0.1000    0.0041
##     20        0.9004             nan     0.1000    0.0021
##     40        0.8068             nan     0.1000   -0.0012
##     60        0.7519             nan     0.1000   -0.0007
##     80        0.7087             nan     0.1000   -0.0023
##    100        0.6761             nan     0.1000   -0.0023
##    120        0.6380             nan     0.1000   -0.0010
##    140        0.6052             nan     0.1000   -0.0015
##    160        0.5761             nan     0.1000   -0.0016
##    180        0.5480             nan     0.1000   -0.0007
##    200        0.5235             nan     0.1000   -0.0014
##    220        0.4975             nan     0.1000   -0.0017
##    240        0.4791             nan     0.1000   -0.0024
##    260        0.4579             nan     0.1000   -0.0014
##    280        0.4363             nan     0.1000   -0.0011
##    300        0.4190             nan     0.1000   -0.0009
##    320        0.4034             nan     0.1000   -0.0006
##    340        0.3878             nan     0.1000   -0.0004
##    360        0.3723             nan     0.1000   -0.0015
##    380        0.3569             nan     0.1000   -0.0009
##    400        0.3425             nan     0.1000   -0.0006
##    420        0.3289             nan     0.1000   -0.0008
##    440        0.3159             nan     0.1000   -0.0007
##    460        0.3032             nan     0.1000   -0.0003
##    480        0.2938             nan     0.1000   -0.0007
##    500        0.2841             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2393             nan     0.1000    0.0262
##      2        1.1987             nan     0.1000    0.0182
##      3        1.1576             nan     0.1000    0.0177
##      4        1.1282             nan     0.1000    0.0121
##      5        1.0999             nan     0.1000    0.0103
##      6        1.0797             nan     0.1000    0.0076
##      7        1.0578             nan     0.1000    0.0084
##      8        1.0357             nan     0.1000    0.0085
##      9        1.0191             nan     0.1000    0.0062
##     10        1.0061             nan     0.1000    0.0028
##     20        0.9023             nan     0.1000    0.0005
##     40        0.8139             nan     0.1000   -0.0003
##     60        0.7567             nan     0.1000    0.0005
##     80        0.7101             nan     0.1000   -0.0005
##    100        0.6680             nan     0.1000   -0.0009
##    120        0.6335             nan     0.1000   -0.0018
##    140        0.6059             nan     0.1000   -0.0021
##    160        0.5795             nan     0.1000   -0.0017
##    180        0.5562             nan     0.1000   -0.0013
##    200        0.5353             nan     0.1000   -0.0010
##    220        0.5173             nan     0.1000   -0.0004
##    240        0.4967             nan     0.1000   -0.0006
##    260        0.4734             nan     0.1000    0.0000
##    280        0.4539             nan     0.1000   -0.0009
##    300        0.4378             nan     0.1000   -0.0019
##    320        0.4187             nan     0.1000   -0.0010
##    340        0.4037             nan     0.1000   -0.0007
##    360        0.3881             nan     0.1000   -0.0001
##    380        0.3753             nan     0.1000   -0.0005
##    400        0.3605             nan     0.1000   -0.0011
##    420        0.3464             nan     0.1000   -0.0006
##    440        0.3318             nan     0.1000   -0.0017
##    460        0.3185             nan     0.1000   -0.0010
##    480        0.3062             nan     0.1000   -0.0008
##    500        0.2956             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2256             nan     0.2000    0.0320
##      2        1.1799             nan     0.2000    0.0218
##      3        1.1523             nan     0.2000    0.0119
##      4        1.1201             nan     0.2000    0.0119
##      5        1.0923             nan     0.2000    0.0127
##      6        1.0773             nan     0.2000    0.0055
##      7        1.0571             nan     0.2000    0.0067
##      8        1.0359             nan     0.2000    0.0083
##      9        1.0240             nan     0.2000    0.0039
##     10        1.0124             nan     0.2000    0.0004
##     20        0.9344             nan     0.2000    0.0019
##     40        0.8667             nan     0.2000   -0.0008
##     60        0.8347             nan     0.2000   -0.0022
##     80        0.8118             nan     0.2000   -0.0019
##    100        0.7928             nan     0.2000   -0.0019
##    120        0.7806             nan     0.2000   -0.0018
##    140        0.7657             nan     0.2000   -0.0024
##    160        0.7532             nan     0.2000   -0.0014
##    180        0.7433             nan     0.2000   -0.0023
##    200        0.7302             nan     0.2000   -0.0016
##    220        0.7193             nan     0.2000   -0.0011
##    240        0.7118             nan     0.2000   -0.0018
##    260        0.7044             nan     0.2000   -0.0011
##    280        0.6968             nan     0.2000   -0.0018
##    300        0.6949             nan     0.2000   -0.0022
##    320        0.6855             nan     0.2000   -0.0028
##    340        0.6797             nan     0.2000   -0.0025
##    360        0.6754             nan     0.2000   -0.0053
##    380        0.6678             nan     0.2000   -0.0020
##    400        0.6614             nan     0.2000   -0.0019
##    420        0.6559             nan     0.2000   -0.0026
##    440        0.6475             nan     0.2000   -0.0024
##    460        0.6394             nan     0.2000   -0.0009
##    480        0.6336             nan     0.2000   -0.0023
##    500        0.6280             nan     0.2000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2352             nan     0.2000    0.0308
##      2        1.1777             nan     0.2000    0.0201
##      3        1.1411             nan     0.2000    0.0135
##      4        1.1078             nan     0.2000    0.0116
##      5        1.0828             nan     0.2000    0.0081
##      6        1.0610             nan     0.2000    0.0093
##      7        1.0440             nan     0.2000    0.0067
##      8        1.0331             nan     0.2000    0.0023
##      9        1.0146             nan     0.2000    0.0050
##     10        1.0009             nan     0.2000    0.0040
##     20        0.9293             nan     0.2000    0.0001
##     40        0.8831             nan     0.2000   -0.0016
##     60        0.8478             nan     0.2000   -0.0029
##     80        0.8245             nan     0.2000   -0.0005
##    100        0.8087             nan     0.2000   -0.0032
##    120        0.7947             nan     0.2000   -0.0016
##    140        0.7792             nan     0.2000   -0.0019
##    160        0.7659             nan     0.2000   -0.0014
##    180        0.7556             nan     0.2000   -0.0011
##    200        0.7447             nan     0.2000   -0.0041
##    220        0.7335             nan     0.2000   -0.0012
##    240        0.7239             nan     0.2000   -0.0024
##    260        0.7160             nan     0.2000   -0.0011
##    280        0.7074             nan     0.2000   -0.0023
##    300        0.7009             nan     0.2000   -0.0019
##    320        0.6902             nan     0.2000   -0.0018
##    340        0.6805             nan     0.2000   -0.0010
##    360        0.6753             nan     0.2000   -0.0017
##    380        0.6692             nan     0.2000   -0.0039
##    400        0.6613             nan     0.2000   -0.0015
##    420        0.6552             nan     0.2000   -0.0023
##    440        0.6489             nan     0.2000   -0.0027
##    460        0.6438             nan     0.2000   -0.0013
##    480        0.6371             nan     0.2000   -0.0021
##    500        0.6342             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2206             nan     0.2000    0.0263
##      2        1.1696             nan     0.2000    0.0210
##      3        1.1356             nan     0.2000    0.0127
##      4        1.1079             nan     0.2000    0.0124
##      5        1.0825             nan     0.2000    0.0099
##      6        1.0602             nan     0.2000    0.0115
##      7        1.0411             nan     0.2000    0.0068
##      8        1.0275             nan     0.2000    0.0053
##      9        1.0071             nan     0.2000    0.0083
##     10        0.9974             nan     0.2000    0.0039
##     20        0.9262             nan     0.2000   -0.0006
##     40        0.8646             nan     0.2000   -0.0004
##     60        0.8364             nan     0.2000   -0.0016
##     80        0.8210             nan     0.2000   -0.0032
##    100        0.8045             nan     0.2000   -0.0021
##    120        0.7923             nan     0.2000   -0.0043
##    140        0.7757             nan     0.2000   -0.0012
##    160        0.7618             nan     0.2000   -0.0019
##    180        0.7526             nan     0.2000   -0.0019
##    200        0.7442             nan     0.2000   -0.0007
##    220        0.7395             nan     0.2000   -0.0018
##    240        0.7323             nan     0.2000   -0.0020
##    260        0.7218             nan     0.2000   -0.0025
##    280        0.7170             nan     0.2000   -0.0024
##    300        0.7087             nan     0.2000   -0.0007
##    320        0.6963             nan     0.2000   -0.0012
##    340        0.6903             nan     0.2000   -0.0019
##    360        0.6818             nan     0.2000   -0.0015
##    380        0.6756             nan     0.2000   -0.0013
##    400        0.6695             nan     0.2000   -0.0011
##    420        0.6639             nan     0.2000   -0.0012
##    440        0.6558             nan     0.2000   -0.0033
##    460        0.6505             nan     0.2000   -0.0018
##    480        0.6443             nan     0.2000   -0.0020
##    500        0.6398             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2091             nan     0.2000    0.0372
##      2        1.1424             nan     0.2000    0.0264
##      3        1.0979             nan     0.2000    0.0198
##      4        1.0637             nan     0.2000    0.0147
##      5        1.0335             nan     0.2000    0.0041
##      6        1.0138             nan     0.2000    0.0071
##      7        0.9881             nan     0.2000    0.0072
##      8        0.9733             nan     0.2000    0.0014
##      9        0.9570             nan     0.2000    0.0029
##     10        0.9456             nan     0.2000    0.0041
##     20        0.8629             nan     0.2000    0.0001
##     40        0.7967             nan     0.2000   -0.0035
##     60        0.7374             nan     0.2000    0.0003
##     80        0.6988             nan     0.2000   -0.0023
##    100        0.6708             nan     0.2000   -0.0020
##    120        0.6396             nan     0.2000   -0.0019
##    140        0.6129             nan     0.2000   -0.0031
##    160        0.5872             nan     0.2000   -0.0022
##    180        0.5611             nan     0.2000   -0.0019
##    200        0.5308             nan     0.2000   -0.0017
##    220        0.5012             nan     0.2000   -0.0026
##    240        0.4768             nan     0.2000   -0.0027
##    260        0.4593             nan     0.2000   -0.0023
##    280        0.4420             nan     0.2000   -0.0012
##    300        0.4205             nan     0.2000   -0.0025
##    320        0.4059             nan     0.2000   -0.0028
##    340        0.3876             nan     0.2000   -0.0035
##    360        0.3727             nan     0.2000   -0.0002
##    380        0.3598             nan     0.2000   -0.0025
##    400        0.3484             nan     0.2000   -0.0016
##    420        0.3350             nan     0.2000   -0.0026
##    440        0.3237             nan     0.2000   -0.0010
##    460        0.3110             nan     0.2000   -0.0011
##    480        0.2973             nan     0.2000   -0.0005
##    500        0.2880             nan     0.2000   -0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2035             nan     0.2000    0.0428
##      2        1.1485             nan     0.2000    0.0188
##      3        1.1029             nan     0.2000    0.0200
##      4        1.0623             nan     0.2000    0.0149
##      5        1.0265             nan     0.2000    0.0123
##      6        1.0050             nan     0.2000    0.0060
##      7        0.9900             nan     0.2000    0.0025
##      8        0.9737             nan     0.2000    0.0030
##      9        0.9565             nan     0.2000    0.0050
##     10        0.9431             nan     0.2000    0.0049
##     20        0.8741             nan     0.2000   -0.0008
##     40        0.8013             nan     0.2000   -0.0031
##     60        0.7442             nan     0.2000   -0.0055
##     80        0.6999             nan     0.2000   -0.0029
##    100        0.6591             nan     0.2000   -0.0007
##    120        0.6303             nan     0.2000   -0.0013
##    140        0.5990             nan     0.2000   -0.0026
##    160        0.5718             nan     0.2000   -0.0037
##    180        0.5447             nan     0.2000   -0.0045
##    200        0.5211             nan     0.2000   -0.0028
##    220        0.4945             nan     0.2000   -0.0012
##    240        0.4693             nan     0.2000   -0.0019
##    260        0.4452             nan     0.2000   -0.0004
##    280        0.4248             nan     0.2000   -0.0017
##    300        0.4111             nan     0.2000   -0.0019
##    320        0.3931             nan     0.2000   -0.0021
##    340        0.3781             nan     0.2000   -0.0001
##    360        0.3641             nan     0.2000   -0.0010
##    380        0.3516             nan     0.2000   -0.0017
##    400        0.3405             nan     0.2000   -0.0030
##    420        0.3336             nan     0.2000   -0.0017
##    440        0.3182             nan     0.2000   -0.0008
##    460        0.3063             nan     0.2000   -0.0014
##    480        0.2955             nan     0.2000   -0.0014
##    500        0.2857             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2115             nan     0.2000    0.0367
##      2        1.1430             nan     0.2000    0.0290
##      3        1.0973             nan     0.2000    0.0154
##      4        1.0569             nan     0.2000    0.0177
##      5        1.0230             nan     0.2000    0.0083
##      6        0.9945             nan     0.2000    0.0129
##      7        0.9770             nan     0.2000    0.0039
##      8        0.9632             nan     0.2000    0.0027
##      9        0.9507             nan     0.2000    0.0034
##     10        0.9378             nan     0.2000    0.0047
##     20        0.8670             nan     0.2000   -0.0034
##     40        0.7920             nan     0.2000   -0.0035
##     60        0.7325             nan     0.2000   -0.0016
##     80        0.6908             nan     0.2000   -0.0028
##    100        0.6562             nan     0.2000   -0.0013
##    120        0.6235             nan     0.2000   -0.0032
##    140        0.5890             nan     0.2000   -0.0021
##    160        0.5573             nan     0.2000   -0.0010
##    180        0.5288             nan     0.2000   -0.0033
##    200        0.5079             nan     0.2000   -0.0020
##    220        0.4855             nan     0.2000   -0.0021
##    240        0.4668             nan     0.2000   -0.0014
##    260        0.4524             nan     0.2000   -0.0014
##    280        0.4355             nan     0.2000   -0.0010
##    300        0.4207             nan     0.2000   -0.0037
##    320        0.4051             nan     0.2000   -0.0013
##    340        0.3897             nan     0.2000   -0.0016
##    360        0.3752             nan     0.2000   -0.0023
##    380        0.3624             nan     0.2000   -0.0026
##    400        0.3525             nan     0.2000   -0.0021
##    420        0.3366             nan     0.2000   -0.0014
##    440        0.3220             nan     0.2000   -0.0020
##    460        0.3068             nan     0.2000   -0.0007
##    480        0.2942             nan     0.2000   -0.0018
##    500        0.2831             nan     0.2000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2118             nan     0.2000    0.0321
##      2        1.1242             nan     0.2000    0.0370
##      3        1.0652             nan     0.2000    0.0222
##      4        1.0222             nan     0.2000    0.0154
##      5        0.9956             nan     0.2000    0.0052
##      6        0.9737             nan     0.2000    0.0093
##      7        0.9524             nan     0.2000    0.0061
##      8        0.9359             nan     0.2000    0.0019
##      9        0.9222             nan     0.2000    0.0015
##     10        0.9071             nan     0.2000   -0.0016
##     20        0.8253             nan     0.2000   -0.0042
##     40        0.7179             nan     0.2000   -0.0049
##     60        0.6550             nan     0.2000   -0.0015
##     80        0.5971             nan     0.2000   -0.0065
##    100        0.5257             nan     0.2000   -0.0018
##    120        0.4843             nan     0.2000   -0.0001
##    140        0.4415             nan     0.2000   -0.0012
##    160        0.3983             nan     0.2000   -0.0018
##    180        0.3629             nan     0.2000   -0.0011
##    200        0.3356             nan     0.2000   -0.0011
##    220        0.3115             nan     0.2000   -0.0018
##    240        0.2906             nan     0.2000   -0.0011
##    260        0.2675             nan     0.2000   -0.0011
##    280        0.2503             nan     0.2000   -0.0016
##    300        0.2339             nan     0.2000   -0.0010
##    320        0.2191             nan     0.2000   -0.0012
##    340        0.2056             nan     0.2000   -0.0005
##    360        0.1929             nan     0.2000   -0.0016
##    380        0.1796             nan     0.2000   -0.0008
##    400        0.1695             nan     0.2000   -0.0009
##    420        0.1609             nan     0.2000   -0.0008
##    440        0.1516             nan     0.2000   -0.0016
##    460        0.1427             nan     0.2000   -0.0007
##    480        0.1324             nan     0.2000   -0.0003
##    500        0.1234             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1938             nan     0.2000    0.0489
##      2        1.1222             nan     0.2000    0.0307
##      3        1.0669             nan     0.2000    0.0145
##      4        1.0201             nan     0.2000    0.0186
##      5        0.9911             nan     0.2000    0.0131
##      6        0.9630             nan     0.2000    0.0088
##      7        0.9484             nan     0.2000    0.0020
##      8        0.9289             nan     0.2000    0.0001
##      9        0.9177             nan     0.2000   -0.0009
##     10        0.9072             nan     0.2000    0.0005
##     20        0.8172             nan     0.2000   -0.0028
##     40        0.7242             nan     0.2000   -0.0078
##     60        0.6527             nan     0.2000   -0.0025
##     80        0.5920             nan     0.2000   -0.0070
##    100        0.5489             nan     0.2000   -0.0011
##    120        0.5025             nan     0.2000   -0.0015
##    140        0.4669             nan     0.2000   -0.0012
##    160        0.4311             nan     0.2000   -0.0012
##    180        0.3981             nan     0.2000   -0.0021
##    200        0.3655             nan     0.2000   -0.0014
##    220        0.3387             nan     0.2000   -0.0028
##    240        0.3140             nan     0.2000   -0.0022
##    260        0.2886             nan     0.2000   -0.0009
##    280        0.2678             nan     0.2000   -0.0021
##    300        0.2473             nan     0.2000   -0.0005
##    320        0.2302             nan     0.2000   -0.0002
##    340        0.2163             nan     0.2000   -0.0012
##    360        0.2023             nan     0.2000   -0.0012
##    380        0.1878             nan     0.2000   -0.0010
##    400        0.1786             nan     0.2000   -0.0012
##    420        0.1677             nan     0.2000   -0.0011
##    440        0.1593             nan     0.2000   -0.0002
##    460        0.1508             nan     0.2000   -0.0001
##    480        0.1432             nan     0.2000   -0.0009
##    500        0.1340             nan     0.2000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1867             nan     0.2000    0.0443
##      2        1.1230             nan     0.2000    0.0284
##      3        1.0711             nan     0.2000    0.0210
##      4        1.0342             nan     0.2000    0.0121
##      5        0.9998             nan     0.2000    0.0070
##      6        0.9728             nan     0.2000    0.0091
##      7        0.9559             nan     0.2000    0.0028
##      8        0.9375             nan     0.2000    0.0021
##      9        0.9201             nan     0.2000    0.0050
##     10        0.9086             nan     0.2000    0.0007
##     20        0.8181             nan     0.2000    0.0002
##     40        0.7242             nan     0.2000   -0.0040
##     60        0.6584             nan     0.2000   -0.0081
##     80        0.5933             nan     0.2000   -0.0022
##    100        0.5360             nan     0.2000   -0.0034
##    120        0.4957             nan     0.2000   -0.0025
##    140        0.4621             nan     0.2000   -0.0025
##    160        0.4292             nan     0.2000   -0.0018
##    180        0.3853             nan     0.2000   -0.0012
##    200        0.3614             nan     0.2000   -0.0030
##    220        0.3384             nan     0.2000   -0.0010
##    240        0.3156             nan     0.2000   -0.0022
##    260        0.2917             nan     0.2000   -0.0011
##    280        0.2704             nan     0.2000   -0.0013
##    300        0.2515             nan     0.2000   -0.0021
##    320        0.2362             nan     0.2000   -0.0009
##    340        0.2200             nan     0.2000   -0.0010
##    360        0.2070             nan     0.2000   -0.0012
##    380        0.1903             nan     0.2000   -0.0004
##    400        0.1760             nan     0.2000   -0.0006
##    420        0.1668             nan     0.2000   -0.0017
##    440        0.1536             nan     0.2000   -0.0009
##    460        0.1428             nan     0.2000   -0.0009
##    480        0.1339             nan     0.2000   -0.0009
##    500        0.1258             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2363             nan     0.3000    0.0200
##      2        1.1506             nan     0.3000    0.0415
##      3        1.0909             nan     0.3000    0.0226
##      4        1.0500             nan     0.3000    0.0127
##      5        1.0319             nan     0.3000    0.0055
##      6        1.0056             nan     0.3000    0.0110
##      7        0.9954             nan     0.3000   -0.0037
##      8        0.9802             nan     0.3000    0.0041
##      9        0.9649             nan     0.3000    0.0031
##     10        0.9558             nan     0.3000    0.0007
##     20        0.8894             nan     0.3000    0.0013
##     40        0.8396             nan     0.3000   -0.0002
##     60        0.8134             nan     0.3000   -0.0031
##     80        0.7919             nan     0.3000   -0.0059
##    100        0.7764             nan     0.3000   -0.0015
##    120        0.7604             nan     0.3000   -0.0008
##    140        0.7509             nan     0.3000   -0.0028
##    160        0.7337             nan     0.3000   -0.0007
##    180        0.7173             nan     0.3000   -0.0045
##    200        0.7092             nan     0.3000   -0.0046
##    220        0.6969             nan     0.3000   -0.0030
##    240        0.6962             nan     0.3000   -0.0019
##    260        0.6897             nan     0.3000   -0.0045
##    280        0.6786             nan     0.3000   -0.0006
##    300        0.6742             nan     0.3000   -0.0019
##    320        0.6641             nan     0.3000   -0.0017
##    340        0.6542             nan     0.3000   -0.0025
##    360        0.6415             nan     0.3000   -0.0034
##    380        0.6385             nan     0.3000   -0.0039
##    400        0.6281             nan     0.3000   -0.0022
##    420        0.6225             nan     0.3000   -0.0010
##    440        0.6155             nan     0.3000   -0.0014
##    460        0.6045             nan     0.3000   -0.0029
##    480        0.5996             nan     0.3000   -0.0032
##    500        0.5900             nan     0.3000   -0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2039             nan     0.3000    0.0467
##      2        1.1430             nan     0.3000    0.0321
##      3        1.0989             nan     0.3000    0.0171
##      4        1.0648             nan     0.3000    0.0115
##      5        1.0226             nan     0.3000    0.0146
##      6        1.0032             nan     0.3000    0.0058
##      7        0.9895             nan     0.3000    0.0031
##      8        0.9775             nan     0.3000    0.0040
##      9        0.9691             nan     0.3000    0.0019
##     10        0.9549             nan     0.3000    0.0008
##     20        0.8869             nan     0.3000   -0.0005
##     40        0.8478             nan     0.3000   -0.0057
##     60        0.8187             nan     0.3000   -0.0053
##     80        0.7926             nan     0.3000   -0.0032
##    100        0.7733             nan     0.3000   -0.0004
##    120        0.7637             nan     0.3000   -0.0005
##    140        0.7455             nan     0.3000   -0.0015
##    160        0.7374             nan     0.3000   -0.0021
##    180        0.7242             nan     0.3000   -0.0034
##    200        0.7119             nan     0.3000   -0.0020
##    220        0.6997             nan     0.3000   -0.0019
##    240        0.6956             nan     0.3000   -0.0026
##    260        0.6835             nan     0.3000   -0.0023
##    280        0.6753             nan     0.3000   -0.0032
##    300        0.6624             nan     0.3000   -0.0022
##    320        0.6482             nan     0.3000   -0.0051
##    340        0.6402             nan     0.3000   -0.0038
##    360        0.6303             nan     0.3000   -0.0048
##    380        0.6248             nan     0.3000   -0.0022
##    400        0.6213             nan     0.3000   -0.0049
##    420        0.6167             nan     0.3000   -0.0015
##    440        0.6074             nan     0.3000   -0.0040
##    460        0.6006             nan     0.3000   -0.0028
##    480        0.5979             nan     0.3000   -0.0018
##    500        0.5928             nan     0.3000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2019             nan     0.3000    0.0447
##      2        1.1506             nan     0.3000    0.0200
##      3        1.1005             nan     0.3000    0.0182
##      4        1.0705             nan     0.3000    0.0096
##      5        1.0422             nan     0.3000    0.0102
##      6        1.0147             nan     0.3000    0.0092
##      7        1.0041             nan     0.3000    0.0043
##      8        0.9857             nan     0.3000    0.0069
##      9        0.9732             nan     0.3000    0.0012
##     10        0.9644             nan     0.3000    0.0040
##     20        0.8889             nan     0.3000    0.0005
##     40        0.8451             nan     0.3000   -0.0001
##     60        0.8101             nan     0.3000   -0.0054
##     80        0.7943             nan     0.3000   -0.0060
##    100        0.7744             nan     0.3000   -0.0027
##    120        0.7560             nan     0.3000   -0.0011
##    140        0.7405             nan     0.3000   -0.0014
##    160        0.7290             nan     0.3000   -0.0021
##    180        0.7133             nan     0.3000   -0.0022
##    200        0.7025             nan     0.3000   -0.0076
##    220        0.6947             nan     0.3000   -0.0017
##    240        0.6848             nan     0.3000   -0.0041
##    260        0.6737             nan     0.3000   -0.0020
##    280        0.6694             nan     0.3000   -0.0025
##    300        0.6577             nan     0.3000   -0.0029
##    320        0.6536             nan     0.3000   -0.0054
##    340        0.6471             nan     0.3000   -0.0005
##    360        0.6427             nan     0.3000   -0.0015
##    380        0.6325             nan     0.3000   -0.0015
##    400        0.6285             nan     0.3000   -0.0005
##    420        0.6214             nan     0.3000   -0.0027
##    440        0.6120             nan     0.3000   -0.0026
##    460        0.6025             nan     0.3000   -0.0032
##    480        0.5965             nan     0.3000   -0.0030
##    500        0.5897             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1703             nan     0.3000    0.0556
##      2        1.0888             nan     0.3000    0.0341
##      3        1.0505             nan     0.3000    0.0084
##      4        1.0082             nan     0.3000    0.0180
##      5        0.9842             nan     0.3000    0.0030
##      6        0.9698             nan     0.3000    0.0033
##      7        0.9526             nan     0.3000    0.0051
##      8        0.9302             nan     0.3000    0.0046
##      9        0.9223             nan     0.3000   -0.0090
##     10        0.9106             nan     0.3000   -0.0009
##     20        0.8423             nan     0.3000   -0.0072
##     40        0.7603             nan     0.3000   -0.0046
##     60        0.6979             nan     0.3000   -0.0047
##     80        0.6455             nan     0.3000   -0.0031
##    100        0.6151             nan     0.3000   -0.0079
##    120        0.5752             nan     0.3000   -0.0008
##    140        0.5366             nan     0.3000   -0.0039
##    160        0.5037             nan     0.3000   -0.0013
##    180        0.4564             nan     0.3000   -0.0035
##    200        0.4317             nan     0.3000   -0.0030
##    220        0.4112             nan     0.3000   -0.0019
##    240        0.3856             nan     0.3000   -0.0028
##    260        0.3654             nan     0.3000   -0.0013
##    280        0.3381             nan     0.3000   -0.0017
##    300        0.3152             nan     0.3000   -0.0028
##    320        0.3003             nan     0.3000   -0.0015
##    340        0.2799             nan     0.3000   -0.0015
##    360        0.2646             nan     0.3000   -0.0014
##    380        0.2514             nan     0.3000   -0.0020
##    400        0.2360             nan     0.3000   -0.0018
##    420        0.2265             nan     0.3000   -0.0023
##    440        0.2165             nan     0.3000   -0.0004
##    460        0.2049             nan     0.3000   -0.0004
##    480        0.1951             nan     0.3000   -0.0007
##    500        0.1844             nan     0.3000   -0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1642             nan     0.3000    0.0541
##      2        1.0945             nan     0.3000    0.0303
##      3        1.0501             nan     0.3000    0.0110
##      4        0.9976             nan     0.3000    0.0221
##      5        0.9703             nan     0.3000    0.0081
##      6        0.9502             nan     0.3000    0.0058
##      7        0.9310             nan     0.3000    0.0023
##      8        0.9210             nan     0.3000   -0.0028
##      9        0.9082             nan     0.3000    0.0028
##     10        0.8996             nan     0.3000   -0.0046
##     20        0.8211             nan     0.3000   -0.0087
##     40        0.7549             nan     0.3000   -0.0019
##     60        0.6970             nan     0.3000   -0.0057
##     80        0.6318             nan     0.3000   -0.0041
##    100        0.5917             nan     0.3000   -0.0055
##    120        0.5531             nan     0.3000   -0.0038
##    140        0.5122             nan     0.3000   -0.0031
##    160        0.4854             nan     0.3000   -0.0025
##    180        0.4556             nan     0.3000   -0.0015
##    200        0.4279             nan     0.3000   -0.0016
##    220        0.4006             nan     0.3000   -0.0048
##    240        0.3829             nan     0.3000   -0.0020
##    260        0.3619             nan     0.3000   -0.0022
##    280        0.3406             nan     0.3000   -0.0036
##    300        0.3139             nan     0.3000   -0.0018
##    320        0.2952             nan     0.3000   -0.0043
##    340        0.2805             nan     0.3000   -0.0011
##    360        0.2591             nan     0.3000   -0.0008
##    380        0.2459             nan     0.3000   -0.0020
##    400        0.2334             nan     0.3000   -0.0016
##    420        0.2247             nan     0.3000   -0.0028
##    440        0.2162             nan     0.3000   -0.0019
##    460        0.2067             nan     0.3000   -0.0005
##    480        0.2001             nan     0.3000   -0.0026
##    500        0.1939             nan     0.3000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1735             nan     0.3000    0.0461
##      2        1.0984             nan     0.3000    0.0317
##      3        1.0460             nan     0.3000    0.0188
##      4        1.0065             nan     0.3000    0.0125
##      5        0.9852             nan     0.3000    0.0056
##      6        0.9630             nan     0.3000    0.0066
##      7        0.9470             nan     0.3000   -0.0003
##      8        0.9256             nan     0.3000    0.0050
##      9        0.9085             nan     0.3000    0.0048
##     10        0.8954             nan     0.3000    0.0024
##     20        0.8293             nan     0.3000    0.0001
##     40        0.7545             nan     0.3000   -0.0028
##     60        0.6990             nan     0.3000   -0.0046
##     80        0.6408             nan     0.3000   -0.0060
##    100        0.6057             nan     0.3000   -0.0003
##    120        0.5672             nan     0.3000   -0.0023
##    140        0.5332             nan     0.3000   -0.0023
##    160        0.4958             nan     0.3000    0.0007
##    180        0.4757             nan     0.3000   -0.0008
##    200        0.4524             nan     0.3000   -0.0029
##    220        0.4225             nan     0.3000   -0.0029
##    240        0.4001             nan     0.3000   -0.0021
##    260        0.3692             nan     0.3000   -0.0008
##    280        0.3529             nan     0.3000   -0.0019
##    300        0.3325             nan     0.3000   -0.0022
##    320        0.3154             nan     0.3000   -0.0036
##    340        0.2873             nan     0.3000   -0.0050
##    360        0.2703             nan     0.3000   -0.0007
##    380        0.2559             nan     0.3000   -0.0023
##    400        0.2451             nan     0.3000   -0.0018
##    420        0.2329             nan     0.3000   -0.0010
##    440        0.2188             nan     0.3000   -0.0013
##    460        0.2036             nan     0.3000   -0.0005
##    480        0.1943             nan     0.3000   -0.0034
##    500        0.1828             nan     0.3000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1600             nan     0.3000    0.0511
##      2        1.0753             nan     0.3000    0.0373
##      3        1.0204             nan     0.3000    0.0179
##      4        0.9846             nan     0.3000    0.0095
##      5        0.9563             nan     0.3000    0.0021
##      6        0.9367             nan     0.3000   -0.0035
##      7        0.9095             nan     0.3000    0.0069
##      8        0.8923             nan     0.3000    0.0023
##      9        0.8720             nan     0.3000    0.0046
##     10        0.8599             nan     0.3000   -0.0021
##     20        0.7779             nan     0.3000   -0.0072
##     40        0.6703             nan     0.3000   -0.0097
##     60        0.5859             nan     0.3000   -0.0011
##     80        0.5078             nan     0.3000   -0.0021
##    100        0.4477             nan     0.3000   -0.0055
##    120        0.4031             nan     0.3000   -0.0020
##    140        0.3409             nan     0.3000   -0.0011
##    160        0.3061             nan     0.3000   -0.0036
##    180        0.2750             nan     0.3000   -0.0017
##    200        0.2490             nan     0.3000   -0.0026
##    220        0.2246             nan     0.3000   -0.0027
##    240        0.2024             nan     0.3000   -0.0017
##    260        0.1815             nan     0.3000   -0.0009
##    280        0.1638             nan     0.3000   -0.0009
##    300        0.1476             nan     0.3000   -0.0001
##    320        0.1336             nan     0.3000   -0.0007
##    340        0.1222             nan     0.3000   -0.0005
##    360        0.1110             nan     0.3000   -0.0006
##    380        0.1016             nan     0.3000   -0.0013
##    400        0.0937             nan     0.3000   -0.0013
##    420        0.0872             nan     0.3000   -0.0008
##    440        0.0806             nan     0.3000   -0.0004
##    460        0.0739             nan     0.3000   -0.0003
##    480        0.0696             nan     0.3000   -0.0003
##    500        0.0648             nan     0.3000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1559             nan     0.3000    0.0554
##      2        1.0671             nan     0.3000    0.0375
##      3        1.0153             nan     0.3000    0.0203
##      4        0.9798             nan     0.3000    0.0083
##      5        0.9516             nan     0.3000    0.0021
##      6        0.9190             nan     0.3000   -0.0029
##      7        0.9051             nan     0.3000   -0.0057
##      8        0.8838             nan     0.3000    0.0055
##      9        0.8665             nan     0.3000   -0.0010
##     10        0.8531             nan     0.3000   -0.0080
##     20        0.7544             nan     0.3000   -0.0043
##     40        0.6598             nan     0.3000   -0.0018
##     60        0.5500             nan     0.3000   -0.0067
##     80        0.4839             nan     0.3000   -0.0039
##    100        0.4237             nan     0.3000   -0.0048
##    120        0.3685             nan     0.3000   -0.0033
##    140        0.3295             nan     0.3000   -0.0015
##    160        0.2942             nan     0.3000   -0.0018
##    180        0.2636             nan     0.3000   -0.0008
##    200        0.2392             nan     0.3000   -0.0010
##    220        0.2178             nan     0.3000   -0.0032
##    240        0.1987             nan     0.3000   -0.0031
##    260        0.1795             nan     0.3000   -0.0008
##    280        0.1638             nan     0.3000   -0.0003
##    300        0.1494             nan     0.3000   -0.0013
##    320        0.1345             nan     0.3000   -0.0008
##    340        0.1244             nan     0.3000   -0.0001
##    360        0.1126             nan     0.3000   -0.0019
##    380        0.1007             nan     0.3000   -0.0002
##    400        0.0930             nan     0.3000   -0.0011
##    420        0.0872             nan     0.3000   -0.0003
##    440        0.0799             nan     0.3000   -0.0000
##    460        0.0734             nan     0.3000   -0.0005
##    480        0.0684             nan     0.3000   -0.0013
##    500        0.0637             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1598             nan     0.3000    0.0606
##      2        1.0663             nan     0.3000    0.0344
##      3        1.0117             nan     0.3000    0.0139
##      4        0.9730             nan     0.3000    0.0137
##      5        0.9476             nan     0.3000   -0.0063
##      6        0.9230             nan     0.3000    0.0059
##      7        0.9097             nan     0.3000   -0.0017
##      8        0.9006             nan     0.3000   -0.0062
##      9        0.8835             nan     0.3000   -0.0031
##     10        0.8705             nan     0.3000   -0.0037
##     20        0.7772             nan     0.3000   -0.0077
##     40        0.6546             nan     0.3000   -0.0020
##     60        0.6023             nan     0.3000   -0.0060
##     80        0.5115             nan     0.3000    0.0001
##    100        0.4509             nan     0.3000   -0.0041
##    120        0.3921             nan     0.3000   -0.0014
##    140        0.3455             nan     0.3000   -0.0026
##    160        0.3013             nan     0.3000   -0.0022
##    180        0.2613             nan     0.3000   -0.0005
##    200        0.2374             nan     0.3000   -0.0022
##    220        0.2183             nan     0.3000   -0.0024
##    240        0.1939             nan     0.3000   -0.0013
##    260        0.1761             nan     0.3000   -0.0023
##    280        0.1619             nan     0.3000   -0.0022
##    300        0.1476             nan     0.3000   -0.0013
##    320        0.1326             nan     0.3000    0.0000
##    340        0.1204             nan     0.3000   -0.0011
##    360        0.1099             nan     0.3000   -0.0005
##    380        0.1013             nan     0.3000   -0.0008
##    400        0.0918             nan     0.3000   -0.0007
##    420        0.0837             nan     0.3000   -0.0008
##    440        0.0784             nan     0.3000   -0.0006
##    460        0.0722             nan     0.3000   -0.0005
##    480        0.0656             nan     0.3000   -0.0008
##    500        0.0615             nan     0.3000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1728             nan     0.5000    0.0484
##      2        1.0864             nan     0.5000    0.0288
##      3        1.0332             nan     0.5000    0.0239
##      4        1.0041             nan     0.5000    0.0008
##      5        0.9755             nan     0.5000    0.0147
##      6        0.9669             nan     0.5000   -0.0017
##      7        0.9528             nan     0.5000    0.0018
##      8        0.9484             nan     0.5000   -0.0029
##      9        0.9330             nan     0.5000    0.0047
##     10        0.9247             nan     0.5000    0.0016
##     20        0.8721             nan     0.5000   -0.0012
##     40        0.8356             nan     0.5000   -0.0111
##     60        0.8003             nan     0.5000   -0.0069
##     80        0.7686             nan     0.5000    0.0024
##    100        0.7525             nan     0.5000   -0.0127
##    120        0.7284             nan     0.5000   -0.0006
##    140        0.7070             nan     0.5000   -0.0021
##    160        0.6913             nan     0.5000   -0.0035
##    180        0.6782             nan     0.5000   -0.0022
##    200        0.6719             nan     0.5000   -0.0055
##    220        0.6543             nan     0.5000   -0.0059
##    240        0.6397             nan     0.5000   -0.0035
##    260        0.6345             nan     0.5000   -0.0084
##    280        0.6368             nan     0.5000   -0.0107
##    300        0.6195             nan     0.5000   -0.0058
##    320        0.6030             nan     0.5000   -0.0067
##    340        0.5956             nan     0.5000   -0.0043
##    360        0.5882             nan     0.5000   -0.0035
##    380        0.5791             nan     0.5000   -0.0070
##    400        0.5684             nan     0.5000   -0.0074
##    420        0.5556             nan     0.5000   -0.0043
##    440        0.5541             nan     0.5000   -0.0038
##    460        0.5377             nan     0.5000   -0.0037
##    480        0.5357             nan     0.5000   -0.0044
##    500        0.5302             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1426             nan     0.5000    0.0569
##      2        1.0819             nan     0.5000    0.0209
##      3        1.0268             nan     0.5000    0.0269
##      4        0.9975             nan     0.5000    0.0125
##      5        0.9750             nan     0.5000    0.0056
##      6        0.9641             nan     0.5000    0.0022
##      7        0.9451             nan     0.5000    0.0083
##      8        0.9287             nan     0.5000    0.0034
##      9        0.9145             nan     0.5000   -0.0021
##     10        0.9168             nan     0.5000   -0.0084
##     20        0.8671             nan     0.5000   -0.0046
##     40        0.8204             nan     0.5000   -0.0045
##     60        0.7696             nan     0.5000   -0.0057
##     80        0.7491             nan     0.5000   -0.0058
##    100        0.7312             nan     0.5000   -0.0021
##    120        0.7090             nan     0.5000   -0.0021
##    140        0.6839             nan     0.5000   -0.0025
##    160        0.6727             nan     0.5000   -0.0056
##    180        0.6620             nan     0.5000   -0.0050
##    200        0.6521             nan     0.5000   -0.0020
##    220        0.6482             nan     0.5000   -0.0031
##    240        0.6298             nan     0.5000   -0.0059
##    260        0.6168             nan     0.5000   -0.0064
##    280        0.6037             nan     0.5000   -0.0012
##    300        0.5901             nan     0.5000   -0.0101
##    320        0.5817             nan     0.5000   -0.0044
##    340        0.5822             nan     0.5000   -0.0032
##    360        0.5665             nan     0.5000   -0.0033
##    380        0.5556             nan     0.5000   -0.0018
##    400        0.5390             nan     0.5000   -0.0054
##    420        0.5357             nan     0.5000   -0.0039
##    440        0.5279             nan     0.5000   -0.0061
##    460        0.5175             nan     0.5000   -0.0003
##    480        0.5021             nan     0.5000   -0.0001
##    500        0.5022             nan     0.5000   -0.0068
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1704             nan     0.5000    0.0654
##      2        1.1048             nan     0.5000    0.0210
##      3        1.0408             nan     0.5000    0.0258
##      4        1.0031             nan     0.5000    0.0118
##      5        0.9878             nan     0.5000   -0.0012
##      6        0.9567             nan     0.5000    0.0072
##      7        0.9462             nan     0.5000    0.0017
##      8        0.9384             nan     0.5000   -0.0014
##      9        0.9393             nan     0.5000   -0.0073
##     10        0.9282             nan     0.5000    0.0026
##     20        0.8530             nan     0.5000   -0.0008
##     40        0.8222             nan     0.5000   -0.0061
##     60        0.8003             nan     0.5000   -0.0085
##     80        0.7648             nan     0.5000   -0.0083
##    100        0.7449             nan     0.5000   -0.0044
##    120        0.7265             nan     0.5000   -0.0042
##    140        0.7170             nan     0.5000    0.0004
##    160        0.7053             nan     0.5000   -0.0043
##    180        0.6904             nan     0.5000   -0.0075
##    200        0.6670             nan     0.5000    0.0004
##    220        0.6637             nan     0.5000   -0.0082
##    240        0.6415             nan     0.5000   -0.0009
##    260        0.6345             nan     0.5000   -0.0003
##    280        0.6333             nan     0.5000   -0.0062
##    300        0.6154             nan     0.5000   -0.0004
##    320        0.6050             nan     0.5000   -0.0010
##    340        0.6050             nan     0.5000   -0.0057
##    360        0.5881             nan     0.5000   -0.0060
##    380        0.5788             nan     0.5000   -0.0048
##    400        0.5610             nan     0.5000   -0.0024
##    420        0.5564             nan     0.5000   -0.0042
##    440        0.5494             nan     0.5000   -0.0030
##    460        0.5414             nan     0.5000   -0.0039
##    480        0.5344             nan     0.5000   -0.0032
##    500        0.5238             nan     0.5000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1282             nan     0.5000    0.0780
##      2        1.0363             nan     0.5000    0.0438
##      3        0.9991             nan     0.5000    0.0102
##      4        0.9615             nan     0.5000    0.0038
##      5        0.9423             nan     0.5000    0.0039
##      6        0.9441             nan     0.5000   -0.0158
##      7        0.9189             nan     0.5000    0.0094
##      8        0.9161             nan     0.5000   -0.0103
##      9        0.9073             nan     0.5000   -0.0135
##     10        0.9007             nan     0.5000   -0.0039
##     20        0.8064             nan     0.5000    0.0033
##     40        0.7175             nan     0.5000   -0.0144
##     60        0.6766             nan     0.5000   -0.0044
##     80        0.6069             nan     0.5000   -0.0061
##    100        0.5411             nan     0.5000   -0.0046
##    120        2.6905             nan     0.5000    0.0000
##    140        5.6915             nan     0.5000   -0.0110
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1215             nan     0.5000    0.0597
##      2        1.0378             nan     0.5000    0.0363
##      3        0.9914             nan     0.5000    0.0188
##      4        0.9621             nan     0.5000    0.0067
##      5        0.9429             nan     0.5000   -0.0079
##      6        0.9216             nan     0.5000   -0.0011
##      7        0.9035             nan     0.5000    0.0044
##      8        0.8815             nan     0.5000    0.0023
##      9        0.8624             nan     0.5000   -0.0006
##     10        0.8529             nan     0.5000   -0.0081
##     20        0.7825             nan     0.5000   -0.0092
##     40        0.6828             nan     0.5000   -0.0082
##     60        0.6196             nan     0.5000   -0.0122
##     80        0.5522             nan     0.5000   -0.0098
##    100        0.4918             nan     0.5000   -0.0028
##    120        0.4479             nan     0.5000   -0.0060
##    140        0.4254             nan     0.5000   -0.0051
##    160        0.4250             nan     0.5000   -0.0064
##    180        0.6236             nan     0.5000   -0.0290
##    200        0.6236             nan     0.5000   -0.0101
##    220        0.5781             nan     0.5000   -0.0042
##    240           inf             nan     0.5000   -0.0045
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1239             nan     0.5000    0.0821
##      2        1.0588             nan     0.5000    0.0228
##      3        0.9929             nan     0.5000    0.0244
##      4        0.9586             nan     0.5000    0.0102
##      5        0.9352             nan     0.5000    0.0023
##      6        0.9112             nan     0.5000    0.0051
##      7        0.8870             nan     0.5000    0.0021
##      8        0.8829             nan     0.5000   -0.0145
##      9        0.8704             nan     0.5000   -0.0014
##     10        0.8623             nan     0.5000   -0.0023
##     20        0.8000             nan     0.5000    0.0019
##     40        0.7235             nan     0.5000   -0.0009
##     60        0.9606             nan     0.5000   -0.0061
##     80        0.8861             nan     0.5000   -0.0018
##    100        0.8264             nan     0.5000   -0.0025
##    120        0.7717             nan     0.5000   -0.0084
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1095             nan     0.5000    0.0674
##      2        1.0086             nan     0.5000    0.0356
##      3        0.9471             nan     0.5000    0.0221
##      4        0.9246             nan     0.5000   -0.0038
##      5        0.8965             nan     0.5000    0.0002
##      6        0.8754             nan     0.5000   -0.0076
##      7        0.8657             nan     0.5000   -0.0114
##      8        0.8558             nan     0.5000   -0.0111
##      9        0.8324             nan     0.5000    0.0026
##     10        0.8115             nan     0.5000   -0.0005
##     20        0.7164             nan     0.5000   -0.0078
##     40        0.6150             nan     0.5000   -0.0127
##     60        0.5084             nan     0.5000   -0.0048
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1012             nan     0.5000    0.0843
##      2        0.9942             nan     0.5000    0.0290
##      3        0.9442             nan     0.5000    0.0042
##      4        0.9096             nan     0.5000    0.0064
##      5        0.8957             nan     0.5000   -0.0124
##      6        0.8808             nan     0.5000   -0.0089
##      7        0.8671             nan     0.5000   -0.0099
##      8        0.8492             nan     0.5000   -0.0048
##      9        0.8356             nan     0.5000   -0.0043
##     10        0.8315             nan     0.5000   -0.0148
##     20        0.7469             nan     0.5000   -0.0205
##     40        0.6148             nan     0.5000   -0.0142
##     60        0.5149             nan     0.5000   -0.0224
##     80        0.4265             nan     0.5000   -0.0072
##    100        0.3472             nan     0.5000   -0.0149
##    120        0.2781             nan     0.5000   -0.0031
##    140        0.2430             nan     0.5000   -0.0038
##    160        0.1891             nan     0.5000   -0.0002
##    180        0.1609             nan     0.5000   -0.0024
##    200        0.1370             nan     0.5000   -0.0023
##    220        0.1195             nan     0.5000   -0.0015
##    240        0.1057             nan     0.5000   -0.0023
##    260        0.0936             nan     0.5000   -0.0003
##    280        0.0805             nan     0.5000   -0.0010
##    300        0.0706             nan     0.5000   -0.0008
##    320        0.0612             nan     0.5000   -0.0005
##    340        0.0518             nan     0.5000   -0.0003
##    360        0.0446             nan     0.5000   -0.0000
##    380        0.0385             nan     0.5000   -0.0007
##    400        0.0345             nan     0.5000   -0.0004
##    420        0.0300             nan     0.5000   -0.0003
##    440        0.0270             nan     0.5000   -0.0005
##    460        0.0242             nan     0.5000   -0.0004
##    480        0.0215             nan     0.5000   -0.0003
##    500        0.0186             nan     0.5000   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0914             nan     0.5000    0.0888
##      2        0.9998             nan     0.5000    0.0381
##      3        0.9396             nan     0.5000    0.0161
##      4        0.9019             nan     0.5000    0.0049
##      5        0.8716             nan     0.5000   -0.0022
##      6        0.8583             nan     0.5000   -0.0086
##      7        0.8489             nan     0.5000   -0.0110
##      8        0.8334             nan     0.5000   -0.0091
##      9        0.8167             nan     0.5000   -0.0003
##     10        0.8100             nan     0.5000   -0.0104
##     20        0.7249             nan     0.5000    0.0006
##     40        0.5850             nan     0.5000   -0.0010
##     60        0.4820             nan     0.5000   -0.0090
##     80        0.4124             nan     0.5000   -0.0076
##    100        0.3481             nan     0.5000   -0.0047
##    120        0.2969             nan     0.5000   -0.0096
##    140        0.2424             nan     0.5000   -0.0014
##    160        0.2028             nan     0.5000   -0.0011
##    180        0.1733             nan     0.5000   -0.0060
##    200        0.1440             nan     0.5000   -0.0017
##    220        0.1261             nan     0.5000   -0.0023
##    240        0.1124             nan     0.5000   -0.0014
##    260        0.0960             nan     0.5000   -0.0000
##    280        0.0846             nan     0.5000   -0.0012
##    300        0.0748             nan     0.5000   -0.0006
##    320        0.0658             nan     0.5000   -0.0005
##    340        0.0579             nan     0.5000   -0.0013
##    360        0.0502             nan     0.5000   -0.0004
##    380        0.0437             nan     0.5000   -0.0014
##    400        0.0400             nan     0.5000   -0.0012
##    420        0.0351             nan     0.5000   -0.0003
##    440        0.0323             nan     0.5000   -0.0003
##    460        0.0286             nan     0.5000   -0.0006
##    480        0.0247             nan     0.5000   -0.0003
##    500        0.0230             nan     0.5000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1213             nan     1.0000    0.0584
##      2        1.0833             nan     1.0000   -0.0141
##      3        1.0204             nan     1.0000    0.0320
##      4        1.0036             nan     1.0000   -0.0022
##      5        0.9540             nan     1.0000    0.0236
##      6        0.9460             nan     1.0000   -0.0089
##      7        0.9370             nan     1.0000   -0.0067
##      8        0.9284             nan     1.0000   -0.0065
##      9        0.9011             nan     1.0000    0.0042
##     10        0.9157             nan     1.0000   -0.0320
##     20        0.9251             nan     1.0000   -0.0124
##     40       25.7224             nan     1.0000   -0.0263
##     60       25.7587             nan     1.0000    0.0022
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1217             nan     1.0000    0.0876
##      2        1.0682             nan     1.0000    0.0040
##      3        0.9966             nan     1.0000    0.0265
##      4        0.9860             nan     1.0000   -0.0104
##      5        0.9794             nan     1.0000   -0.0100
##      6        0.9649             nan     1.0000   -0.0115
##      7        0.9706             nan     1.0000   -0.0185
##      8        0.9564             nan     1.0000   -0.0120
##      9        0.9400             nan     1.0000    0.0055
##     10        0.9468             nan     1.0000   -0.0207
##     20        0.9346             nan     1.0000    0.0036
##     40        1.1025             nan     1.0000   -0.0221
##     60 28981244.7274             nan     1.0000   -0.0220
##     80 30522697.3681             nan     1.0000   -0.0230
##    100 30522697.2868             nan     1.0000   -0.0057
##    120 30522704.0144             nan     1.0000   -0.0003
##    140 30522703.9701             nan     1.0000   -0.0132
##    160 30522704.0809             nan     1.0000   -0.1274
##    180 30522703.9786             nan     1.0000   -0.0043
##    200 30522703.9418             nan     1.0000   -0.0145
##    220 30522703.9396             nan     1.0000   -0.0017
##    240 30522703.9099             nan     1.0000    0.0042
##    260 30522703.9026             nan     1.0000   -0.0022
##    280 30522706.0835             nan     1.0000   -2.1563
##    300 1324823907878.6494             nan     1.0000   -0.0000
##    320 1324823907878.6235             nan     1.0000   -0.0006
##    340 1324823907878.6399             nan     1.0000   -0.0145
##    360 1324823907878.6636             nan     1.0000   -0.0125
##    380 1324823907878.5723             nan     1.0000    0.0043
##    400 1324823907878.0676             nan     1.0000   -0.0263
##    420 1324823907878.0110             nan     1.0000   -0.0031
##    440 1324823907877.9277             nan     1.0000    0.0024
##    460 1324823907877.8740             nan     1.0000    0.0018
##    480 1324823907877.8247             nan     1.0000   -0.0000
##    500 1324823907877.7754             nan     1.0000    0.0130
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1902             nan     1.0000    0.0254
##      2        1.0542             nan     1.0000    0.0414
##      3        1.0036             nan     1.0000    0.0252
##      4        0.9880             nan     1.0000   -0.0059
##      5        0.9849             nan     1.0000   -0.0126
##      6        0.9816             nan     1.0000   -0.0061
##      7        0.9883             nan     1.0000   -0.0299
##      8        0.9769             nan     1.0000   -0.0083
##      9        0.9670             nan     1.0000   -0.0119
##     10        0.9553             nan     1.0000   -0.0099
##     20        0.9398             nan     1.0000   -0.0471
##     40           inf             nan     1.0000      -inf
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0596             nan     1.0000    0.0986
##      2        0.9825             nan     1.0000    0.0154
##      3        0.9879             nan     1.0000   -0.0380
##      4        0.9559             nan     1.0000   -0.0298
##      5        0.9344             nan     1.0000   -0.0155
##      6        0.9320             nan     1.0000   -0.0238
##      7        0.9408             nan     1.0000   -0.0325
##      8           inf             nan     1.0000      -inf
##      9           inf             nan     1.0000       nan
##     10           inf             nan     1.0000   -0.0731
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       inf
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0640             nan     1.0000    0.1208
##      2        0.9850             nan     1.0000    0.0256
##      3        0.9420             nan     1.0000    0.0076
##      4        0.9227             nan     1.0000   -0.0061
##      5        0.9106             nan     1.0000   -0.0324
##      6        0.9096             nan     1.0000   -0.0205
##      7        0.9110             nan     1.0000   -0.0272
##      8        0.8859             nan     1.0000   -0.0143
##      9        0.9110             nan     1.0000   -0.0505
##     10        0.9267             nan     1.0000   -0.0461
##     20        1.1683             nan     1.0000   -0.0128
##     40        1.1747             nan     1.0000   -0.0142
##     60     4933.7175             nan     1.0000   -0.0011
##     80     4933.3141             nan     1.0000   -0.0270
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0820             nan     1.0000    0.0805
##      2        1.0174             nan     1.0000    0.0026
##      3        0.9759             nan     1.0000    0.0026
##      4        0.9371             nan     1.0000    0.0077
##      5        0.9499             nan     1.0000   -0.0375
##      6        0.9180             nan     1.0000   -0.0153
##      7        0.9140             nan     1.0000   -0.0226
##      8        0.9104             nan     1.0000   -0.0309
##      9        0.8839             nan     1.0000   -0.0012
##     10        0.8656             nan     1.0000   -0.0026
##     20        0.8716             nan     1.0000    0.0186
##     40        0.9061             nan     1.0000   -0.0460
##     60        0.7553             nan     1.0000    0.0035
##     80        0.8702             nan     1.0000   -0.0418
##    100      306.8049             nan     1.0000   -0.1809
##    120      307.4369             nan     1.0000   -0.0821
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0500             nan     1.0000    0.0936
##      2        0.9645             nan     1.0000    0.0311
##      3        0.9178             nan     1.0000    0.0009
##      4        0.8963             nan     1.0000   -0.0081
##      5        0.8771             nan     1.0000   -0.0319
##      6        0.8682             nan     1.0000   -0.0387
##      7        0.8650             nan     1.0000   -0.0537
##      8        0.8355             nan     1.0000   -0.0248
##      9        0.8836             nan     1.0000   -0.0918
##     10        0.9386             nan     1.0000   -0.0668
##     20       82.7065             nan     1.0000   -0.0118
##     40       82.6499             nan     1.0000    0.0051
##     60       82.0008             nan     1.0000   -0.0016
##     80           inf             nan     1.0000   -0.0022
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000   -0.0103
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0606             nan     1.0000    0.0574
##      2        1.0185             nan     1.0000   -0.0077
##      3        1.0273             nan     1.0000   -0.0579
##      4        1.0484             nan     1.0000   -0.0594
##      5        1.0300             nan     1.0000   -0.0557
##      6        1.0018             nan     1.0000   -0.0209
##      7        1.0244             nan     1.0000   -0.0682
##      8        1.0134             nan     1.0000   -0.0303
##      9        0.9943             nan     1.0000   -0.0406
##     10        0.9882             nan     1.0000   -0.0439
##     20        1.4253             nan     1.0000   -0.0561
##     40   138600.0285             nan     1.0000   -0.2103
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0211             nan     1.0000    0.1065
##      2        0.9532             nan     1.0000   -0.0003
##      3        0.9306             nan     1.0000   -0.0287
##      4        0.9066             nan     1.0000   -0.0358
##      5        0.8934             nan     1.0000   -0.0109
##      6        0.8631             nan     1.0000   -0.0083
##      7        0.8690             nan     1.0000   -0.0399
##      8        0.8614             nan     1.0000   -0.0286
##      9        0.8961             nan     1.0000   -0.0669
##     10        0.9001             nan     1.0000   -0.0430
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0001
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0001
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2862             nan     0.0010    0.0002
##     40        1.2793             nan     0.0010    0.0001
##     60        1.2724             nan     0.0010    0.0002
##     80        1.2659             nan     0.0010    0.0002
##    100        1.2598             nan     0.0010    0.0001
##    120        1.2539             nan     0.0010    0.0001
##    140        1.2482             nan     0.0010    0.0001
##    160        1.2425             nan     0.0010    0.0001
##    180        1.2371             nan     0.0010    0.0001
##    200        1.2319             nan     0.0010    0.0001
##    220        1.2266             nan     0.0010    0.0001
##    240        1.2214             nan     0.0010    0.0001
##    260        1.2166             nan     0.0010    0.0001
##    280        1.2118             nan     0.0010    0.0001
##    300        1.2073             nan     0.0010    0.0001
##    320        1.2030             nan     0.0010    0.0001
##    340        1.1986             nan     0.0010    0.0001
##    360        1.1946             nan     0.0010    0.0001
##    380        1.1907             nan     0.0010    0.0001
##    400        1.1866             nan     0.0010    0.0001
##    420        1.1826             nan     0.0010    0.0001
##    440        1.1789             nan     0.0010    0.0001
##    460        1.1752             nan     0.0010    0.0001
##    480        1.1716             nan     0.0010    0.0001
##    500        1.1680             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0001
##      4        1.2920             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2862             nan     0.0010    0.0002
##     40        1.2789             nan     0.0010    0.0001
##     60        1.2724             nan     0.0010    0.0001
##     80        1.2661             nan     0.0010    0.0001
##    100        1.2597             nan     0.0010    0.0001
##    120        1.2537             nan     0.0010    0.0001
##    140        1.2479             nan     0.0010    0.0001
##    160        1.2422             nan     0.0010    0.0001
##    180        1.2367             nan     0.0010    0.0001
##    200        1.2315             nan     0.0010    0.0001
##    220        1.2264             nan     0.0010    0.0001
##    240        1.2214             nan     0.0010    0.0001
##    260        1.2166             nan     0.0010    0.0001
##    280        1.2120             nan     0.0010    0.0001
##    300        1.2073             nan     0.0010    0.0001
##    320        1.2028             nan     0.0010    0.0001
##    340        1.1986             nan     0.0010    0.0001
##    360        1.1944             nan     0.0010    0.0001
##    380        1.1904             nan     0.0010    0.0001
##    400        1.1862             nan     0.0010    0.0001
##    420        1.1824             nan     0.0010    0.0001
##    440        1.1788             nan     0.0010    0.0001
##    460        1.1750             nan     0.0010    0.0001
##    480        1.1715             nan     0.0010    0.0001
##    500        1.1679             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2862             nan     0.0010    0.0002
##     40        1.2795             nan     0.0010    0.0002
##     60        1.2725             nan     0.0010    0.0002
##     80        1.2661             nan     0.0010    0.0002
##    100        1.2598             nan     0.0010    0.0001
##    120        1.2537             nan     0.0010    0.0001
##    140        1.2479             nan     0.0010    0.0001
##    160        1.2423             nan     0.0010    0.0001
##    180        1.2368             nan     0.0010    0.0001
##    200        1.2313             nan     0.0010    0.0001
##    220        1.2262             nan     0.0010    0.0001
##    240        1.2215             nan     0.0010    0.0001
##    260        1.2167             nan     0.0010    0.0001
##    280        1.2123             nan     0.0010    0.0001
##    300        1.2076             nan     0.0010    0.0001
##    320        1.2033             nan     0.0010    0.0001
##    340        1.1992             nan     0.0010    0.0001
##    360        1.1950             nan     0.0010    0.0001
##    380        1.1910             nan     0.0010    0.0001
##    400        1.1870             nan     0.0010    0.0001
##    420        1.1832             nan     0.0010    0.0001
##    440        1.1793             nan     0.0010    0.0001
##    460        1.1755             nan     0.0010    0.0001
##    480        1.1719             nan     0.0010    0.0000
##    500        1.1684             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2840             nan     0.0010    0.0002
##     40        1.2751             nan     0.0010    0.0002
##     60        1.2664             nan     0.0010    0.0002
##     80        1.2578             nan     0.0010    0.0002
##    100        1.2495             nan     0.0010    0.0002
##    120        1.2419             nan     0.0010    0.0002
##    140        1.2342             nan     0.0010    0.0002
##    160        1.2270             nan     0.0010    0.0002
##    180        1.2198             nan     0.0010    0.0002
##    200        1.2127             nan     0.0010    0.0002
##    220        1.2060             nan     0.0010    0.0001
##    240        1.1997             nan     0.0010    0.0001
##    260        1.1931             nan     0.0010    0.0001
##    280        1.1868             nan     0.0010    0.0001
##    300        1.1806             nan     0.0010    0.0001
##    320        1.1747             nan     0.0010    0.0001
##    340        1.1691             nan     0.0010    0.0001
##    360        1.1635             nan     0.0010    0.0001
##    380        1.1582             nan     0.0010    0.0001
##    400        1.1528             nan     0.0010    0.0001
##    420        1.1479             nan     0.0010    0.0001
##    440        1.1427             nan     0.0010    0.0001
##    460        1.1378             nan     0.0010    0.0001
##    480        1.1330             nan     0.0010    0.0001
##    500        1.1284             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2916             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2907             nan     0.0010    0.0002
##      7        1.2902             nan     0.0010    0.0002
##      8        1.2898             nan     0.0010    0.0002
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2889             nan     0.0010    0.0002
##     20        1.2845             nan     0.0010    0.0002
##     40        1.2756             nan     0.0010    0.0002
##     60        1.2669             nan     0.0010    0.0002
##     80        1.2584             nan     0.0010    0.0002
##    100        1.2504             nan     0.0010    0.0002
##    120        1.2426             nan     0.0010    0.0002
##    140        1.2350             nan     0.0010    0.0002
##    160        1.2276             nan     0.0010    0.0001
##    180        1.2203             nan     0.0010    0.0002
##    200        1.2135             nan     0.0010    0.0001
##    220        1.2066             nan     0.0010    0.0001
##    240        1.2000             nan     0.0010    0.0002
##    260        1.1936             nan     0.0010    0.0001
##    280        1.1874             nan     0.0010    0.0001
##    300        1.1814             nan     0.0010    0.0001
##    320        1.1756             nan     0.0010    0.0001
##    340        1.1697             nan     0.0010    0.0001
##    360        1.1642             nan     0.0010    0.0001
##    380        1.1585             nan     0.0010    0.0001
##    400        1.1530             nan     0.0010    0.0001
##    420        1.1479             nan     0.0010    0.0001
##    440        1.1428             nan     0.0010    0.0001
##    460        1.1378             nan     0.0010    0.0001
##    480        1.1330             nan     0.0010    0.0001
##    500        1.1284             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2840             nan     0.0010    0.0002
##     40        1.2751             nan     0.0010    0.0002
##     60        1.2666             nan     0.0010    0.0002
##     80        1.2580             nan     0.0010    0.0002
##    100        1.2497             nan     0.0010    0.0002
##    120        1.2421             nan     0.0010    0.0001
##    140        1.2345             nan     0.0010    0.0001
##    160        1.2268             nan     0.0010    0.0002
##    180        1.2196             nan     0.0010    0.0002
##    200        1.2126             nan     0.0010    0.0001
##    220        1.2057             nan     0.0010    0.0002
##    240        1.1990             nan     0.0010    0.0001
##    260        1.1926             nan     0.0010    0.0001
##    280        1.1864             nan     0.0010    0.0001
##    300        1.1804             nan     0.0010    0.0001
##    320        1.1746             nan     0.0010    0.0001
##    340        1.1687             nan     0.0010    0.0001
##    360        1.1631             nan     0.0010    0.0001
##    380        1.1576             nan     0.0010    0.0001
##    400        1.1521             nan     0.0010    0.0001
##    420        1.1469             nan     0.0010    0.0001
##    440        1.1419             nan     0.0010    0.0001
##    460        1.1370             nan     0.0010    0.0001
##    480        1.1323             nan     0.0010    0.0001
##    500        1.1278             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2897             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2886             nan     0.0010    0.0002
##     10        1.2881             nan     0.0010    0.0002
##     20        1.2831             nan     0.0010    0.0002
##     40        1.2726             nan     0.0010    0.0002
##     60        1.2624             nan     0.0010    0.0002
##     80        1.2525             nan     0.0010    0.0002
##    100        1.2429             nan     0.0010    0.0001
##    120        1.2341             nan     0.0010    0.0002
##    140        1.2255             nan     0.0010    0.0002
##    160        1.2169             nan     0.0010    0.0002
##    180        1.2083             nan     0.0010    0.0002
##    200        1.2002             nan     0.0010    0.0002
##    220        1.1923             nan     0.0010    0.0002
##    240        1.1846             nan     0.0010    0.0002
##    260        1.1772             nan     0.0010    0.0001
##    280        1.1704             nan     0.0010    0.0002
##    300        1.1634             nan     0.0010    0.0001
##    320        1.1567             nan     0.0010    0.0001
##    340        1.1501             nan     0.0010    0.0002
##    360        1.1437             nan     0.0010    0.0001
##    380        1.1375             nan     0.0010    0.0001
##    400        1.1316             nan     0.0010    0.0001
##    420        1.1259             nan     0.0010    0.0001
##    440        1.1201             nan     0.0010    0.0001
##    460        1.1144             nan     0.0010    0.0001
##    480        1.1090             nan     0.0010    0.0001
##    500        1.1036             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2908             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0003
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0002
##     10        1.2880             nan     0.0010    0.0002
##     20        1.2828             nan     0.0010    0.0002
##     40        1.2723             nan     0.0010    0.0002
##     60        1.2622             nan     0.0010    0.0002
##     80        1.2524             nan     0.0010    0.0002
##    100        1.2430             nan     0.0010    0.0002
##    120        1.2339             nan     0.0010    0.0002
##    140        1.2249             nan     0.0010    0.0002
##    160        1.2164             nan     0.0010    0.0002
##    180        1.2081             nan     0.0010    0.0002
##    200        1.2002             nan     0.0010    0.0001
##    220        1.1924             nan     0.0010    0.0001
##    240        1.1848             nan     0.0010    0.0002
##    260        1.1775             nan     0.0010    0.0002
##    280        1.1705             nan     0.0010    0.0001
##    300        1.1637             nan     0.0010    0.0001
##    320        1.1569             nan     0.0010    0.0001
##    340        1.1504             nan     0.0010    0.0002
##    360        1.1441             nan     0.0010    0.0001
##    380        1.1379             nan     0.0010    0.0001
##    400        1.1319             nan     0.0010    0.0001
##    420        1.1261             nan     0.0010    0.0001
##    440        1.1206             nan     0.0010    0.0001
##    460        1.1149             nan     0.0010    0.0001
##    480        1.1093             nan     0.0010    0.0001
##    500        1.1040             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0003
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2887             nan     0.0010    0.0002
##     10        1.2881             nan     0.0010    0.0002
##     20        1.2828             nan     0.0010    0.0002
##     40        1.2723             nan     0.0010    0.0002
##     60        1.2625             nan     0.0010    0.0002
##     80        1.2528             nan     0.0010    0.0002
##    100        1.2435             nan     0.0010    0.0002
##    120        1.2345             nan     0.0010    0.0002
##    140        1.2260             nan     0.0010    0.0002
##    160        1.2174             nan     0.0010    0.0002
##    180        1.2091             nan     0.0010    0.0002
##    200        1.2015             nan     0.0010    0.0001
##    220        1.1937             nan     0.0010    0.0002
##    240        1.1862             nan     0.0010    0.0002
##    260        1.1786             nan     0.0010    0.0002
##    280        1.1713             nan     0.0010    0.0002
##    300        1.1645             nan     0.0010    0.0001
##    320        1.1578             nan     0.0010    0.0001
##    340        1.1513             nan     0.0010    0.0001
##    360        1.1448             nan     0.0010    0.0001
##    380        1.1385             nan     0.0010    0.0001
##    400        1.1324             nan     0.0010    0.0001
##    420        1.1266             nan     0.0010    0.0001
##    440        1.1208             nan     0.0010    0.0001
##    460        1.1153             nan     0.0010    0.0001
##    480        1.1097             nan     0.0010    0.0001
##    500        1.1042             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2575             nan     0.1000    0.0168
##      2        1.2282             nan     0.1000    0.0130
##      3        1.2064             nan     0.1000    0.0088
##      4        1.1824             nan     0.1000    0.0091
##      5        1.1643             nan     0.1000    0.0088
##      6        1.1494             nan     0.1000    0.0065
##      7        1.1332             nan     0.1000    0.0071
##      8        1.1184             nan     0.1000    0.0037
##      9        1.1058             nan     0.1000    0.0059
##     10        1.0907             nan     0.1000    0.0055
##     20        1.0107             nan     0.1000    0.0018
##     40        0.9268             nan     0.1000    0.0002
##     60        0.8827             nan     0.1000   -0.0001
##     80        0.8590             nan     0.1000   -0.0007
##    100        0.8379             nan     0.1000    0.0008
##    120        0.8233             nan     0.1000   -0.0012
##    140        0.8122             nan     0.1000   -0.0019
##    160        0.8025             nan     0.1000   -0.0013
##    180        0.7941             nan     0.1000   -0.0006
##    200        0.7848             nan     0.1000   -0.0015
##    220        0.7787             nan     0.1000   -0.0013
##    240        0.7710             nan     0.1000   -0.0012
##    260        0.7648             nan     0.1000   -0.0015
##    280        0.7585             nan     0.1000   -0.0013
##    300        0.7513             nan     0.1000   -0.0011
##    320        0.7461             nan     0.1000   -0.0009
##    340        0.7408             nan     0.1000   -0.0011
##    360        0.7345             nan     0.1000   -0.0004
##    380        0.7311             nan     0.1000   -0.0001
##    400        0.7254             nan     0.1000   -0.0010
##    420        0.7210             nan     0.1000   -0.0005
##    440        0.7161             nan     0.1000   -0.0011
##    460        0.7132             nan     0.1000   -0.0011
##    480        0.7096             nan     0.1000   -0.0010
##    500        0.7060             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2620             nan     0.1000    0.0159
##      2        1.2349             nan     0.1000    0.0132
##      3        1.2101             nan     0.1000    0.0128
##      4        1.1853             nan     0.1000    0.0083
##      5        1.1670             nan     0.1000    0.0079
##      6        1.1509             nan     0.1000    0.0074
##      7        1.1361             nan     0.1000    0.0065
##      8        1.1243             nan     0.1000    0.0054
##      9        1.1120             nan     0.1000    0.0048
##     10        1.0993             nan     0.1000    0.0048
##     20        1.0139             nan     0.1000    0.0026
##     40        0.9324             nan     0.1000    0.0001
##     60        0.8926             nan     0.1000   -0.0003
##     80        0.8618             nan     0.1000   -0.0007
##    100        0.8411             nan     0.1000   -0.0004
##    120        0.8266             nan     0.1000   -0.0004
##    140        0.8166             nan     0.1000   -0.0004
##    160        0.8050             nan     0.1000   -0.0003
##    180        0.7971             nan     0.1000   -0.0014
##    200        0.7902             nan     0.1000   -0.0010
##    220        0.7836             nan     0.1000   -0.0013
##    240        0.7765             nan     0.1000   -0.0006
##    260        0.7726             nan     0.1000   -0.0005
##    280        0.7657             nan     0.1000   -0.0016
##    300        0.7622             nan     0.1000   -0.0012
##    320        0.7566             nan     0.1000   -0.0004
##    340        0.7524             nan     0.1000   -0.0007
##    360        0.7482             nan     0.1000   -0.0011
##    380        0.7453             nan     0.1000   -0.0005
##    400        0.7409             nan     0.1000   -0.0012
##    420        0.7361             nan     0.1000   -0.0010
##    440        0.7300             nan     0.1000   -0.0009
##    460        0.7261             nan     0.1000   -0.0011
##    480        0.7222             nan     0.1000   -0.0007
##    500        0.7182             nan     0.1000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2616             nan     0.1000    0.0134
##      2        1.2343             nan     0.1000    0.0133
##      3        1.2051             nan     0.1000    0.0121
##      4        1.1827             nan     0.1000    0.0099
##      5        1.1616             nan     0.1000    0.0066
##      6        1.1463             nan     0.1000    0.0061
##      7        1.1339             nan     0.1000    0.0051
##      8        1.1192             nan     0.1000    0.0044
##      9        1.1062             nan     0.1000    0.0056
##     10        1.1006             nan     0.1000   -0.0002
##     20        1.0109             nan     0.1000    0.0017
##     40        0.9280             nan     0.1000   -0.0005
##     60        0.8887             nan     0.1000   -0.0001
##     80        0.8624             nan     0.1000   -0.0010
##    100        0.8439             nan     0.1000   -0.0003
##    120        0.8279             nan     0.1000   -0.0008
##    140        0.8155             nan     0.1000   -0.0008
##    160        0.8066             nan     0.1000   -0.0013
##    180        0.7995             nan     0.1000   -0.0002
##    200        0.7911             nan     0.1000   -0.0014
##    220        0.7861             nan     0.1000   -0.0002
##    240        0.7782             nan     0.1000   -0.0004
##    260        0.7719             nan     0.1000   -0.0009
##    280        0.7666             nan     0.1000   -0.0010
##    300        0.7625             nan     0.1000   -0.0006
##    320        0.7551             nan     0.1000   -0.0006
##    340        0.7511             nan     0.1000   -0.0014
##    360        0.7480             nan     0.1000   -0.0011
##    380        0.7423             nan     0.1000   -0.0011
##    400        0.7368             nan     0.1000   -0.0006
##    420        0.7319             nan     0.1000   -0.0009
##    440        0.7276             nan     0.1000   -0.0004
##    460        0.7207             nan     0.1000   -0.0012
##    480        0.7166             nan     0.1000   -0.0006
##    500        0.7119             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2516             nan     0.1000    0.0162
##      2        1.2091             nan     0.1000    0.0192
##      3        1.1775             nan     0.1000    0.0148
##      4        1.1494             nan     0.1000    0.0104
##      5        1.1275             nan     0.1000    0.0073
##      6        1.1053             nan     0.1000    0.0092
##      7        1.0878             nan     0.1000    0.0073
##      8        1.0703             nan     0.1000    0.0067
##      9        1.0485             nan     0.1000    0.0073
##     10        1.0349             nan     0.1000    0.0043
##     20        0.9424             nan     0.1000    0.0012
##     40        0.8539             nan     0.1000   -0.0003
##     60        0.8113             nan     0.1000   -0.0022
##     80        0.7762             nan     0.1000   -0.0010
##    100        0.7507             nan     0.1000   -0.0009
##    120        0.7211             nan     0.1000   -0.0014
##    140        0.6996             nan     0.1000   -0.0011
##    160        0.6809             nan     0.1000   -0.0007
##    180        0.6604             nan     0.1000   -0.0006
##    200        0.6377             nan     0.1000   -0.0009
##    220        0.6227             nan     0.1000   -0.0007
##    240        0.6060             nan     0.1000   -0.0018
##    260        0.5911             nan     0.1000   -0.0005
##    280        0.5751             nan     0.1000   -0.0009
##    300        0.5610             nan     0.1000   -0.0016
##    320        0.5489             nan     0.1000   -0.0009
##    340        0.5340             nan     0.1000   -0.0017
##    360        0.5210             nan     0.1000   -0.0011
##    380        0.5056             nan     0.1000   -0.0008
##    400        0.4969             nan     0.1000   -0.0013
##    420        0.4851             nan     0.1000   -0.0010
##    440        0.4752             nan     0.1000   -0.0001
##    460        0.4658             nan     0.1000   -0.0003
##    480        0.4573             nan     0.1000   -0.0009
##    500        0.4462             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2463             nan     0.1000    0.0207
##      2        1.2096             nan     0.1000    0.0140
##      3        1.1819             nan     0.1000    0.0118
##      4        1.1523             nan     0.1000    0.0095
##      5        1.1307             nan     0.1000    0.0092
##      6        1.1102             nan     0.1000    0.0093
##      7        1.0865             nan     0.1000    0.0081
##      8        1.0681             nan     0.1000    0.0074
##      9        1.0523             nan     0.1000    0.0051
##     10        1.0373             nan     0.1000    0.0048
##     20        0.9373             nan     0.1000    0.0009
##     40        0.8585             nan     0.1000   -0.0006
##     60        0.8085             nan     0.1000   -0.0003
##     80        0.7679             nan     0.1000   -0.0008
##    100        0.7438             nan     0.1000   -0.0009
##    120        0.7199             nan     0.1000   -0.0010
##    140        0.6988             nan     0.1000   -0.0008
##    160        0.6759             nan     0.1000   -0.0014
##    180        0.6606             nan     0.1000   -0.0017
##    200        0.6424             nan     0.1000   -0.0011
##    220        0.6284             nan     0.1000   -0.0005
##    240        0.6125             nan     0.1000   -0.0009
##    260        0.5904             nan     0.1000   -0.0004
##    280        0.5788             nan     0.1000   -0.0012
##    300        0.5642             nan     0.1000   -0.0012
##    320        0.5474             nan     0.1000   -0.0011
##    340        0.5347             nan     0.1000   -0.0012
##    360        0.5251             nan     0.1000   -0.0014
##    380        0.5125             nan     0.1000   -0.0006
##    400        0.5006             nan     0.1000   -0.0009
##    420        0.4908             nan     0.1000   -0.0008
##    440        0.4798             nan     0.1000   -0.0017
##    460        0.4673             nan     0.1000   -0.0006
##    480        0.4566             nan     0.1000   -0.0004
##    500        0.4436             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2491             nan     0.1000    0.0217
##      2        1.2137             nan     0.1000    0.0157
##      3        1.1796             nan     0.1000    0.0141
##      4        1.1542             nan     0.1000    0.0085
##      5        1.1277             nan     0.1000    0.0111
##      6        1.1047             nan     0.1000    0.0106
##      7        1.0870             nan     0.1000    0.0082
##      8        1.0682             nan     0.1000    0.0069
##      9        1.0548             nan     0.1000    0.0039
##     10        1.0371             nan     0.1000    0.0076
##     20        0.9445             nan     0.1000    0.0019
##     40        0.8650             nan     0.1000   -0.0009
##     60        0.8156             nan     0.1000    0.0004
##     80        0.7813             nan     0.1000   -0.0014
##    100        0.7495             nan     0.1000   -0.0009
##    120        0.7249             nan     0.1000   -0.0016
##    140        0.7037             nan     0.1000   -0.0011
##    160        0.6799             nan     0.1000   -0.0012
##    180        0.6632             nan     0.1000   -0.0012
##    200        0.6461             nan     0.1000   -0.0015
##    220        0.6309             nan     0.1000   -0.0012
##    240        0.6151             nan     0.1000   -0.0013
##    260        0.6002             nan     0.1000   -0.0025
##    280        0.5867             nan     0.1000   -0.0008
##    300        0.5735             nan     0.1000   -0.0004
##    320        0.5595             nan     0.1000   -0.0004
##    340        0.5475             nan     0.1000   -0.0015
##    360        0.5339             nan     0.1000   -0.0001
##    380        0.5224             nan     0.1000   -0.0016
##    400        0.5096             nan     0.1000   -0.0005
##    420        0.4977             nan     0.1000   -0.0013
##    440        0.4866             nan     0.1000   -0.0010
##    460        0.4771             nan     0.1000   -0.0009
##    480        0.4681             nan     0.1000   -0.0006
##    500        0.4558             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2405             nan     0.1000    0.0240
##      2        1.1942             nan     0.1000    0.0181
##      3        1.1563             nan     0.1000    0.0178
##      4        1.1260             nan     0.1000    0.0132
##      5        1.0987             nan     0.1000    0.0084
##      6        1.0744             nan     0.1000    0.0089
##      7        1.0498             nan     0.1000    0.0110
##      8        1.0269             nan     0.1000    0.0084
##      9        1.0121             nan     0.1000    0.0036
##     10        0.9997             nan     0.1000    0.0029
##     20        0.8941             nan     0.1000    0.0015
##     40        0.7982             nan     0.1000   -0.0023
##     60        0.7369             nan     0.1000   -0.0008
##     80        0.6900             nan     0.1000   -0.0003
##    100        0.6515             nan     0.1000   -0.0015
##    120        0.6140             nan     0.1000   -0.0009
##    140        0.5859             nan     0.1000   -0.0008
##    160        0.5553             nan     0.1000   -0.0003
##    180        0.5287             nan     0.1000   -0.0016
##    200        0.5081             nan     0.1000   -0.0008
##    220        0.4874             nan     0.1000   -0.0007
##    240        0.4660             nan     0.1000   -0.0012
##    260        0.4478             nan     0.1000   -0.0013
##    280        0.4279             nan     0.1000   -0.0004
##    300        0.4115             nan     0.1000   -0.0010
##    320        0.3954             nan     0.1000   -0.0025
##    340        0.3820             nan     0.1000   -0.0010
##    360        0.3705             nan     0.1000   -0.0013
##    380        0.3575             nan     0.1000   -0.0008
##    400        0.3443             nan     0.1000   -0.0009
##    420        0.3317             nan     0.1000   -0.0003
##    440        0.3206             nan     0.1000   -0.0004
##    460        0.3087             nan     0.1000   -0.0006
##    480        0.2994             nan     0.1000   -0.0007
##    500        0.2899             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2451             nan     0.1000    0.0194
##      2        1.2034             nan     0.1000    0.0177
##      3        1.1687             nan     0.1000    0.0163
##      4        1.1322             nan     0.1000    0.0157
##      5        1.1025             nan     0.1000    0.0125
##      6        1.0715             nan     0.1000    0.0115
##      7        1.0488             nan     0.1000    0.0091
##      8        1.0271             nan     0.1000    0.0083
##      9        1.0123             nan     0.1000    0.0046
##     10        0.9939             nan     0.1000    0.0062
##     20        0.8947             nan     0.1000   -0.0005
##     40        0.7967             nan     0.1000   -0.0001
##     60        0.7489             nan     0.1000   -0.0036
##     80        0.7018             nan     0.1000   -0.0005
##    100        0.6652             nan     0.1000   -0.0023
##    120        0.6296             nan     0.1000   -0.0010
##    140        0.5993             nan     0.1000   -0.0031
##    160        0.5690             nan     0.1000   -0.0007
##    180        0.5380             nan     0.1000   -0.0013
##    200        0.5105             nan     0.1000    0.0002
##    220        0.4847             nan     0.1000   -0.0011
##    240        0.4622             nan     0.1000   -0.0011
##    260        0.4427             nan     0.1000   -0.0014
##    280        0.4226             nan     0.1000   -0.0007
##    300        0.4045             nan     0.1000   -0.0012
##    320        0.3872             nan     0.1000   -0.0009
##    340        0.3720             nan     0.1000   -0.0005
##    360        0.3603             nan     0.1000   -0.0006
##    380        0.3474             nan     0.1000   -0.0006
##    400        0.3335             nan     0.1000   -0.0014
##    420        0.3195             nan     0.1000   -0.0004
##    440        0.3075             nan     0.1000   -0.0011
##    460        0.2964             nan     0.1000   -0.0004
##    480        0.2848             nan     0.1000   -0.0006
##    500        0.2747             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2424             nan     0.1000    0.0252
##      2        1.1986             nan     0.1000    0.0170
##      3        1.1628             nan     0.1000    0.0162
##      4        1.1331             nan     0.1000    0.0118
##      5        1.1065             nan     0.1000    0.0075
##      6        1.0824             nan     0.1000    0.0100
##      7        1.0584             nan     0.1000    0.0099
##      8        1.0399             nan     0.1000    0.0067
##      9        1.0214             nan     0.1000    0.0066
##     10        1.0098             nan     0.1000    0.0021
##     20        0.9037             nan     0.1000    0.0006
##     40        0.7989             nan     0.1000   -0.0019
##     60        0.7410             nan     0.1000   -0.0015
##     80        0.6970             nan     0.1000   -0.0012
##    100        0.6640             nan     0.1000   -0.0023
##    120        0.6323             nan     0.1000   -0.0032
##    140        0.5977             nan     0.1000   -0.0028
##    160        0.5709             nan     0.1000   -0.0014
##    180        0.5432             nan     0.1000   -0.0015
##    200        0.5158             nan     0.1000   -0.0004
##    220        0.4916             nan     0.1000   -0.0019
##    240        0.4708             nan     0.1000   -0.0006
##    260        0.4542             nan     0.1000   -0.0006
##    280        0.4387             nan     0.1000   -0.0002
##    300        0.4221             nan     0.1000   -0.0006
##    320        0.4043             nan     0.1000   -0.0007
##    340        0.3875             nan     0.1000   -0.0011
##    360        0.3744             nan     0.1000   -0.0015
##    380        0.3631             nan     0.1000   -0.0007
##    400        0.3501             nan     0.1000   -0.0007
##    420        0.3377             nan     0.1000   -0.0008
##    440        0.3252             nan     0.1000   -0.0008
##    460        0.3133             nan     0.1000   -0.0007
##    480        0.3031             nan     0.1000   -0.0012
##    500        0.2933             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2240             nan     0.2000    0.0286
##      2        1.1837             nan     0.2000    0.0183
##      3        1.1567             nan     0.2000    0.0125
##      4        1.1277             nan     0.2000    0.0111
##      5        1.0994             nan     0.2000    0.0119
##      6        1.0703             nan     0.2000    0.0076
##      7        1.0520             nan     0.2000    0.0063
##      8        1.0358             nan     0.2000    0.0059
##      9        1.0259             nan     0.2000    0.0004
##     10        1.0160             nan     0.2000    0.0035
##     20        0.9325             nan     0.2000    0.0011
##     40        0.8758             nan     0.2000   -0.0004
##     60        0.8326             nan     0.2000   -0.0012
##     80        0.8142             nan     0.2000   -0.0037
##    100        0.7961             nan     0.2000   -0.0005
##    120        0.7840             nan     0.2000   -0.0012
##    140        0.7717             nan     0.2000   -0.0026
##    160        0.7637             nan     0.2000   -0.0036
##    180        0.7495             nan     0.2000   -0.0022
##    200        0.7393             nan     0.2000   -0.0007
##    220        0.7312             nan     0.2000   -0.0028
##    240        0.7208             nan     0.2000   -0.0005
##    260        0.7128             nan     0.2000   -0.0022
##    280        0.7018             nan     0.2000   -0.0022
##    300        0.6955             nan     0.2000   -0.0020
##    320        0.6886             nan     0.2000   -0.0014
##    340        0.6832             nan     0.2000   -0.0021
##    360        0.6775             nan     0.2000   -0.0026
##    380        0.6673             nan     0.2000   -0.0025
##    400        0.6591             nan     0.2000   -0.0012
##    420        0.6514             nan     0.2000   -0.0031
##    440        0.6490             nan     0.2000   -0.0040
##    460        0.6443             nan     0.2000   -0.0019
##    480        0.6402             nan     0.2000   -0.0023
##    500        0.6346             nan     0.2000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2293             nan     0.2000    0.0300
##      2        1.1859             nan     0.2000    0.0213
##      3        1.1538             nan     0.2000    0.0157
##      4        1.1254             nan     0.2000    0.0118
##      5        1.1011             nan     0.2000    0.0103
##      6        1.0770             nan     0.2000    0.0117
##      7        1.0583             nan     0.2000    0.0059
##      8        1.0396             nan     0.2000    0.0038
##      9        1.0183             nan     0.2000    0.0045
##     10        1.0049             nan     0.2000    0.0027
##     20        0.9322             nan     0.2000    0.0028
##     40        0.8549             nan     0.2000   -0.0025
##     60        0.8256             nan     0.2000   -0.0030
##     80        0.8093             nan     0.2000   -0.0013
##    100        0.7929             nan     0.2000   -0.0017
##    120        0.7846             nan     0.2000   -0.0010
##    140        0.7678             nan     0.2000   -0.0023
##    160        0.7563             nan     0.2000   -0.0015
##    180        0.7437             nan     0.2000   -0.0006
##    200        0.7347             nan     0.2000   -0.0013
##    220        0.7291             nan     0.2000   -0.0023
##    240        0.7186             nan     0.2000   -0.0030
##    260        0.7069             nan     0.2000   -0.0017
##    280        0.7033             nan     0.2000   -0.0026
##    300        0.6973             nan     0.2000   -0.0018
##    320        0.6874             nan     0.2000   -0.0016
##    340        0.6803             nan     0.2000   -0.0013
##    360        0.6731             nan     0.2000   -0.0004
##    380        0.6641             nan     0.2000   -0.0014
##    400        0.6636             nan     0.2000   -0.0016
##    420        0.6533             nan     0.2000   -0.0011
##    440        0.6463             nan     0.2000   -0.0011
##    460        0.6378             nan     0.2000   -0.0009
##    480        0.6348             nan     0.2000   -0.0019
##    500        0.6285             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2264             nan     0.2000    0.0290
##      2        1.1815             nan     0.2000    0.0209
##      3        1.1523             nan     0.2000    0.0125
##      4        1.1236             nan     0.2000    0.0114
##      5        1.1000             nan     0.2000    0.0087
##      6        1.0757             nan     0.2000    0.0093
##      7        1.0551             nan     0.2000    0.0084
##      8        1.0436             nan     0.2000    0.0023
##      9        1.0306             nan     0.2000    0.0037
##     10        1.0141             nan     0.2000    0.0054
##     20        0.9295             nan     0.2000    0.0005
##     40        0.8633             nan     0.2000   -0.0043
##     60        0.8218             nan     0.2000   -0.0019
##     80        0.8003             nan     0.2000   -0.0011
##    100        0.7854             nan     0.2000   -0.0023
##    120        0.7741             nan     0.2000   -0.0025
##    140        0.7628             nan     0.2000   -0.0009
##    160        0.7515             nan     0.2000   -0.0010
##    180        0.7389             nan     0.2000   -0.0014
##    200        0.7312             nan     0.2000   -0.0033
##    220        0.7173             nan     0.2000   -0.0033
##    240        0.7156             nan     0.2000   -0.0036
##    260        0.7050             nan     0.2000   -0.0005
##    280        0.6993             nan     0.2000   -0.0026
##    300        0.6890             nan     0.2000   -0.0004
##    320        0.6825             nan     0.2000   -0.0011
##    340        0.6770             nan     0.2000   -0.0031
##    360        0.6730             nan     0.2000   -0.0028
##    380        0.6668             nan     0.2000   -0.0021
##    400        0.6587             nan     0.2000   -0.0029
##    420        0.6491             nan     0.2000   -0.0018
##    440        0.6440             nan     0.2000   -0.0014
##    460        0.6376             nan     0.2000   -0.0015
##    480        0.6337             nan     0.2000   -0.0029
##    500        0.6288             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2122             nan     0.2000    0.0424
##      2        1.1424             nan     0.2000    0.0292
##      3        1.0987             nan     0.2000    0.0162
##      4        1.0609             nan     0.2000    0.0155
##      5        1.0359             nan     0.2000    0.0069
##      6        1.0127             nan     0.2000    0.0071
##      7        0.9872             nan     0.2000    0.0056
##      8        0.9730             nan     0.2000    0.0040
##      9        0.9577             nan     0.2000    0.0045
##     10        0.9386             nan     0.2000    0.0057
##     20        0.8572             nan     0.2000   -0.0015
##     40        0.7791             nan     0.2000   -0.0012
##     60        0.7256             nan     0.2000    0.0003
##     80        0.6898             nan     0.2000   -0.0049
##    100        0.6600             nan     0.2000   -0.0017
##    120        0.6301             nan     0.2000   -0.0015
##    140        0.6030             nan     0.2000   -0.0017
##    160        0.5940             nan     0.2000   -0.0039
##    180        0.5493             nan     0.2000   -0.0037
##    200        0.5467             nan     0.2000   -0.0027
##    220        0.5161             nan     0.2000   -0.0025
##    240        0.4927             nan     0.2000   -0.0040
##    260        0.4694             nan     0.2000   -0.0016
##    280        0.4438             nan     0.2000   -0.0017
##    300        0.4202             nan     0.2000   -0.0016
##    320        0.4046             nan     0.2000   -0.0008
##    340        0.3880             nan     0.2000   -0.0022
##    360        0.3756             nan     0.2000   -0.0010
##    380      249.6284             nan     0.2000   -0.0011
##    400      249.6181             nan     0.2000    0.0000
##    420      249.2166             nan     0.2000   -0.0018
##    440      248.9391             nan     0.2000   -0.0000
##    460      248.9310             nan     0.2000   -0.0033
##    480      248.9249             nan     0.2000   -0.0012
##    500      248.9146             nan     0.2000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2122             nan     0.2000    0.0388
##      2        1.1539             nan     0.2000    0.0278
##      3        1.1043             nan     0.2000    0.0201
##      4        1.0682             nan     0.2000    0.0107
##      5        1.0392             nan     0.2000    0.0089
##      6        1.0156             nan     0.2000    0.0082
##      7        0.9910             nan     0.2000    0.0125
##      8        0.9732             nan     0.2000    0.0048
##      9        0.9489             nan     0.2000    0.0092
##     10        0.9385             nan     0.2000    0.0030
##     20        0.8622             nan     0.2000   -0.0058
##     40        0.7754             nan     0.2000   -0.0017
##     60        0.7273             nan     0.2000   -0.0023
##     80        0.6819             nan     0.2000   -0.0010
##    100        0.6457             nan     0.2000   -0.0014
##    120        0.6136             nan     0.2000   -0.0003
##    140        0.5804             nan     0.2000   -0.0023
##    160        0.5508             nan     0.2000   -0.0031
##    180        0.5279             nan     0.2000   -0.0015
##    200        0.5017             nan     0.2000   -0.0033
##    220        0.4767             nan     0.2000   -0.0024
##    240        0.4619             nan     0.2000   -0.0016
##    260        0.4393             nan     0.2000   -0.0001
##    280        0.4229             nan     0.2000   -0.0020
##    300        0.4081             nan     0.2000   -0.0042
##    320        0.3865             nan     0.2000   -0.0015
##    340        0.3731             nan     0.2000   -0.0017
##    360        0.3591             nan     0.2000   -0.0022
##    380        0.3445             nan     0.2000   -0.0024
##    400        0.3317             nan     0.2000   -0.0006
##    420        0.3172             nan     0.2000   -0.0010
##    440        0.3041             nan     0.2000   -0.0013
##    460        0.2950             nan     0.2000   -0.0023
##    480        0.2829             nan     0.2000   -0.0014
##    500        0.2721             nan     0.2000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2064             nan     0.2000    0.0347
##      2        1.1466             nan     0.2000    0.0218
##      3        1.1007             nan     0.2000    0.0189
##      4        1.0653             nan     0.2000    0.0165
##      5        1.0349             nan     0.2000    0.0107
##      6        1.0135             nan     0.2000    0.0083
##      7        0.9953             nan     0.2000    0.0024
##      8        0.9766             nan     0.2000    0.0036
##      9        0.9654             nan     0.2000   -0.0020
##     10        0.9499             nan     0.2000    0.0051
##     20        0.8601             nan     0.2000   -0.0021
##     40        0.7897             nan     0.2000   -0.0046
##     60        0.7483             nan     0.2000   -0.0032
##     80        0.7040             nan     0.2000   -0.0028
##    100        0.6621             nan     0.2000   -0.0009
##    120        0.6352             nan     0.2000   -0.0042
##    140        0.6105             nan     0.2000   -0.0023
##    160        0.5836             nan     0.2000   -0.0037
##    180        0.5598             nan     0.2000   -0.0012
##    200        0.5337             nan     0.2000   -0.0010
##    220        0.5070             nan     0.2000   -0.0023
##    240        0.4828             nan     0.2000   -0.0013
##    260        0.4587             nan     0.2000   -0.0007
##    280        0.4406             nan     0.2000   -0.0021
##    300        0.4178             nan     0.2000   -0.0009
##    320        0.4041             nan     0.2000   -0.0021
##    340        0.3906             nan     0.2000   -0.0011
##    360        0.3759             nan     0.2000   -0.0007
##    380        0.3647             nan     0.2000   -0.0018
##    400        0.3542             nan     0.2000   -0.0012
##    420        0.3348             nan     0.2000   -0.0004
##    440        0.3219             nan     0.2000   -0.0010
##    460        0.3100             nan     0.2000   -0.0019
##    480        0.3006             nan     0.2000   -0.0012
##    500        0.2883             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1896             nan     0.2000    0.0468
##      2        1.1206             nan     0.2000    0.0295
##      3        1.0628             nan     0.2000    0.0218
##      4        1.0220             nan     0.2000    0.0096
##      5        0.9901             nan     0.2000    0.0075
##      6        0.9613             nan     0.2000    0.0109
##      7        0.9413             nan     0.2000    0.0062
##      8        0.9255             nan     0.2000    0.0008
##      9        0.9091             nan     0.2000    0.0026
##     10        0.8931             nan     0.2000    0.0035
##     20        0.8059             nan     0.2000   -0.0037
##     40        0.7206             nan     0.2000   -0.0027
##     60        0.6331             nan     0.2000   -0.0031
##     80        0.5665             nan     0.2000   -0.0055
##    100        0.5185             nan     0.2000   -0.0059
##    120        0.4743             nan     0.2000   -0.0031
##    140        0.4321             nan     0.2000   -0.0026
##    160        0.4001             nan     0.2000   -0.0041
##    180        0.3710             nan     0.2000   -0.0021
##    200        0.3404             nan     0.2000   -0.0004
##    220        0.3158             nan     0.2000   -0.0009
##    240        0.2929             nan     0.2000   -0.0019
##    260        0.2709             nan     0.2000   -0.0005
##    280        0.2529             nan     0.2000   -0.0016
##    300        0.2387             nan     0.2000   -0.0006
##    320        0.2231             nan     0.2000   -0.0025
##    340        0.2123             nan     0.2000   -0.0005
##    360        0.1985             nan     0.2000   -0.0013
##    380        0.1866             nan     0.2000   -0.0004
##    400        0.1766             nan     0.2000   -0.0005
##    420        0.1672             nan     0.2000   -0.0009
##    440        0.1561             nan     0.2000   -0.0006
##    460        0.1478             nan     0.2000   -0.0011
##    480        0.1385             nan     0.2000   -0.0001
##    500        0.1291             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2074             nan     0.2000    0.0410
##      2        1.1258             nan     0.2000    0.0305
##      3        1.0630             nan     0.2000    0.0247
##      4        1.0216             nan     0.2000    0.0155
##      5        0.9924             nan     0.2000    0.0045
##      6        0.9618             nan     0.2000    0.0102
##      7        0.9390             nan     0.2000    0.0048
##      8        0.9222             nan     0.2000    0.0059
##      9        0.9065             nan     0.2000    0.0009
##     10        0.8958             nan     0.2000   -0.0020
##     20        0.7979             nan     0.2000   -0.0027
##     40        0.7134             nan     0.2000   -0.0048
##     60        0.6486             nan     0.2000   -0.0029
##     80        0.5991             nan     0.2000   -0.0021
##    100        0.5428             nan     0.2000   -0.0014
##    120        0.4898             nan     0.2000   -0.0030
##    140        0.4467             nan     0.2000   -0.0019
##    160        0.4094             nan     0.2000   -0.0025
##    180        0.3758             nan     0.2000   -0.0015
##    200        0.3448             nan     0.2000   -0.0008
##    220        0.3163             nan     0.2000   -0.0005
##    240        0.2922             nan     0.2000   -0.0013
##    260        0.2762             nan     0.2000   -0.0012
##    280        0.2598             nan     0.2000   -0.0011
##    300        0.2454             nan     0.2000   -0.0008
##    320        0.2290             nan     0.2000   -0.0011
##    340        0.2140             nan     0.2000   -0.0017
##    360        0.2007             nan     0.2000   -0.0009
##    380        0.1869             nan     0.2000   -0.0010
##    400        0.1745             nan     0.2000   -0.0014
##    420        0.1648             nan     0.2000   -0.0015
##    440        0.1555             nan     0.2000   -0.0008
##    460        0.1460             nan     0.2000   -0.0009
##    480        0.1340             nan     0.2000   -0.0012
##    500        0.1264             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2050             nan     0.2000    0.0351
##      2        1.1348             nan     0.2000    0.0286
##      3        1.0737             nan     0.2000    0.0254
##      4        1.0302             nan     0.2000    0.0179
##      5        0.9946             nan     0.2000    0.0155
##      6        0.9601             nan     0.2000    0.0111
##      7        0.9404             nan     0.2000    0.0033
##      8        0.9209             nan     0.2000    0.0044
##      9        0.9026             nan     0.2000    0.0020
##     10        0.8894             nan     0.2000    0.0025
##     20        0.8010             nan     0.2000   -0.0033
##     40        0.7091             nan     0.2000   -0.0046
##     60        0.6381             nan     0.2000   -0.0030
##     80        0.5778             nan     0.2000   -0.0021
##    100        0.5258             nan     0.2000   -0.0028
##    120        0.4807             nan     0.2000   -0.0025
##    140        0.4432             nan     0.2000   -0.0025
##    160        0.4164             nan     0.2000   -0.0031
##    180        0.3869             nan     0.2000   -0.0014
##    200        0.3525             nan     0.2000   -0.0017
##    220        0.3305             nan     0.2000   -0.0029
##    240        0.3058             nan     0.2000   -0.0013
##    260        0.2840             nan     0.2000   -0.0015
##    280        0.2634             nan     0.2000   -0.0023
##    300        0.2458             nan     0.2000   -0.0010
##    320        0.2262             nan     0.2000   -0.0004
##    340        0.2104             nan     0.2000   -0.0006
##    360        0.1995             nan     0.2000   -0.0015
##    380        0.1874             nan     0.2000   -0.0017
##    400        0.1736             nan     0.2000   -0.0008
##    420        0.1643             nan     0.2000   -0.0005
##    440        0.1556             nan     0.2000   -0.0005
##    460        0.1455             nan     0.2000   -0.0002
##    480        0.1370             nan     0.2000   -0.0008
##    500        0.1288             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2061             nan     0.3000    0.0438
##      2        1.1591             nan     0.3000    0.0175
##      3        1.1256             nan     0.3000    0.0140
##      4        1.0875             nan     0.3000    0.0163
##      5        1.0634             nan     0.3000    0.0098
##      6        1.0385             nan     0.3000    0.0065
##      7        1.0169             nan     0.3000    0.0075
##      8        0.9937             nan     0.3000    0.0076
##      9        0.9790             nan     0.3000    0.0049
##     10        0.9659             nan     0.3000    0.0059
##     20        0.9056             nan     0.3000   -0.0027
##     40        0.8374             nan     0.3000   -0.0020
##     60        0.8160             nan     0.3000   -0.0013
##     80        0.7894             nan     0.3000   -0.0043
##    100        0.7773             nan     0.3000   -0.0032
##    120        0.7553             nan     0.3000   -0.0017
##    140        0.7379             nan     0.3000   -0.0023
##    160        0.7251             nan     0.3000   -0.0031
##    180        0.7034             nan     0.3000   -0.0018
##    200        0.6993             nan     0.3000   -0.0035
##    220        0.6860             nan     0.3000   -0.0041
##    240        0.6725             nan     0.3000   -0.0000
##    260        0.6614             nan     0.3000   -0.0016
##    280        0.6537             nan     0.3000   -0.0009
##    300        0.6473             nan     0.3000   -0.0020
##    320        0.6351             nan     0.3000   -0.0015
##    340        0.6229             nan     0.3000   -0.0029
##    360        0.6148             nan     0.3000   -0.0029
##    380        0.6046             nan     0.3000    0.0003
##    400        0.5969             nan     0.3000   -0.0034
##    420        0.5896             nan     0.3000   -0.0017
##    440        0.5841             nan     0.3000   -0.0020
##    460        0.5813             nan     0.3000   -0.0040
##    480        0.5722             nan     0.3000   -0.0011
##    500        0.5715             nan     0.3000   -0.0033
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2186             nan     0.3000    0.0378
##      2        1.1578             nan     0.3000    0.0279
##      3        1.1168             nan     0.3000    0.0130
##      4        1.0864             nan     0.3000    0.0116
##      5        1.0528             nan     0.3000    0.0066
##      6        1.0204             nan     0.3000    0.0118
##      7        1.0074             nan     0.3000    0.0036
##      8        0.9918             nan     0.3000    0.0026
##      9        0.9764             nan     0.3000   -0.0015
##     10        0.9552             nan     0.3000    0.0058
##     20        0.8746             nan     0.3000    0.0003
##     40        0.8253             nan     0.3000   -0.0025
##     60        0.8020             nan     0.3000   -0.0034
##     80        0.7862             nan     0.3000   -0.0011
##    100        0.7686             nan     0.3000   -0.0014
##    120        0.7564             nan     0.3000   -0.0020
##    140        0.7381             nan     0.3000   -0.0042
##    160        0.7215             nan     0.3000   -0.0023
##    180        0.7130             nan     0.3000   -0.0021
##    200        0.7028             nan     0.3000   -0.0039
##    220        0.6886             nan     0.3000   -0.0020
##    240        0.6866             nan     0.3000   -0.0017
##    260        0.6668             nan     0.3000   -0.0011
##    280        0.6575             nan     0.3000   -0.0027
##    300        0.6460             nan     0.3000   -0.0000
##    320        0.6400             nan     0.3000   -0.0035
##    340        0.6291             nan     0.3000   -0.0023
##    360        0.6225             nan     0.3000   -0.0040
##    380        0.6142             nan     0.3000   -0.0024
##    400        0.6094             nan     0.3000   -0.0024
##    420        0.6034             nan     0.3000   -0.0053
##    440        0.5950             nan     0.3000   -0.0034
##    460        0.5885             nan     0.3000   -0.0036
##    480        0.5744             nan     0.3000   -0.0024
##    500        0.5695             nan     0.3000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2152             nan     0.3000    0.0407
##      2        1.1526             nan     0.3000    0.0260
##      3        1.1120             nan     0.3000    0.0180
##      4        1.0753             nan     0.3000    0.0166
##      5        1.0501             nan     0.3000    0.0076
##      6        1.0250             nan     0.3000    0.0080
##      7        1.0180             nan     0.3000   -0.0018
##      8        0.9974             nan     0.3000    0.0068
##      9        0.9753             nan     0.3000    0.0060
##     10        0.9618             nan     0.3000    0.0021
##     20        0.9014             nan     0.3000   -0.0014
##     40        0.8438             nan     0.3000   -0.0055
##     60        0.8152             nan     0.3000   -0.0031
##     80        0.7957             nan     0.3000   -0.0053
##    100        0.7772             nan     0.3000   -0.0048
##    120        0.7610             nan     0.3000   -0.0072
##    140        0.7465             nan     0.3000   -0.0029
##    160        0.7279             nan     0.3000    0.0001
##    180        0.7108             nan     0.3000   -0.0003
##    200        0.7059             nan     0.3000   -0.0030
##    220        0.6938             nan     0.3000   -0.0028
##    240        0.6813             nan     0.3000   -0.0015
##    260        0.6732             nan     0.3000   -0.0026
##    280        0.6641             nan     0.3000    0.0005
##    300        0.6540             nan     0.3000   -0.0013
##    320        0.6499             nan     0.3000   -0.0041
##    340        0.6440             nan     0.3000   -0.0020
##    360        0.6355             nan     0.3000   -0.0012
##    380        0.6274             nan     0.3000   -0.0023
##    400        0.6202             nan     0.3000   -0.0010
##    420        0.6164             nan     0.3000   -0.0009
##    440        0.6109             nan     0.3000   -0.0024
##    460        0.6023             nan     0.3000   -0.0016
##    480        0.5929             nan     0.3000   -0.0005
##    500        0.5835             nan     0.3000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1772             nan     0.3000    0.0386
##      2        1.0950             nan     0.3000    0.0331
##      3        1.0496             nan     0.3000    0.0107
##      4        1.0008             nan     0.3000    0.0125
##      5        0.9718             nan     0.3000    0.0035
##      6        0.9408             nan     0.3000    0.0040
##      7        0.9197             nan     0.3000    0.0079
##      8        0.9059             nan     0.3000    0.0007
##      9        0.8919             nan     0.3000    0.0027
##     10        0.8804             nan     0.3000    0.0030
##     20        0.8143             nan     0.3000   -0.0002
##     40        0.7330             nan     0.3000   -0.0012
##     60        0.6718             nan     0.3000   -0.0057
##     80        0.6233             nan     0.3000   -0.0040
##    100        0.5866             nan     0.3000   -0.0101
##    120        0.5493             nan     0.3000   -0.0031
##    140        0.5161             nan     0.3000   -0.0027
##    160        0.4836             nan     0.3000   -0.0003
##    180        0.4578             nan     0.3000   -0.0074
##    200        0.4283             nan     0.3000   -0.0045
##    220        0.4023             nan     0.3000   -0.0018
##    240        0.3838             nan     0.3000   -0.0032
##    260           inf             nan     0.3000   -0.3685
##    280           inf             nan     0.3000   -0.0021
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000   -0.0046
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1681             nan     0.3000    0.0547
##      2        1.1068             nan     0.3000    0.0205
##      3        1.0536             nan     0.3000    0.0246
##      4        1.0062             nan     0.3000    0.0186
##      5        0.9738             nan     0.3000    0.0123
##      6        0.9520             nan     0.3000    0.0064
##      7        0.9351             nan     0.3000   -0.0001
##      8        0.9097             nan     0.3000    0.0062
##      9        0.8901             nan     0.3000    0.0051
##     10        0.8784             nan     0.3000    0.0014
##     20        0.8116             nan     0.3000   -0.0047
##     40        0.7338             nan     0.3000   -0.0034
##     60        0.6709             nan     0.3000   -0.0024
##     80        0.6116             nan     0.3000   -0.0018
##    100        0.5634             nan     0.3000   -0.0043
##    120        0.5224             nan     0.3000   -0.0032
##    140        0.5004             nan     0.3000    0.0020
##    160        0.4602             nan     0.3000   -0.0055
##    180        0.4311             nan     0.3000   -0.0025
##    200        0.4063             nan     0.3000   -0.0014
##    220        0.3743             nan     0.3000   -0.0026
##    240        0.3458             nan     0.3000   -0.0031
##    260        0.3189             nan     0.3000   -0.0009
##    280        0.3022             nan     0.3000   -0.0036
##    300        0.2869             nan     0.3000   -0.0010
##    320        0.2646             nan     0.3000   -0.0013
##    340        0.2477             nan     0.3000   -0.0006
##    360        0.2400             nan     0.3000   -0.0015
##    380        0.2251             nan     0.3000   -0.0021
##    400        0.2147             nan     0.3000   -0.0023
##    420        0.2039             nan     0.3000   -0.0030
##    440        0.1907             nan     0.3000   -0.0026
##    460        0.1811             nan     0.3000   -0.0007
##    480        0.1727             nan     0.3000   -0.0017
##    500        0.1642             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1675             nan     0.3000    0.0512
##      2        1.0930             nan     0.3000    0.0382
##      3        1.0470             nan     0.3000    0.0161
##      4        1.0107             nan     0.3000    0.0128
##      5        0.9646             nan     0.3000    0.0065
##      6        0.9445             nan     0.3000    0.0053
##      7        0.9284             nan     0.3000    0.0030
##      8        0.9119             nan     0.3000    0.0009
##      9        0.9008             nan     0.3000   -0.0030
##     10        0.8907             nan     0.3000    0.0004
##     20        0.8361             nan     0.3000   -0.0038
##     40        0.7397             nan     0.3000   -0.0033
##     60        0.6804             nan     0.3000   -0.0035
##     80        0.6342             nan     0.3000   -0.0028
##    100        0.5910             nan     0.3000   -0.0018
##    120        0.5503             nan     0.3000   -0.0014
##    140        0.5113             nan     0.3000   -0.0038
##    160        0.4766             nan     0.3000   -0.0045
##    180        0.4404             nan     0.3000   -0.0022
##    200        0.4064             nan     0.3000   -0.0012
##    220        0.3825             nan     0.3000   -0.0029
##    240        0.3674             nan     0.3000   -0.0027
##    260        0.3459             nan     0.3000   -0.0014
##    280        0.3237             nan     0.3000   -0.0033
##    300        0.3037             nan     0.3000   -0.0017
##    320        0.2877             nan     0.3000   -0.0038
##    340        0.2736             nan     0.3000   -0.0016
##    360        0.2602             nan     0.3000   -0.0012
##    380        0.2438             nan     0.3000   -0.0019
##    400        0.2342             nan     0.3000   -0.0024
##    420        0.2202             nan     0.3000   -0.0011
##    440        0.2092             nan     0.3000   -0.0019
##    460        0.1935             nan     0.3000   -0.0005
##    480        0.1864             nan     0.3000   -0.0007
##    500        0.1806             nan     0.3000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1546             nan     0.3000    0.0595
##      2        1.0731             nan     0.3000    0.0339
##      3        1.0198             nan     0.3000    0.0125
##      4        0.9777             nan     0.3000    0.0126
##      5        0.9426             nan     0.3000    0.0059
##      6        0.9283             nan     0.3000   -0.0057
##      7        0.9086             nan     0.3000    0.0027
##      8        0.8855             nan     0.3000    0.0034
##      9        0.8739             nan     0.3000   -0.0034
##     10        0.8641             nan     0.3000   -0.0014
##     20        0.8008             nan     0.3000   -0.0082
##     40        0.7021             nan     0.3000   -0.0103
##     60        0.6036             nan     0.3000   -0.0028
##     80        0.5234             nan     0.3000   -0.0025
##    100        0.4544             nan     0.3000    0.0000
##    120        0.4040             nan     0.3000   -0.0084
##    140        0.3472             nan     0.3000   -0.0015
##    160        0.3172             nan     0.3000   -0.0026
##    180        0.2789             nan     0.3000   -0.0035
##    200        0.2511             nan     0.3000   -0.0010
##    220        0.2285             nan     0.3000   -0.0052
##    240        0.2085             nan     0.3000   -0.0007
##    260        0.1906             nan     0.3000   -0.0004
##    280        0.1773             nan     0.3000   -0.0023
##    300        0.1587             nan     0.3000   -0.0002
##    320        0.1494             nan     0.3000   -0.0021
##    340        0.1362             nan     0.3000   -0.0009
##    360        0.1227             nan     0.3000   -0.0003
##    380        0.1141             nan     0.3000   -0.0011
##    400        0.1031             nan     0.3000   -0.0009
##    420        0.0931             nan     0.3000   -0.0007
##    440        0.0864             nan     0.3000   -0.0014
##    460        0.0795             nan     0.3000   -0.0006
##    480        0.0733             nan     0.3000   -0.0004
##    500        0.0679             nan     0.3000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1628             nan     0.3000    0.0601
##      2        1.0931             nan     0.3000    0.0229
##      3        1.0317             nan     0.3000    0.0201
##      4        0.9826             nan     0.3000    0.0099
##      5        0.9522             nan     0.3000    0.0041
##      6        0.9303             nan     0.3000    0.0011
##      7        0.9114             nan     0.3000    0.0020
##      8        0.8977             nan     0.3000    0.0011
##      9        0.8883             nan     0.3000   -0.0059
##     10        0.8691             nan     0.3000    0.0039
##     20        0.7863             nan     0.3000   -0.0030
##     40        0.6657             nan     0.3000   -0.0031
##     60        0.5902             nan     0.3000   -0.0066
##     80        0.5167             nan     0.3000   -0.0027
##    100        0.4681             nan     0.3000   -0.0052
##    120        0.4139             nan     0.3000   -0.0019
##    140        0.3687             nan     0.3000   -0.0077
##    160        0.3289             nan     0.3000   -0.0035
##    180        0.2938             nan     0.3000   -0.0013
##    200        0.2678             nan     0.3000   -0.0025
##    220        0.2352             nan     0.3000   -0.0015
##    240        0.2125             nan     0.3000   -0.0020
##    260        0.1871             nan     0.3000   -0.0016
##    280        0.1738             nan     0.3000   -0.0001
##    300        0.1536             nan     0.3000    0.0000
##    320        0.1417             nan     0.3000   -0.0001
##    340        0.1300             nan     0.3000   -0.0005
##    360        0.1165             nan     0.3000   -0.0014
##    380        0.1071             nan     0.3000   -0.0007
##    400        0.0966             nan     0.3000   -0.0006
##    420        0.0896             nan     0.3000   -0.0002
##    440        0.0836             nan     0.3000   -0.0009
##    460        0.0761             nan     0.3000   -0.0007
##    480        0.0706             nan     0.3000   -0.0005
##    500        0.0649             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1702             nan     0.3000    0.0536
##      2        1.0699             nan     0.3000    0.0396
##      3        1.0049             nan     0.3000    0.0248
##      4        0.9619             nan     0.3000    0.0111
##      5        0.9291             nan     0.3000    0.0066
##      6        0.9037             nan     0.3000    0.0039
##      7        0.8884             nan     0.3000   -0.0011
##      8        0.8734             nan     0.3000   -0.0009
##      9        0.8587             nan     0.3000   -0.0039
##     10        0.8415             nan     0.3000    0.0027
##     20        0.7607             nan     0.3000   -0.0035
##     40        0.6471             nan     0.3000   -0.0024
##     60        0.5628             nan     0.3000   -0.0033
##     80        0.4951             nan     0.3000   -0.0021
##    100        0.4378             nan     0.3000   -0.0026
##    120        0.3950             nan     0.3000   -0.0049
##    140        0.3598             nan     0.3000   -0.0031
##    160        0.3166             nan     0.3000   -0.0018
##    180        0.2904             nan     0.3000   -0.0016
##    200        0.2616             nan     0.3000   -0.0007
##    220        0.2366             nan     0.3000   -0.0016
##    240        0.2099             nan     0.3000   -0.0006
##    260        0.1891             nan     0.3000   -0.0000
##    280        0.1695             nan     0.3000   -0.0011
##    300        0.1534             nan     0.3000   -0.0010
##    320        0.1408             nan     0.3000   -0.0013
##    340        0.1292             nan     0.3000   -0.0010
##    360        0.1204             nan     0.3000   -0.0005
##    380        0.1112             nan     0.3000   -0.0012
##    400        0.1028             nan     0.3000   -0.0008
##    420        0.0950             nan     0.3000   -0.0012
##    440        0.0849             nan     0.3000   -0.0004
##    460        0.0791             nan     0.3000   -0.0002
##    480        0.0720             nan     0.3000   -0.0004
##    500        0.0677             nan     0.3000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1553             nan     0.5000    0.0517
##      2        1.0976             nan     0.5000    0.0218
##      3        1.0609             nan     0.5000    0.0195
##      4        1.0156             nan     0.5000    0.0178
##      5        0.9946             nan     0.5000    0.0055
##      6        0.9898             nan     0.5000   -0.0028
##      7        0.9774             nan     0.5000    0.0021
##      8        0.9583             nan     0.5000    0.0067
##      9        0.9421             nan     0.5000    0.0002
##     10        0.9314             nan     0.5000    0.0007
##     20        0.8788             nan     0.5000   -0.0039
##     40        2.1554             nan     0.5000   -0.0020
##     60           inf             nan     0.5000      -inf
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000   -0.0006
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1663             nan     0.5000    0.0646
##      2        1.1049             nan     0.5000    0.0210
##      3        1.0535             nan     0.5000    0.0169
##      4        1.0079             nan     0.5000    0.0174
##      5        0.9926             nan     0.5000    0.0019
##      6        0.9736             nan     0.5000    0.0011
##      7        0.9400             nan     0.5000    0.0114
##      8        0.9282             nan     0.5000   -0.0012
##      9        0.9097             nan     0.5000    0.0033
##     10        0.8999             nan     0.5000   -0.0028
##     20        0.8501             nan     0.5000    0.0006
##     40        0.7852             nan     0.5000    0.0013
##     60        0.7548             nan     0.5000   -0.0053
##     80        0.7466             nan     0.5000   -0.0095
##    100        0.7250             nan     0.5000    0.0016
##    120        0.7074             nan     0.5000   -0.0027
##    140        0.6896             nan     0.5000   -0.0058
##    160        0.6665             nan     0.5000   -0.0029
##    180        0.6515             nan     0.5000   -0.0013
##    200        0.6377             nan     0.5000   -0.0028
##    220        0.6381             nan     0.5000   -0.0041
##    240        0.6224             nan     0.5000   -0.0036
##    260        0.6115             nan     0.5000   -0.0050
##    280        0.5918             nan     0.5000   -0.0033
##    300        0.5779             nan     0.5000   -0.0089
##    320        0.5673             nan     0.5000   -0.0047
##    340        0.5632             nan     0.5000   -0.0059
##    360        0.5565             nan     0.5000   -0.0071
##    380        0.5500             nan     0.5000   -0.0051
##    400        0.5332             nan     0.5000   -0.0056
##    420        0.5228             nan     0.5000   -0.0059
##    440        0.5165             nan     0.5000   -0.0073
##    460        0.5170             nan     0.5000   -0.0067
##    480        0.5102             nan     0.5000   -0.0049
##    500        0.4998             nan     0.5000   -0.0063
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1561             nan     0.5000    0.0624
##      2        1.0953             nan     0.5000    0.0199
##      3        1.0491             nan     0.5000    0.0191
##      4        1.0040             nan     0.5000    0.0201
##      5        0.9874             nan     0.5000    0.0035
##      6        0.9630             nan     0.5000    0.0080
##      7        0.9496             nan     0.5000    0.0056
##      8        0.9345             nan     0.5000    0.0044
##      9        0.9375             nan     0.5000   -0.0130
##     10        0.9317             nan     0.5000   -0.0073
##     20        0.8803             nan     0.5000   -0.0123
##     40        0.8241             nan     0.5000   -0.0094
##     60        0.7944             nan     0.5000   -0.0043
##     80        0.7590             nan     0.5000   -0.0058
##    100        0.7434             nan     0.5000   -0.0059
##    120        0.7153             nan     0.5000   -0.0010
##    140        0.7004             nan     0.5000   -0.0092
##    160        0.6776             nan     0.5000   -0.0054
##    180        0.6735             nan     0.5000   -0.0038
##    200        0.6517             nan     0.5000    0.0031
##    220        0.6449             nan     0.5000   -0.0108
##    240        0.6263             nan     0.5000   -0.0002
##    260        0.6199             nan     0.5000   -0.0032
##    280        0.6122             nan     0.5000   -0.0050
##    300        0.5929             nan     0.5000   -0.0060
##    320        0.5806             nan     0.5000   -0.0054
##    340        0.5688             nan     0.5000   -0.0017
##    360        0.5711             nan     0.5000   -0.0132
##    380        0.5681             nan     0.5000   -0.0039
##    400        0.5475             nan     0.5000   -0.0026
##    420        0.5375             nan     0.5000   -0.0040
##    440        0.5256             nan     0.5000    0.0012
##    460        0.5156             nan     0.5000    0.0008
##    480        0.5089             nan     0.5000   -0.0045
##    500        0.4951             nan     0.5000   -0.0050
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1081             nan     0.5000    0.0839
##      2        1.0281             nan     0.5000    0.0311
##      3        0.9734             nan     0.5000    0.0148
##      4        0.9368             nan     0.5000    0.0116
##      5        0.9024             nan     0.5000    0.0066
##      6        0.8926             nan     0.5000   -0.0072
##      7        0.8841             nan     0.5000   -0.0084
##      8        0.8656             nan     0.5000    0.0042
##      9        0.8567             nan     0.5000   -0.0038
##     10        0.8452             nan     0.5000   -0.0046
##     20        0.7997             nan     0.5000   -0.0122
##     40        0.6660             nan     0.5000   -0.0064
##     60        0.6027             nan     0.5000   -0.0124
##     80        0.5380             nan     0.5000   -0.0114
##    100        0.4833             nan     0.5000   -0.0130
##    120        0.4329             nan     0.5000   -0.0071
##    140        0.3935             nan     0.5000   -0.0046
##    160        0.3385             nan     0.5000   -0.0026
##    180        0.3083             nan     0.5000   -0.0027
##    200        0.2904             nan     0.5000   -0.0012
##    220        0.2553             nan     0.5000   -0.0025
##    240        0.2314             nan     0.5000   -0.0055
##    260        0.2069             nan     0.5000   -0.0061
##    280        0.1898             nan     0.5000   -0.0031
##    300        0.1700             nan     0.5000   -0.0011
##    320        0.1576             nan     0.5000   -0.0037
##    340        0.1460             nan     0.5000   -0.0020
##    360        0.1321             nan     0.5000   -0.0014
##    380        0.1190             nan     0.5000   -0.0008
##    400        0.1088             nan     0.5000   -0.0019
##    420        0.0998             nan     0.5000   -0.0005
##    440        0.0961             nan     0.5000   -0.0027
##    460        0.0905             nan     0.5000   -0.0010
##    480        0.0830             nan     0.5000   -0.0012
##    500        0.0769             nan     0.5000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1270             nan     0.5000    0.0807
##      2        1.0477             nan     0.5000    0.0335
##      3        1.0000             nan     0.5000    0.0102
##      4        0.9660             nan     0.5000    0.0053
##      5        0.9460             nan     0.5000    0.0051
##      6        0.9347             nan     0.5000   -0.0152
##      7        0.9055             nan     0.5000    0.0113
##      8        0.8915             nan     0.5000   -0.0024
##      9        0.8845             nan     0.5000   -0.0118
##     10        0.8727             nan     0.5000   -0.0021
##     20        0.7930             nan     0.5000   -0.0110
##     40        0.7013             nan     0.5000   -0.0098
##     60        0.6180             nan     0.5000   -0.0174
##     80        0.5542             nan     0.5000   -0.0059
##    100        0.4770             nan     0.5000   -0.0035
##    120        0.4439             nan     0.5000   -0.0080
##    140        0.4224             nan     0.5000   -0.0229
##    160        0.3806             nan     0.5000   -0.0001
##    180        0.3417             nan     0.5000   -0.0058
##    200        0.3101             nan     0.5000   -0.0065
##    220        0.3233             nan     0.5000   -0.0077
##    240        0.2907             nan     0.5000   -0.0067
##    260        0.2737             nan     0.5000   -0.0019
##    280 3258755157680.8887             nan     0.5000   -0.0015
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1371             nan     0.5000    0.0820
##      2        1.0387             nan     0.5000    0.0277
##      3        0.9721             nan     0.5000    0.0307
##      4        0.9380             nan     0.5000    0.0164
##      5        0.9067             nan     0.5000    0.0085
##      6        0.8876             nan     0.5000   -0.0029
##      7        0.8830             nan     0.5000   -0.0092
##      8        0.8643             nan     0.5000    0.0002
##      9        0.8553             nan     0.5000   -0.0065
##     10        0.8390             nan     0.5000    0.0017
##     20        0.7774             nan     0.5000   -0.0132
##     40        0.6937             nan     0.5000   -0.0035
##     60        0.6191             nan     0.5000   -0.0091
##     80        0.5568             nan     0.5000   -0.0071
##    100        0.4943             nan     0.5000   -0.0022
##    120        0.4453             nan     0.5000   -0.0053
##    140        0.4132             nan     0.5000   -0.0077
##    160        0.3716             nan     0.5000   -0.0027
##    180        0.3498             nan     0.5000   -0.0085
##    200        0.3171             nan     0.5000   -0.0028
##    220        0.2758             nan     0.5000   -0.0029
##    240        0.2487             nan     0.5000   -0.0021
##    260        0.2234             nan     0.5000   -0.0023
##    280        0.2115             nan     0.5000   -0.0010
##    300        0.1966             nan     0.5000   -0.0024
##    320        0.1833             nan     0.5000   -0.0009
##    340        0.1685             nan     0.5000   -0.0024
##    360        0.1519             nan     0.5000   -0.0015
##    380        0.1399             nan     0.5000   -0.0031
##    400        0.1283             nan     0.5000   -0.0016
##    420        0.1168             nan     0.5000   -0.0009
##    440        0.1079             nan     0.5000   -0.0017
##    460        0.1015             nan     0.5000   -0.0005
##    480        0.0961             nan     0.5000   -0.0034
##    500        0.0880             nan     0.5000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1041             nan     0.5000    0.0907
##      2        0.9974             nan     0.5000    0.0398
##      3        0.9441             nan     0.5000    0.0182
##      4        0.9097             nan     0.5000    0.0018
##      5        0.8843             nan     0.5000   -0.0084
##      6        0.8621             nan     0.5000    0.0024
##      7        0.8419             nan     0.5000   -0.0073
##      8        0.8287             nan     0.5000   -0.0047
##      9        0.8195             nan     0.5000   -0.0098
##     10        0.8071             nan     0.5000   -0.0053
##     20        0.8358             nan     0.5000   -0.0149
##     40        0.6282             nan     0.5000   -0.0030
##     60        1.4746             nan     0.5000   -0.0152
##     80     1315.3979             nan     0.5000   -0.0076
##    100     1315.3406             nan     0.5000   -0.0025
##    120     1315.3188             nan     0.5000   -0.0005
##    140     1315.2851             nan     0.5000   -0.0039
##    160     1315.5094             nan     0.5000   -0.5024
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0938             nan     0.5000    0.0845
##      2        1.0205             nan     0.5000    0.0250
##      3        0.9485             nan     0.5000    0.0110
##      4        0.8998             nan     0.5000    0.0061
##      5        0.8766             nan     0.5000   -0.0193
##      6        0.8580             nan     0.5000   -0.0040
##      7        0.8486             nan     0.5000   -0.0161
##      8        0.8281             nan     0.5000   -0.0051
##      9        0.8101             nan     0.5000   -0.0031
##     10        0.7926             nan     0.5000   -0.0010
##     20        0.7179             nan     0.5000   -0.0143
##     40        0.5771             nan     0.5000   -0.0130
##     60        0.4621             nan     0.5000   -0.0108
##     80        0.3687             nan     0.5000   -0.0107
##    100        0.3008             nan     0.5000   -0.0005
##    120        0.2532             nan     0.5000   -0.0022
##    140        0.2180             nan     0.5000   -0.0008
##    160        0.1828             nan     0.5000   -0.0046
##    180        0.1570             nan     0.5000   -0.0032
##    200        0.1361             nan     0.5000   -0.0023
##    220        0.1218             nan     0.5000   -0.0013
##    240        0.1068             nan     0.5000   -0.0001
##    260        0.0930             nan     0.5000   -0.0014
##    280        0.0804             nan     0.5000   -0.0013
##    300        0.0710             nan     0.5000   -0.0013
##    320        0.0604             nan     0.5000    0.0001
##    340        0.0552             nan     0.5000   -0.0010
##    360        0.0482             nan     0.5000   -0.0003
##    380        0.0431             nan     0.5000   -0.0004
##    400        0.0397             nan     0.5000   -0.0003
##    420        0.0350             nan     0.5000   -0.0003
##    440        0.0311             nan     0.5000   -0.0003
##    460        0.0281             nan     0.5000   -0.0003
##    480        0.0252             nan     0.5000   -0.0003
##    500        0.0224             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1022             nan     0.5000    0.0865
##      2        1.0093             nan     0.5000    0.0320
##      3        0.9600             nan     0.5000    0.0102
##      4        0.9195             nan     0.5000    0.0057
##      5        0.9019             nan     0.5000   -0.0028
##      6        0.8776             nan     0.5000    0.0021
##      7        0.8580             nan     0.5000   -0.0093
##      8        0.8354             nan     0.5000   -0.0072
##      9        0.8188             nan     0.5000   -0.0032
##     10        0.8118             nan     0.5000   -0.0137
##     20        0.7289             nan     0.5000   -0.0161
##     40        0.5875             nan     0.5000   -0.0074
##     60        0.4665             nan     0.5000   -0.0049
##     80        0.4080             nan     0.5000   -0.0085
##    100        0.3236             nan     0.5000   -0.0041
##    120        0.2645             nan     0.5000   -0.0035
##    140        0.2247             nan     0.5000   -0.0037
##    160        0.1966             nan     0.5000   -0.0032
##    180        0.1641             nan     0.5000   -0.0008
##    200        0.1396             nan     0.5000   -0.0029
##    220        0.1187             nan     0.5000   -0.0019
##    240        0.1028             nan     0.5000   -0.0015
##    260        0.0892             nan     0.5000   -0.0026
##    280        0.0783             nan     0.5000   -0.0009
##    300        0.0699             nan     0.5000   -0.0007
##    320        0.0624             nan     0.5000   -0.0012
##    340        0.0536             nan     0.5000   -0.0003
##    360        0.0457             nan     0.5000   -0.0009
##    380        0.0399             nan     0.5000   -0.0004
##    400        0.0355             nan     0.5000   -0.0007
##    420        0.0308             nan     0.5000   -0.0002
##    440        0.0268             nan     0.5000    0.0001
##    460        0.0228             nan     0.5000   -0.0002
##    480        0.0208             nan     0.5000   -0.0003
##    500        0.0187             nan     0.5000   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1146             nan     1.0000    0.0998
##      2        1.0866             nan     1.0000   -0.0146
##      3        1.0295             nan     1.0000    0.0205
##      4        1.0139             nan     1.0000   -0.0121
##      5        0.9821             nan     1.0000    0.0018
##      6        1.0043             nan     1.0000   -0.0409
##      7        0.9919             nan     1.0000   -0.0109
##      8        0.9861             nan     1.0000   -0.0061
##      9        0.9571             nan     1.0000    0.0071
##     10        0.9558             nan     1.0000   -0.0121
##     20        1.0333             nan     1.0000   -0.0116
##     40    17211.4037             nan     1.0000   -0.0194
##     60    17212.7577             nan     1.0000   -0.0127
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1392             nan     1.0000    0.0653
##      2        1.0733             nan     1.0000    0.0118
##      3        1.0090             nan     1.0000    0.0270
##      4        0.9657             nan     1.0000    0.0104
##      5        0.9582             nan     1.0000   -0.0141
##      6        0.9747             nan     1.0000   -0.0335
##      7        0.9607             nan     1.0000    0.0018
##      8        0.9690             nan     1.0000   -0.0256
##      9        0.9496             nan     1.0000    0.0016
##     10        0.9546             nan     1.0000   -0.0196
##     20        0.8940             nan     1.0000   -0.0022
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000   -0.0152
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1319             nan     1.0000    0.0746
##      2        1.0697             nan     1.0000    0.0175
##      3        1.0182             nan     1.0000    0.0145
##      4        0.9773             nan     1.0000    0.0209
##      5        0.9596             nan     1.0000    0.0009
##      6        0.9484             nan     1.0000   -0.0001
##      7        0.9378             nan     1.0000   -0.0037
##      8        0.9244             nan     1.0000    0.0009
##      9        0.9023             nan     1.0000    0.0049
##     10        0.9011             nan     1.0000   -0.0101
##     20        0.8309             nan     1.0000   -0.0045
##     40        0.7952             nan     1.0000   -0.0255
##     60        0.7967             nan     1.0000   -0.0127
##     80        0.7663             nan     1.0000   -0.0176
##    100        0.7019             nan     1.0000   -0.0032
##    120        0.7089             nan     1.0000   -0.0283
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0824             nan     1.0000    0.0672
##      2        1.0257             nan     1.0000   -0.0208
##      3        0.9913             nan     1.0000    0.0055
##      4        0.9736             nan     1.0000   -0.0185
##      5        0.9636             nan     1.0000   -0.0259
##      6        0.9436             nan     1.0000   -0.0096
##      7        0.9226             nan     1.0000   -0.0205
##      8        0.9169             nan     1.0000   -0.0288
##      9        1.0423             nan     1.0000   -0.1557
##     10        0.9702             nan     1.0000   -0.0275
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000   -0.0013
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1107             nan     1.0000    0.0454
##      2        1.0154             nan     1.0000    0.0138
##      3        0.9838             nan     1.0000   -0.0141
##      4        0.9523             nan     1.0000   -0.0114
##      5        0.9294             nan     1.0000   -0.0042
##      6        0.9594             nan     1.0000   -0.0539
##      7        0.9059             nan     1.0000    0.0005
##      8        0.8834             nan     1.0000   -0.0117
##      9        0.8775             nan     1.0000   -0.0243
##     10        0.8510             nan     1.0000   -0.0016
##     20        0.8692             nan     1.0000   -0.0306
##     40           inf             nan     1.0000      -inf
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0598             nan     1.0000    0.1025
##      2        1.0134             nan     1.0000   -0.0082
##      3        0.9889             nan     1.0000   -0.0049
##      4        0.9654             nan     1.0000   -0.0049
##      5        0.9547             nan     1.0000   -0.0271
##      6        0.9297             nan     1.0000   -0.0150
##      7        0.9208             nan     1.0000   -0.0111
##      8        0.9059             nan     1.0000   -0.0115
##      9        0.8868             nan     1.0000   -0.0112
##     10        0.8825             nan     1.0000   -0.0230
##     20        0.8607             nan     1.0000   -0.0259
##     40        2.9888             nan     1.0000   -0.0350
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0490             nan     1.0000    0.0720
##      2        0.9504             nan     1.0000    0.0314
##      3        0.9201             nan     1.0000   -0.0097
##      4        0.9283             nan     1.0000   -0.0702
##      5        1.1002             nan     1.0000   -0.2281
##      6        1.0620             nan     1.0000   -0.0145
##      7        1.0242             nan     1.0000   -0.0116
##      8        1.0150             nan     1.0000   -0.0324
##      9        1.0017             nan     1.0000   -0.0326
##     10        1.0066             nan     1.0000   -0.0269
##     20        1.2278             nan     1.0000   -0.0360
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0376             nan     1.0000    0.0806
##      2        1.0113             nan     1.0000   -0.0427
##      3        0.9792             nan     1.0000   -0.0347
##      4        0.9827             nan     1.0000   -0.0459
##      5        0.9668             nan     1.0000   -0.0381
##      6        0.9147             nan     1.0000    0.0046
##      7        0.9202             nan     1.0000   -0.0539
##      8        0.9254             nan     1.0000   -0.0654
##      9        0.9485             nan     1.0000   -0.0672
##     10        0.9197             nan     1.0000   -0.0153
##     20        1.2740             nan     1.0000   -0.1575
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0684             nan     1.0000    0.0810
##      2        0.9634             nan     1.0000    0.0429
##      3        0.9231             nan     1.0000   -0.0151
##      4        0.9154             nan     1.0000   -0.0335
##      5        0.9295             nan     1.0000   -0.0868
##      6        0.8956             nan     1.0000   -0.0126
##      7        0.8799             nan     1.0000   -0.0082
##      8        0.8826             nan     1.0000   -0.0500
##      9        0.8675             nan     1.0000   -0.0390
##     10        0.8464             nan     1.0000   -0.0119
##     20        1.2892             nan     1.0000   -0.0178
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0001
##      3        1.2935             nan     0.0010    0.0002
##      4        1.2931             nan     0.0010    0.0002
##      5        1.2928             nan     0.0010    0.0002
##      6        1.2925             nan     0.0010    0.0002
##      7        1.2921             nan     0.0010    0.0002
##      8        1.2918             nan     0.0010    0.0001
##      9        1.2914             nan     0.0010    0.0002
##     10        1.2910             nan     0.0010    0.0002
##     20        1.2875             nan     0.0010    0.0002
##     40        1.2806             nan     0.0010    0.0002
##     60        1.2740             nan     0.0010    0.0002
##     80        1.2675             nan     0.0010    0.0001
##    100        1.2612             nan     0.0010    0.0002
##    120        1.2552             nan     0.0010    0.0001
##    140        1.2494             nan     0.0010    0.0001
##    160        1.2438             nan     0.0010    0.0001
##    180        1.2382             nan     0.0010    0.0001
##    200        1.2328             nan     0.0010    0.0001
##    220        1.2276             nan     0.0010    0.0001
##    240        1.2229             nan     0.0010    0.0001
##    260        1.2180             nan     0.0010    0.0001
##    280        1.2132             nan     0.0010    0.0001
##    300        1.2086             nan     0.0010    0.0001
##    320        1.2041             nan     0.0010    0.0001
##    340        1.1998             nan     0.0010    0.0001
##    360        1.1956             nan     0.0010    0.0001
##    380        1.1917             nan     0.0010    0.0001
##    400        1.1876             nan     0.0010    0.0001
##    420        1.1837             nan     0.0010    0.0001
##    440        1.1798             nan     0.0010    0.0001
##    460        1.1760             nan     0.0010    0.0001
##    480        1.1723             nan     0.0010    0.0001
##    500        1.1687             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0002
##      3        1.2935             nan     0.0010    0.0002
##      4        1.2931             nan     0.0010    0.0002
##      5        1.2928             nan     0.0010    0.0001
##      6        1.2924             nan     0.0010    0.0002
##      7        1.2920             nan     0.0010    0.0002
##      8        1.2917             nan     0.0010    0.0002
##      9        1.2914             nan     0.0010    0.0002
##     10        1.2910             nan     0.0010    0.0002
##     20        1.2875             nan     0.0010    0.0002
##     40        1.2806             nan     0.0010    0.0002
##     60        1.2739             nan     0.0010    0.0001
##     80        1.2674             nan     0.0010    0.0002
##    100        1.2612             nan     0.0010    0.0001
##    120        1.2549             nan     0.0010    0.0001
##    140        1.2492             nan     0.0010    0.0001
##    160        1.2435             nan     0.0010    0.0001
##    180        1.2380             nan     0.0010    0.0001
##    200        1.2327             nan     0.0010    0.0001
##    220        1.2276             nan     0.0010    0.0001
##    240        1.2227             nan     0.0010    0.0001
##    260        1.2177             nan     0.0010    0.0001
##    280        1.2130             nan     0.0010    0.0001
##    300        1.2085             nan     0.0010    0.0001
##    320        1.2041             nan     0.0010    0.0001
##    340        1.1996             nan     0.0010    0.0001
##    360        1.1953             nan     0.0010    0.0001
##    380        1.1911             nan     0.0010    0.0001
##    400        1.1871             nan     0.0010    0.0001
##    420        1.1832             nan     0.0010    0.0000
##    440        1.1794             nan     0.0010    0.0001
##    460        1.1756             nan     0.0010    0.0001
##    480        1.1720             nan     0.0010    0.0001
##    500        1.1683             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2939             nan     0.0010    0.0001
##      3        1.2935             nan     0.0010    0.0001
##      4        1.2931             nan     0.0010    0.0002
##      5        1.2927             nan     0.0010    0.0002
##      6        1.2923             nan     0.0010    0.0002
##      7        1.2920             nan     0.0010    0.0002
##      8        1.2917             nan     0.0010    0.0002
##      9        1.2913             nan     0.0010    0.0002
##     10        1.2909             nan     0.0010    0.0002
##     20        1.2873             nan     0.0010    0.0002
##     40        1.2802             nan     0.0010    0.0002
##     60        1.2734             nan     0.0010    0.0001
##     80        1.2670             nan     0.0010    0.0001
##    100        1.2604             nan     0.0010    0.0002
##    120        1.2545             nan     0.0010    0.0001
##    140        1.2489             nan     0.0010    0.0001
##    160        1.2432             nan     0.0010    0.0001
##    180        1.2380             nan     0.0010    0.0001
##    200        1.2330             nan     0.0010    0.0001
##    220        1.2279             nan     0.0010    0.0001
##    240        1.2229             nan     0.0010    0.0001
##    260        1.2182             nan     0.0010    0.0001
##    280        1.2134             nan     0.0010    0.0001
##    300        1.2087             nan     0.0010    0.0001
##    320        1.2042             nan     0.0010    0.0001
##    340        1.1999             nan     0.0010    0.0001
##    360        1.1957             nan     0.0010    0.0001
##    380        1.1915             nan     0.0010    0.0001
##    400        1.1874             nan     0.0010    0.0001
##    420        1.1833             nan     0.0010    0.0001
##    440        1.1795             nan     0.0010    0.0001
##    460        1.1758             nan     0.0010    0.0001
##    480        1.1720             nan     0.0010    0.0001
##    500        1.1683             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2922             nan     0.0010    0.0002
##      6        1.2917             nan     0.0010    0.0002
##      7        1.2913             nan     0.0010    0.0002
##      8        1.2908             nan     0.0010    0.0002
##      9        1.2903             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2853             nan     0.0010    0.0002
##     40        1.2765             nan     0.0010    0.0001
##     60        1.2679             nan     0.0010    0.0002
##     80        1.2595             nan     0.0010    0.0002
##    100        1.2515             nan     0.0010    0.0002
##    120        1.2436             nan     0.0010    0.0002
##    140        1.2359             nan     0.0010    0.0002
##    160        1.2286             nan     0.0010    0.0002
##    180        1.2215             nan     0.0010    0.0001
##    200        1.2146             nan     0.0010    0.0002
##    220        1.2080             nan     0.0010    0.0001
##    240        1.2016             nan     0.0010    0.0001
##    260        1.1952             nan     0.0010    0.0001
##    280        1.1890             nan     0.0010    0.0001
##    300        1.1829             nan     0.0010    0.0001
##    320        1.1770             nan     0.0010    0.0001
##    340        1.1713             nan     0.0010    0.0001
##    360        1.1656             nan     0.0010    0.0001
##    380        1.1602             nan     0.0010    0.0001
##    400        1.1547             nan     0.0010    0.0001
##    420        1.1495             nan     0.0010    0.0001
##    440        1.1445             nan     0.0010    0.0001
##    460        1.1393             nan     0.0010    0.0001
##    480        1.1346             nan     0.0010    0.0001
##    500        1.1298             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2930             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2921             nan     0.0010    0.0002
##      6        1.2916             nan     0.0010    0.0002
##      7        1.2911             nan     0.0010    0.0002
##      8        1.2907             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2852             nan     0.0010    0.0002
##     40        1.2764             nan     0.0010    0.0002
##     60        1.2678             nan     0.0010    0.0002
##     80        1.2593             nan     0.0010    0.0002
##    100        1.2513             nan     0.0010    0.0002
##    120        1.2435             nan     0.0010    0.0002
##    140        1.2359             nan     0.0010    0.0002
##    160        1.2287             nan     0.0010    0.0002
##    180        1.2214             nan     0.0010    0.0002
##    200        1.2145             nan     0.0010    0.0002
##    220        1.2077             nan     0.0010    0.0001
##    240        1.2011             nan     0.0010    0.0002
##    260        1.1944             nan     0.0010    0.0002
##    280        1.1882             nan     0.0010    0.0001
##    300        1.1821             nan     0.0010    0.0001
##    320        1.1764             nan     0.0010    0.0001
##    340        1.1706             nan     0.0010    0.0001
##    360        1.1650             nan     0.0010    0.0001
##    380        1.1596             nan     0.0010    0.0001
##    400        1.1541             nan     0.0010    0.0001
##    420        1.1490             nan     0.0010    0.0001
##    440        1.1438             nan     0.0010    0.0001
##    460        1.1389             nan     0.0010    0.0001
##    480        1.1340             nan     0.0010    0.0001
##    500        1.1293             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2927             nan     0.0010    0.0002
##      5        1.2923             nan     0.0010    0.0002
##      6        1.2918             nan     0.0010    0.0002
##      7        1.2913             nan     0.0010    0.0002
##      8        1.2909             nan     0.0010    0.0002
##      9        1.2905             nan     0.0010    0.0002
##     10        1.2900             nan     0.0010    0.0002
##     20        1.2854             nan     0.0010    0.0002
##     40        1.2766             nan     0.0010    0.0002
##     60        1.2679             nan     0.0010    0.0002
##     80        1.2593             nan     0.0010    0.0001
##    100        1.2512             nan     0.0010    0.0001
##    120        1.2432             nan     0.0010    0.0002
##    140        1.2355             nan     0.0010    0.0001
##    160        1.2280             nan     0.0010    0.0002
##    180        1.2208             nan     0.0010    0.0002
##    200        1.2137             nan     0.0010    0.0002
##    220        1.2069             nan     0.0010    0.0002
##    240        1.2005             nan     0.0010    0.0001
##    260        1.1940             nan     0.0010    0.0001
##    280        1.1880             nan     0.0010    0.0001
##    300        1.1820             nan     0.0010    0.0001
##    320        1.1762             nan     0.0010    0.0001
##    340        1.1704             nan     0.0010    0.0001
##    360        1.1647             nan     0.0010    0.0001
##    380        1.1593             nan     0.0010    0.0001
##    400        1.1541             nan     0.0010    0.0001
##    420        1.1488             nan     0.0010    0.0001
##    440        1.1439             nan     0.0010    0.0001
##    460        1.1390             nan     0.0010    0.0001
##    480        1.1342             nan     0.0010    0.0001
##    500        1.1296             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2934             nan     0.0010    0.0003
##      3        1.2929             nan     0.0010    0.0002
##      4        1.2923             nan     0.0010    0.0003
##      5        1.2918             nan     0.0010    0.0002
##      6        1.2913             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2902             nan     0.0010    0.0002
##      9        1.2897             nan     0.0010    0.0002
##     10        1.2892             nan     0.0010    0.0002
##     20        1.2838             nan     0.0010    0.0002
##     40        1.2738             nan     0.0010    0.0002
##     60        1.2633             nan     0.0010    0.0002
##     80        1.2537             nan     0.0010    0.0002
##    100        1.2443             nan     0.0010    0.0002
##    120        1.2354             nan     0.0010    0.0002
##    140        1.2266             nan     0.0010    0.0002
##    160        1.2176             nan     0.0010    0.0002
##    180        1.2095             nan     0.0010    0.0002
##    200        1.2014             nan     0.0010    0.0002
##    220        1.1934             nan     0.0010    0.0002
##    240        1.1855             nan     0.0010    0.0002
##    260        1.1782             nan     0.0010    0.0001
##    280        1.1710             nan     0.0010    0.0001
##    300        1.1642             nan     0.0010    0.0001
##    320        1.1573             nan     0.0010    0.0001
##    340        1.1508             nan     0.0010    0.0001
##    360        1.1444             nan     0.0010    0.0002
##    380        1.1385             nan     0.0010    0.0001
##    400        1.1325             nan     0.0010    0.0001
##    420        1.1267             nan     0.0010    0.0001
##    440        1.1206             nan     0.0010    0.0001
##    460        1.1148             nan     0.0010    0.0001
##    480        1.1094             nan     0.0010    0.0001
##    500        1.1040             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0003
##      3        1.2930             nan     0.0010    0.0002
##      4        1.2925             nan     0.0010    0.0002
##      5        1.2920             nan     0.0010    0.0002
##      6        1.2914             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2893             nan     0.0010    0.0002
##     20        1.2841             nan     0.0010    0.0002
##     40        1.2738             nan     0.0010    0.0003
##     60        1.2636             nan     0.0010    0.0002
##     80        1.2538             nan     0.0010    0.0002
##    100        1.2445             nan     0.0010    0.0002
##    120        1.2356             nan     0.0010    0.0002
##    140        1.2270             nan     0.0010    0.0002
##    160        1.2185             nan     0.0010    0.0002
##    180        1.2101             nan     0.0010    0.0002
##    200        1.2020             nan     0.0010    0.0002
##    220        1.1942             nan     0.0010    0.0002
##    240        1.1864             nan     0.0010    0.0002
##    260        1.1791             nan     0.0010    0.0001
##    280        1.1719             nan     0.0010    0.0002
##    300        1.1649             nan     0.0010    0.0001
##    320        1.1583             nan     0.0010    0.0001
##    340        1.1519             nan     0.0010    0.0001
##    360        1.1453             nan     0.0010    0.0001
##    380        1.1391             nan     0.0010    0.0001
##    400        1.1332             nan     0.0010    0.0001
##    420        1.1272             nan     0.0010    0.0001
##    440        1.1213             nan     0.0010    0.0001
##    460        1.1156             nan     0.0010    0.0001
##    480        1.1102             nan     0.0010    0.0001
##    500        1.1047             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0003
##      3        1.2930             nan     0.0010    0.0002
##      4        1.2925             nan     0.0010    0.0002
##      5        1.2919             nan     0.0010    0.0002
##      6        1.2914             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2898             nan     0.0010    0.0002
##     10        1.2892             nan     0.0010    0.0003
##     20        1.2840             nan     0.0010    0.0002
##     40        1.2736             nan     0.0010    0.0002
##     60        1.2639             nan     0.0010    0.0002
##     80        1.2541             nan     0.0010    0.0002
##    100        1.2448             nan     0.0010    0.0002
##    120        1.2357             nan     0.0010    0.0002
##    140        1.2270             nan     0.0010    0.0002
##    160        1.2185             nan     0.0010    0.0001
##    180        1.2100             nan     0.0010    0.0002
##    200        1.2021             nan     0.0010    0.0001
##    220        1.1947             nan     0.0010    0.0002
##    240        1.1868             nan     0.0010    0.0002
##    260        1.1793             nan     0.0010    0.0001
##    280        1.1724             nan     0.0010    0.0001
##    300        1.1655             nan     0.0010    0.0001
##    320        1.1587             nan     0.0010    0.0001
##    340        1.1524             nan     0.0010    0.0001
##    360        1.1461             nan     0.0010    0.0001
##    380        1.1399             nan     0.0010    0.0001
##    400        1.1337             nan     0.0010    0.0001
##    420        1.1277             nan     0.0010    0.0001
##    440        1.1220             nan     0.0010    0.0001
##    460        1.1163             nan     0.0010    0.0001
##    480        1.1108             nan     0.0010    0.0001
##    500        1.1054             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2648             nan     0.1000    0.0130
##      2        1.2325             nan     0.1000    0.0142
##      3        1.2053             nan     0.1000    0.0116
##      4        1.1844             nan     0.1000    0.0087
##      5        1.1656             nan     0.1000    0.0077
##      6        1.1480             nan     0.1000    0.0071
##      7        1.1304             nan     0.1000    0.0072
##      8        1.1173             nan     0.1000    0.0053
##      9        1.1032             nan     0.1000    0.0061
##     10        1.0892             nan     0.1000    0.0058
##     20        1.0050             nan     0.1000    0.0008
##     40        0.9212             nan     0.1000    0.0010
##     60        0.8802             nan     0.1000   -0.0006
##     80        0.8544             nan     0.1000   -0.0013
##    100        0.8381             nan     0.1000   -0.0004
##    120        0.8217             nan     0.1000   -0.0003
##    140        0.8121             nan     0.1000   -0.0009
##    160        0.8018             nan     0.1000   -0.0003
##    180        0.7924             nan     0.1000   -0.0009
##    200        0.7817             nan     0.1000   -0.0004
##    220        0.7741             nan     0.1000   -0.0014
##    240        0.7669             nan     0.1000   -0.0004
##    260        0.7615             nan     0.1000   -0.0017
##    280        0.7516             nan     0.1000   -0.0004
##    300        0.7460             nan     0.1000   -0.0020
##    320        0.7382             nan     0.1000   -0.0006
##    340        0.7318             nan     0.1000   -0.0014
##    360        0.7256             nan     0.1000   -0.0006
##    380        0.7190             nan     0.1000   -0.0009
##    400        0.7126             nan     0.1000   -0.0005
##    420        0.7071             nan     0.1000   -0.0011
##    440        0.7023             nan     0.1000   -0.0017
##    460        0.6958             nan     0.1000   -0.0010
##    480        0.6907             nan     0.1000   -0.0008
##    500        0.6861             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2585             nan     0.1000    0.0161
##      2        1.2275             nan     0.1000    0.0142
##      3        1.2069             nan     0.1000    0.0060
##      4        1.1812             nan     0.1000    0.0103
##      5        1.1609             nan     0.1000    0.0096
##      6        1.1474             nan     0.1000    0.0054
##      7        1.1327             nan     0.1000    0.0072
##      8        1.1190             nan     0.1000    0.0057
##      9        1.1048             nan     0.1000    0.0055
##     10        1.0918             nan     0.1000    0.0053
##     20        1.0085             nan     0.1000    0.0011
##     40        0.9182             nan     0.1000   -0.0000
##     60        0.8785             nan     0.1000   -0.0007
##     80        0.8538             nan     0.1000    0.0004
##    100        0.8362             nan     0.1000   -0.0008
##    120        0.8220             nan     0.1000   -0.0003
##    140        0.8112             nan     0.1000   -0.0011
##    160        0.7988             nan     0.1000   -0.0007
##    180        0.7877             nan     0.1000   -0.0008
##    200        0.7776             nan     0.1000   -0.0009
##    220        0.7698             nan     0.1000   -0.0015
##    240        0.7644             nan     0.1000   -0.0012
##    260        0.7566             nan     0.1000   -0.0010
##    280        0.7513             nan     0.1000   -0.0006
##    300        0.7439             nan     0.1000   -0.0010
##    320        0.7380             nan     0.1000   -0.0012
##    340        0.7308             nan     0.1000   -0.0009
##    360        0.7240             nan     0.1000   -0.0006
##    380        0.7194             nan     0.1000   -0.0010
##    400        0.7167             nan     0.1000   -0.0010
##    420        0.7094             nan     0.1000   -0.0008
##    440        0.7044             nan     0.1000   -0.0005
##    460        0.6981             nan     0.1000   -0.0002
##    480        0.6935             nan     0.1000   -0.0002
##    500        0.6901             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2594             nan     0.1000    0.0162
##      2        1.2266             nan     0.1000    0.0122
##      3        1.2010             nan     0.1000    0.0104
##      4        1.1833             nan     0.1000    0.0079
##      5        1.1657             nan     0.1000    0.0078
##      6        1.1515             nan     0.1000    0.0075
##      7        1.1343             nan     0.1000    0.0070
##      8        1.1189             nan     0.1000    0.0063
##      9        1.1023             nan     0.1000    0.0050
##     10        1.0916             nan     0.1000    0.0051
##     20        1.0015             nan     0.1000    0.0025
##     40        0.9175             nan     0.1000    0.0005
##     60        0.8792             nan     0.1000   -0.0013
##     80        0.8554             nan     0.1000   -0.0002
##    100        0.8378             nan     0.1000   -0.0012
##    120        0.8237             nan     0.1000   -0.0011
##    140        0.8063             nan     0.1000   -0.0005
##    160        0.7962             nan     0.1000   -0.0007
##    180        0.7864             nan     0.1000   -0.0007
##    200        0.7781             nan     0.1000   -0.0001
##    220        0.7682             nan     0.1000   -0.0012
##    240        0.7615             nan     0.1000   -0.0025
##    260        0.7530             nan     0.1000   -0.0005
##    280        0.7464             nan     0.1000   -0.0007
##    300        0.7401             nan     0.1000   -0.0016
##    320        0.7330             nan     0.1000   -0.0002
##    340        0.7281             nan     0.1000   -0.0012
##    360        0.7202             nan     0.1000   -0.0016
##    380        0.7150             nan     0.1000   -0.0001
##    400        0.7103             nan     0.1000   -0.0012
##    420        0.7058             nan     0.1000   -0.0007
##    440        0.7001             nan     0.1000   -0.0013
##    460        0.6943             nan     0.1000   -0.0004
##    480        0.6916             nan     0.1000   -0.0007
##    500        0.6856             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2437             nan     0.1000    0.0202
##      2        1.2089             nan     0.1000    0.0164
##      3        1.1762             nan     0.1000    0.0110
##      4        1.1487             nan     0.1000    0.0140
##      5        1.1254             nan     0.1000    0.0095
##      6        1.1043             nan     0.1000    0.0078
##      7        1.0885             nan     0.1000    0.0058
##      8        1.0692             nan     0.1000    0.0066
##      9        1.0525             nan     0.1000    0.0067
##     10        1.0395             nan     0.1000    0.0048
##     20        0.9447             nan     0.1000    0.0013
##     40        0.8563             nan     0.1000   -0.0017
##     60        0.8022             nan     0.1000    0.0007
##     80        0.7639             nan     0.1000   -0.0007
##    100        0.7390             nan     0.1000   -0.0019
##    120        0.7133             nan     0.1000   -0.0015
##    140        0.6899             nan     0.1000   -0.0007
##    160        0.6708             nan     0.1000   -0.0010
##    180        0.6541             nan     0.1000   -0.0003
##    200        0.6372             nan     0.1000   -0.0011
##    220        0.6173             nan     0.1000   -0.0020
##    240        0.5996             nan     0.1000   -0.0014
##    260        0.5835             nan     0.1000   -0.0007
##    280        0.5704             nan     0.1000   -0.0014
##    300        0.5554             nan     0.1000   -0.0016
##    320        0.5405             nan     0.1000   -0.0004
##    340        0.5276             nan     0.1000   -0.0003
##    360        0.5143             nan     0.1000   -0.0011
##    380        0.5024             nan     0.1000   -0.0009
##    400        0.4899             nan     0.1000   -0.0003
##    420        0.4784             nan     0.1000   -0.0003
##    440        0.4686             nan     0.1000   -0.0016
##    460        0.4580             nan     0.1000   -0.0003
##    480        0.4473             nan     0.1000   -0.0011
##    500        0.4375             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2513             nan     0.1000    0.0206
##      2        1.2154             nan     0.1000    0.0176
##      3        1.1826             nan     0.1000    0.0148
##      4        1.1563             nan     0.1000    0.0101
##      5        1.1301             nan     0.1000    0.0106
##      6        1.1073             nan     0.1000    0.0103
##      7        1.0862             nan     0.1000    0.0071
##      8        1.0651             nan     0.1000    0.0067
##      9        1.0521             nan     0.1000    0.0030
##     10        1.0386             nan     0.1000    0.0035
##     20        0.9400             nan     0.1000   -0.0008
##     40        0.8583             nan     0.1000    0.0008
##     60        0.8111             nan     0.1000   -0.0000
##     80        0.7702             nan     0.1000   -0.0010
##    100        0.7448             nan     0.1000   -0.0009
##    120        0.7222             nan     0.1000   -0.0014
##    140        0.7004             nan     0.1000   -0.0017
##    160        0.6788             nan     0.1000   -0.0005
##    180        0.6527             nan     0.1000   -0.0003
##    200        0.6355             nan     0.1000   -0.0012
##    220        0.6174             nan     0.1000   -0.0007
##    240        0.6007             nan     0.1000   -0.0012
##    260        0.5867             nan     0.1000   -0.0015
##    280        0.5742             nan     0.1000   -0.0010
##    300        0.5594             nan     0.1000   -0.0010
##    320        0.5412             nan     0.1000   -0.0013
##    340        0.5295             nan     0.1000   -0.0012
##    360        0.5161             nan     0.1000   -0.0011
##    380        0.5048             nan     0.1000   -0.0012
##    400        0.4919             nan     0.1000   -0.0014
##    420        0.4781             nan     0.1000   -0.0003
##    440        0.4675             nan     0.1000   -0.0015
##    460        0.4559             nan     0.1000   -0.0006
##    480        0.4464             nan     0.1000   -0.0008
##    500        0.4344             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2494             nan     0.1000    0.0198
##      2        1.2105             nan     0.1000    0.0178
##      3        1.1790             nan     0.1000    0.0142
##      4        1.1499             nan     0.1000    0.0102
##      5        1.1225             nan     0.1000    0.0113
##      6        1.0987             nan     0.1000    0.0082
##      7        1.0808             nan     0.1000    0.0071
##      8        1.0641             nan     0.1000    0.0045
##      9        1.0473             nan     0.1000    0.0058
##     10        1.0342             nan     0.1000    0.0053
##     20        0.9428             nan     0.1000   -0.0000
##     40        0.8531             nan     0.1000   -0.0004
##     60        0.8080             nan     0.1000   -0.0016
##     80        0.7742             nan     0.1000   -0.0002
##    100        0.7412             nan     0.1000   -0.0008
##    120        0.7203             nan     0.1000   -0.0010
##    140        0.6929             nan     0.1000   -0.0018
##    160        0.6746             nan     0.1000   -0.0018
##    180        0.6538             nan     0.1000   -0.0009
##    200        0.6352             nan     0.1000   -0.0007
##    220        0.6187             nan     0.1000   -0.0018
##    240        0.6045             nan     0.1000   -0.0006
##    260        0.5871             nan     0.1000   -0.0007
##    280        0.5723             nan     0.1000   -0.0007
##    300        0.5593             nan     0.1000   -0.0015
##    320        0.5461             nan     0.1000   -0.0004
##    340        0.5321             nan     0.1000   -0.0002
##    360        0.5174             nan     0.1000   -0.0009
##    380        0.5058             nan     0.1000   -0.0010
##    400        0.4895             nan     0.1000   -0.0009
##    420        0.4762             nan     0.1000   -0.0005
##    440        0.4671             nan     0.1000   -0.0007
##    460        0.4580             nan     0.1000   -0.0014
##    480        0.4488             nan     0.1000   -0.0008
##    500        0.4383             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2471             nan     0.1000    0.0234
##      2        1.1995             nan     0.1000    0.0189
##      3        1.1636             nan     0.1000    0.0129
##      4        1.1309             nan     0.1000    0.0141
##      5        1.1056             nan     0.1000    0.0073
##      6        1.0801             nan     0.1000    0.0093
##      7        1.0638             nan     0.1000    0.0040
##      8        1.0427             nan     0.1000    0.0079
##      9        1.0266             nan     0.1000    0.0065
##     10        1.0096             nan     0.1000    0.0070
##     20        0.9056             nan     0.1000    0.0003
##     40        0.8038             nan     0.1000    0.0004
##     60        0.7459             nan     0.1000   -0.0009
##     80        0.7010             nan     0.1000   -0.0016
##    100        0.6538             nan     0.1000   -0.0020
##    120        0.6168             nan     0.1000   -0.0009
##    140        0.5863             nan     0.1000   -0.0010
##    160        0.5526             nan     0.1000   -0.0009
##    180        0.5302             nan     0.1000   -0.0006
##    200        0.5060             nan     0.1000   -0.0001
##    220        0.4856             nan     0.1000   -0.0004
##    240        0.4605             nan     0.1000   -0.0018
##    260        0.4393             nan     0.1000   -0.0009
##    280        0.4206             nan     0.1000   -0.0001
##    300        0.4068             nan     0.1000   -0.0009
##    320        0.3886             nan     0.1000   -0.0004
##    340        0.3737             nan     0.1000   -0.0006
##    360        0.3567             nan     0.1000   -0.0008
##    380        0.3457             nan     0.1000   -0.0010
##    400        0.3328             nan     0.1000   -0.0009
##    420        0.3208             nan     0.1000   -0.0006
##    440        0.3101             nan     0.1000   -0.0010
##    460        0.2993             nan     0.1000   -0.0004
##    480        0.2884             nan     0.1000   -0.0012
##    500        0.2763             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2405             nan     0.1000    0.0231
##      2        1.2001             nan     0.1000    0.0137
##      3        1.1616             nan     0.1000    0.0140
##      4        1.1273             nan     0.1000    0.0121
##      5        1.0989             nan     0.1000    0.0086
##      6        1.0786             nan     0.1000    0.0067
##      7        1.0536             nan     0.1000    0.0084
##      8        1.0355             nan     0.1000    0.0075
##      9        1.0209             nan     0.1000    0.0037
##     10        1.0092             nan     0.1000    0.0037
##     20        0.9028             nan     0.1000    0.0019
##     40        0.8041             nan     0.1000    0.0002
##     60        0.7487             nan     0.1000   -0.0020
##     80        0.7126             nan     0.1000   -0.0005
##    100        0.6699             nan     0.1000   -0.0018
##    120        0.6391             nan     0.1000   -0.0018
##    140        0.6065             nan     0.1000   -0.0009
##    160        0.5737             nan     0.1000   -0.0015
##    180        0.5481             nan     0.1000   -0.0022
##    200        0.5262             nan     0.1000   -0.0029
##    220        0.5004             nan     0.1000   -0.0009
##    240        0.4735             nan     0.1000   -0.0032
##    260        0.4528             nan     0.1000   -0.0006
##    280        0.4333             nan     0.1000   -0.0011
##    300        0.4137             nan     0.1000   -0.0005
##    320        0.3968             nan     0.1000    0.0000
##    340        0.3806             nan     0.1000   -0.0008
##    360        0.3663             nan     0.1000   -0.0009
##    380        0.3522             nan     0.1000   -0.0008
##    400        0.3380             nan     0.1000   -0.0005
##    420        0.3235             nan     0.1000   -0.0006
##    440        0.3091             nan     0.1000   -0.0006
##    460        0.2996             nan     0.1000   -0.0005
##    480        0.2866             nan     0.1000   -0.0005
##    500        0.2773             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2460             nan     0.1000    0.0195
##      2        1.1974             nan     0.1000    0.0193
##      3        1.1624             nan     0.1000    0.0169
##      4        1.1299             nan     0.1000    0.0111
##      5        1.1017             nan     0.1000    0.0117
##      6        1.0821             nan     0.1000    0.0077
##      7        1.0544             nan     0.1000    0.0091
##      8        1.0359             nan     0.1000    0.0049
##      9        1.0189             nan     0.1000    0.0064
##     10        1.0043             nan     0.1000    0.0057
##     20        0.8904             nan     0.1000    0.0003
##     40        0.8021             nan     0.1000   -0.0001
##     60        0.7442             nan     0.1000   -0.0022
##     80        0.7032             nan     0.1000   -0.0001
##    100        0.6649             nan     0.1000   -0.0021
##    120        0.6306             nan     0.1000   -0.0018
##    140        0.5949             nan     0.1000   -0.0006
##    160        0.5620             nan     0.1000   -0.0013
##    180        0.5340             nan     0.1000   -0.0014
##    200        0.5121             nan     0.1000   -0.0011
##    220        0.4896             nan     0.1000   -0.0018
##    240        0.4676             nan     0.1000   -0.0015
##    260        0.4499             nan     0.1000   -0.0007
##    280        0.4274             nan     0.1000   -0.0011
##    300        0.4117             nan     0.1000   -0.0002
##    320        0.3912             nan     0.1000   -0.0006
##    340        0.3774             nan     0.1000   -0.0007
##    360        0.3620             nan     0.1000   -0.0005
##    380        0.3492             nan     0.1000   -0.0010
##    400        0.3336             nan     0.1000   -0.0014
##    420        0.3225             nan     0.1000   -0.0010
##    440        0.3104             nan     0.1000   -0.0012
##    460        0.2975             nan     0.1000   -0.0002
##    480        0.2853             nan     0.1000   -0.0007
##    500        0.2765             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2282             nan     0.2000    0.0296
##      2        1.1824             nan     0.2000    0.0197
##      3        1.1476             nan     0.2000    0.0119
##      4        1.1131             nan     0.2000    0.0129
##      5        1.0841             nan     0.2000    0.0132
##      6        1.0622             nan     0.2000    0.0054
##      7        1.0443             nan     0.2000    0.0066
##      8        1.0242             nan     0.2000    0.0076
##      9        1.0133             nan     0.2000    0.0032
##     10        1.0003             nan     0.2000    0.0024
##     20        0.9191             nan     0.2000    0.0011
##     40        0.8581             nan     0.2000   -0.0003
##     60        0.8211             nan     0.2000   -0.0011
##     80        0.7995             nan     0.2000   -0.0018
##    100        0.7756             nan     0.2000   -0.0023
##    120        0.7607             nan     0.2000   -0.0010
##    140        0.7458             nan     0.2000   -0.0013
##    160        0.7344             nan     0.2000   -0.0029
##    180        0.7262             nan     0.2000   -0.0021
##    200        0.7159             nan     0.2000   -0.0031
##    220        0.7043             nan     0.2000   -0.0025
##    240        0.6926             nan     0.2000   -0.0020
##    260        0.6847             nan     0.2000   -0.0009
##    280        0.6792             nan     0.2000   -0.0022
##    300        0.6710             nan     0.2000   -0.0015
##    320        0.6632             nan     0.2000    0.0001
##    340        0.6583             nan     0.2000   -0.0021
##    360        0.6551             nan     0.2000   -0.0007
##    380        0.6468             nan     0.2000   -0.0019
##    400        0.6412             nan     0.2000   -0.0039
##    420        0.6295             nan     0.2000   -0.0015
##    440        0.6271             nan     0.2000   -0.0031
##    460        0.6225             nan     0.2000   -0.0009
##    480        0.6154             nan     0.2000   -0.0036
##    500        0.6141             nan     0.2000   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2260             nan     0.2000    0.0341
##      2        1.1778             nan     0.2000    0.0220
##      3        1.1465             nan     0.2000    0.0147
##      4        1.1171             nan     0.2000    0.0127
##      5        1.0915             nan     0.2000    0.0110
##      6        1.0708             nan     0.2000    0.0051
##      7        1.0436             nan     0.2000    0.0090
##      8        1.0267             nan     0.2000    0.0065
##      9        1.0162             nan     0.2000    0.0028
##     10        0.9990             nan     0.2000    0.0049
##     20        0.9166             nan     0.2000   -0.0010
##     40        0.8479             nan     0.2000   -0.0007
##     60        0.8179             nan     0.2000   -0.0003
##     80        0.7933             nan     0.2000   -0.0016
##    100        0.7741             nan     0.2000   -0.0023
##    120        0.7612             nan     0.2000   -0.0003
##    140        0.7505             nan     0.2000   -0.0011
##    160        0.7390             nan     0.2000   -0.0012
##    180        0.7260             nan     0.2000   -0.0004
##    200        0.7202             nan     0.2000   -0.0009
##    220        0.7087             nan     0.2000   -0.0037
##    240        0.6993             nan     0.2000   -0.0024
##    260        0.6921             nan     0.2000   -0.0030
##    280        0.6845             nan     0.2000   -0.0013
##    300        0.6737             nan     0.2000   -0.0025
##    320        0.6680             nan     0.2000   -0.0018
##    340        0.6606             nan     0.2000   -0.0015
##    360        0.6561             nan     0.2000   -0.0022
##    380        0.6494             nan     0.2000   -0.0003
##    400        0.6445             nan     0.2000   -0.0004
##    420        0.6403             nan     0.2000   -0.0036
##    440        0.6337             nan     0.2000   -0.0018
##    460        0.6308             nan     0.2000   -0.0028
##    480        0.6244             nan     0.2000   -0.0009
##    500        0.6192             nan     0.2000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2436             nan     0.2000    0.0214
##      2        1.1860             nan     0.2000    0.0241
##      3        1.1488             nan     0.2000    0.0104
##      4        1.1168             nan     0.2000    0.0130
##      5        1.0917             nan     0.2000    0.0113
##      6        1.0681             nan     0.2000    0.0101
##      7        1.0594             nan     0.2000    0.0005
##      8        1.0434             nan     0.2000    0.0060
##      9        1.0210             nan     0.2000    0.0048
##     10        1.0074             nan     0.2000    0.0057
##     20        0.9327             nan     0.2000    0.0008
##     40        0.8595             nan     0.2000    0.0000
##     60        0.8259             nan     0.2000   -0.0017
##     80        0.8046             nan     0.2000   -0.0025
##    100        0.7876             nan     0.2000   -0.0011
##    120        0.7668             nan     0.2000   -0.0017
##    140        0.7552             nan     0.2000   -0.0017
##    160        0.7413             nan     0.2000   -0.0021
##    180        0.7297             nan     0.2000   -0.0005
##    200        0.7211             nan     0.2000   -0.0049
##    220        0.7122             nan     0.2000   -0.0013
##    240        0.7004             nan     0.2000   -0.0031
##    260        0.6924             nan     0.2000   -0.0017
##    280        0.6819             nan     0.2000   -0.0024
##    300        0.6715             nan     0.2000   -0.0017
##    320        0.6643             nan     0.2000   -0.0034
##    340        0.6559             nan     0.2000   -0.0017
##    360        0.6494             nan     0.2000   -0.0010
##    380        0.6461             nan     0.2000   -0.0023
##    400        0.6359             nan     0.2000   -0.0031
##    420        0.6311             nan     0.2000   -0.0018
##    440        0.6222             nan     0.2000   -0.0001
##    460        0.6187             nan     0.2000   -0.0024
##    480        0.6093             nan     0.2000   -0.0016
##    500        0.6060             nan     0.2000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2072             nan     0.2000    0.0402
##      2        1.1427             nan     0.2000    0.0281
##      3        1.0919             nan     0.2000    0.0196
##      4        1.0564             nan     0.2000    0.0133
##      5        1.0291             nan     0.2000    0.0073
##      6        1.0002             nan     0.2000    0.0099
##      7        0.9796             nan     0.2000    0.0033
##      8        0.9633             nan     0.2000    0.0053
##      9        0.9520             nan     0.2000   -0.0004
##     10        0.9387             nan     0.2000    0.0031
##     20        0.8547             nan     0.2000   -0.0019
##     40        0.7764             nan     0.2000   -0.0015
##     60        0.7304             nan     0.2000   -0.0030
##     80        0.6921             nan     0.2000   -0.0045
##    100        0.6442             nan     0.2000   -0.0019
##    120        0.6104             nan     0.2000   -0.0021
##    140        0.5841             nan     0.2000   -0.0005
##    160        0.5450             nan     0.2000   -0.0039
##    180        0.5140             nan     0.2000   -0.0011
##    200        0.4945             nan     0.2000   -0.0031
##    220        0.4698             nan     0.2000   -0.0025
##    240        0.4509             nan     0.2000   -0.0009
##    260        0.4322             nan     0.2000   -0.0011
##    280        0.4134             nan     0.2000   -0.0027
##    300        0.3907             nan     0.2000   -0.0020
##    320        0.3735             nan     0.2000   -0.0011
##    340        0.3574             nan     0.2000   -0.0013
##    360        0.3421             nan     0.2000   -0.0010
##    380        0.3254             nan     0.2000   -0.0019
##    400        0.3097             nan     0.2000   -0.0009
##    420        0.2973             nan     0.2000   -0.0001
##    440        0.2870             nan     0.2000   -0.0019
##    460        0.2768             nan     0.2000   -0.0019
##    480        0.2643             nan     0.2000   -0.0005
##    500        0.2565             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2120             nan     0.2000    0.0315
##      2        1.1532             nan     0.2000    0.0289
##      3        1.1045             nan     0.2000    0.0190
##      4        1.0749             nan     0.2000    0.0140
##      5        1.0447             nan     0.2000    0.0106
##      6        1.0202             nan     0.2000    0.0078
##      7        0.9999             nan     0.2000    0.0069
##      8        0.9800             nan     0.2000    0.0049
##      9        0.9623             nan     0.2000    0.0053
##     10        0.9490             nan     0.2000    0.0026
##     20        0.8653             nan     0.2000   -0.0026
##     40        0.7769             nan     0.2000   -0.0041
##     60        0.7214             nan     0.2000   -0.0026
##     80        0.6720             nan     0.2000   -0.0026
##    100        0.6335             nan     0.2000   -0.0024
##    120        0.5987             nan     0.2000   -0.0020
##    140        0.5642             nan     0.2000   -0.0006
##    160        0.5405             nan     0.2000   -0.0007
##    180        0.5156             nan     0.2000   -0.0008
##    200        0.4875             nan     0.2000   -0.0033
##    220        0.4666             nan     0.2000   -0.0013
##    240        0.4431             nan     0.2000   -0.0019
##    260        0.4225             nan     0.2000   -0.0023
##    280        0.4007             nan     0.2000   -0.0008
##    300        0.3877             nan     0.2000   -0.0016
##    320        0.3694             nan     0.2000   -0.0003
##    340        0.3578             nan     0.2000   -0.0008
##    360        0.3434             nan     0.2000   -0.0014
##    380        0.3351             nan     0.2000   -0.0025
##    400        0.3214             nan     0.2000   -0.0012
##    420        0.3109             nan     0.2000   -0.0016
##    440        0.3020             nan     0.2000   -0.0034
##    460        0.2882             nan     0.2000   -0.0015
##    480        0.2752             nan     0.2000   -0.0012
##    500        0.2645             nan     0.2000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2229             nan     0.2000    0.0269
##      2        1.1601             nan     0.2000    0.0281
##      3        1.1140             nan     0.2000    0.0152
##      4        1.0750             nan     0.2000    0.0143
##      5        1.0386             nan     0.2000    0.0123
##      6        1.0164             nan     0.2000    0.0034
##      7        0.9970             nan     0.2000    0.0063
##      8        0.9768             nan     0.2000    0.0066
##      9        0.9687             nan     0.2000   -0.0021
##     10        0.9510             nan     0.2000    0.0042
##     20        0.8574             nan     0.2000    0.0001
##     40        0.7717             nan     0.2000   -0.0016
##     60        0.7239             nan     0.2000   -0.0030
##     80        0.6873             nan     0.2000   -0.0036
##    100        0.6569             nan     0.2000   -0.0044
##    120        0.6238             nan     0.2000   -0.0049
##    140        0.5842             nan     0.2000   -0.0030
##    160        0.5587             nan     0.2000   -0.0046
##    180        0.5365             nan     0.2000   -0.0032
##    200        0.5092             nan     0.2000   -0.0026
##    220        0.4842             nan     0.2000   -0.0010
##    240        0.4583             nan     0.2000   -0.0010
##    260        0.4418             nan     0.2000   -0.0024
##    280        0.4227             nan     0.2000   -0.0023
##    300        0.4027             nan     0.2000   -0.0009
##    320        0.3862             nan     0.2000   -0.0012
##    340        0.3729             nan     0.2000   -0.0010
##    360        0.3585             nan     0.2000   -0.0014
##    380        0.3473             nan     0.2000   -0.0018
##    400        0.3318             nan     0.2000   -0.0006
##    420        0.3201             nan     0.2000   -0.0006
##    440        0.3082             nan     0.2000   -0.0018
##    460        0.2921             nan     0.2000   -0.0008
##    480        0.2812             nan     0.2000   -0.0014
##    500        0.2759             nan     0.2000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2052             nan     0.2000    0.0358
##      2        1.1289             nan     0.2000    0.0333
##      3        1.0769             nan     0.2000    0.0253
##      4        1.0380             nan     0.2000    0.0116
##      5        1.0037             nan     0.2000    0.0131
##      6        0.9729             nan     0.2000    0.0090
##      7        0.9528             nan     0.2000    0.0055
##      8        0.9328             nan     0.2000    0.0012
##      9        0.9171             nan     0.2000    0.0022
##     10        0.8982             nan     0.2000    0.0048
##     20        0.8062             nan     0.2000   -0.0026
##     40        0.7109             nan     0.2000   -0.0015
##     60        0.6343             nan     0.2000   -0.0049
##     80        0.5840             nan     0.2000   -0.0029
##    100        0.5290             nan     0.2000   -0.0029
##    120        0.4834             nan     0.2000   -0.0025
##    140        0.4381             nan     0.2000   -0.0037
##    160        0.3944             nan     0.2000   -0.0017
##    180        0.3631             nan     0.2000   -0.0011
##    200        0.3282             nan     0.2000   -0.0021
##    220        0.3011             nan     0.2000   -0.0014
##    240        0.2835             nan     0.2000   -0.0023
##    260        0.2629             nan     0.2000   -0.0010
##    280        0.2471             nan     0.2000   -0.0006
##    300        0.2314             nan     0.2000   -0.0023
##    320        0.2172             nan     0.2000   -0.0002
##    340        0.2036             nan     0.2000   -0.0008
##    360        0.1924             nan     0.2000   -0.0009
##    380        0.1776             nan     0.2000   -0.0001
##    400        0.1701             nan     0.2000   -0.0002
##    420        0.1588             nan     0.2000   -0.0012
##    440        0.1516             nan     0.2000   -0.0015
##    460        0.1423             nan     0.2000   -0.0003
##    480        0.1336             nan     0.2000   -0.0005
##    500        0.1261             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2071             nan     0.2000    0.0436
##      2        1.1261             nan     0.2000    0.0362
##      3        1.0734             nan     0.2000    0.0216
##      4        1.0359             nan     0.2000    0.0104
##      5        1.0044             nan     0.2000    0.0113
##      6        0.9803             nan     0.2000    0.0048
##      7        0.9534             nan     0.2000    0.0077
##      8        0.9361             nan     0.2000    0.0034
##      9        0.9221             nan     0.2000    0.0019
##     10        0.9044             nan     0.2000    0.0082
##     20        0.8159             nan     0.2000   -0.0014
##     40        0.7123             nan     0.2000    0.0001
##     60        0.6457             nan     0.2000   -0.0032
##     80        0.5779             nan     0.2000   -0.0025
##    100        0.5316             nan     0.2000   -0.0009
##    120        0.4940             nan     0.2000   -0.0066
##    140        0.4491             nan     0.2000   -0.0052
##    160        0.4129             nan     0.2000   -0.0024
##    180        0.3868             nan     0.2000   -0.0027
##    200        0.3547             nan     0.2000   -0.0014
##    220        0.3276             nan     0.2000   -0.0010
##    240        0.3068             nan     0.2000   -0.0008
##    260        0.2870             nan     0.2000   -0.0020
##    280        0.2615             nan     0.2000   -0.0011
##    300        0.2411             nan     0.2000   -0.0013
##    320        0.2250             nan     0.2000   -0.0017
##    340        0.2091             nan     0.2000   -0.0018
##    360        0.1940             nan     0.2000   -0.0016
##    380        0.1837             nan     0.2000   -0.0006
##    400        0.1727             nan     0.2000   -0.0005
##    420        0.1619             nan     0.2000   -0.0007
##    440        0.1524             nan     0.2000   -0.0005
##    460        0.1418             nan     0.2000   -0.0003
##    480        0.1346             nan     0.2000   -0.0006
##    500        0.1261             nan     0.2000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1885             nan     0.2000    0.0441
##      2        1.1268             nan     0.2000    0.0232
##      3        1.0714             nan     0.2000    0.0154
##      4        1.0342             nan     0.2000    0.0141
##      5        1.0032             nan     0.2000    0.0099
##      6        0.9759             nan     0.2000    0.0079
##      7        0.9539             nan     0.2000    0.0055
##      8        0.9333             nan     0.2000    0.0083
##      9        0.9178             nan     0.2000    0.0026
##     10        0.8998             nan     0.2000    0.0026
##     20        0.8147             nan     0.2000   -0.0009
##     40        0.7101             nan     0.2000   -0.0038
##     60        0.6326             nan     0.2000   -0.0021
##     80        0.5779             nan     0.2000   -0.0003
##    100        0.5199             nan     0.2000   -0.0024
##    120        0.4790             nan     0.2000   -0.0030
##    140        0.4413             nan     0.2000   -0.0024
##    160        0.4034             nan     0.2000   -0.0020
##    180        0.3666             nan     0.2000   -0.0019
##    200        0.3379             nan     0.2000   -0.0047
##    220        0.3135             nan     0.2000   -0.0009
##    240        0.2863             nan     0.2000   -0.0025
##    260        0.2652             nan     0.2000   -0.0017
##    280        0.2480             nan     0.2000   -0.0019
##    300        0.2322             nan     0.2000   -0.0013
##    320        0.2185             nan     0.2000   -0.0003
##    340        0.2052             nan     0.2000   -0.0020
##    360        0.1934             nan     0.2000   -0.0011
##    380        0.1804             nan     0.2000   -0.0009
##    400        0.1685             nan     0.2000   -0.0016
##    420        0.1588             nan     0.2000   -0.0011
##    440        0.1493             nan     0.2000   -0.0005
##    460        0.1411             nan     0.2000   -0.0008
##    480        0.1327             nan     0.2000   -0.0007
##    500        0.1244             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1968             nan     0.3000    0.0409
##      2        1.1473             nan     0.3000    0.0246
##      3        1.1050             nan     0.3000    0.0147
##      4        1.0681             nan     0.3000    0.0143
##      5        1.0435             nan     0.3000    0.0058
##      6        1.0172             nan     0.3000    0.0108
##      7        0.9943             nan     0.3000    0.0082
##      8        0.9751             nan     0.3000    0.0065
##      9        0.9606             nan     0.3000    0.0029
##     10        0.9494             nan     0.3000    0.0040
##     20        0.8870             nan     0.3000   -0.0046
##     40        0.8252             nan     0.3000    0.0010
##     60        0.7887             nan     0.3000   -0.0017
##     80        0.7597             nan     0.3000   -0.0030
##    100        0.7424             nan     0.3000   -0.0030
##    120        0.7264             nan     0.3000   -0.0030
##    140        0.7162             nan     0.3000   -0.0006
##    160        0.6991             nan     0.3000   -0.0019
##    180        0.6855             nan     0.3000   -0.0008
##    200        0.6746             nan     0.3000   -0.0038
##    220        0.6609             nan     0.3000   -0.0042
##    240        0.6516             nan     0.3000   -0.0045
##    260        0.6384             nan     0.3000   -0.0002
##    280        0.6323             nan     0.3000   -0.0007
##    300        0.6257             nan     0.3000   -0.0014
##    320        0.6172             nan     0.3000   -0.0052
##    340        0.6090             nan     0.3000   -0.0005
##    360        0.6044             nan     0.3000   -0.0032
##    380        0.5937             nan     0.3000   -0.0031
##    400        0.5862             nan     0.3000   -0.0032
##    420        0.5779             nan     0.3000   -0.0019
##    440        0.5708             nan     0.3000   -0.0042
##    460        0.5669             nan     0.3000   -0.0028
##    480        0.5589             nan     0.3000   -0.0033
##    500        0.5542             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2058             nan     0.3000    0.0417
##      2        1.1479             nan     0.3000    0.0312
##      3        1.1014             nan     0.3000    0.0180
##      4        1.0763             nan     0.3000    0.0056
##      5        1.0440             nan     0.3000    0.0133
##      6        1.0122             nan     0.3000    0.0112
##      7        0.9956             nan     0.3000    0.0013
##      8        0.9810             nan     0.3000    0.0000
##      9        0.9656             nan     0.3000    0.0057
##     10        0.9537             nan     0.3000    0.0039
##     20        0.8833             nan     0.3000   -0.0023
##     40        0.8439             nan     0.3000   -0.0064
##     60        0.7999             nan     0.3000   -0.0024
##     80        0.7716             nan     0.3000   -0.0027
##    100        0.7544             nan     0.3000   -0.0003
##    120        0.7387             nan     0.3000   -0.0025
##    140        0.7311             nan     0.3000   -0.0055
##    160        0.7093             nan     0.3000   -0.0018
##    180        0.6921             nan     0.3000   -0.0005
##    200        0.6834             nan     0.3000   -0.0011
##    220        0.6740             nan     0.3000   -0.0039
##    240        0.6638             nan     0.3000   -0.0010
##    260        0.6550             nan     0.3000   -0.0061
##    280        0.6423             nan     0.3000   -0.0018
##    300        0.6315             nan     0.3000   -0.0041
##    320        0.6298             nan     0.3000   -0.0025
##    340        0.6211             nan     0.3000   -0.0018
##    360        0.6158             nan     0.3000   -0.0040
##    380        0.6094             nan     0.3000   -0.0076
##    400        0.5980             nan     0.3000   -0.0013
##    420        0.5961             nan     0.3000   -0.0078
##    440        0.5861             nan     0.3000   -0.0041
##    460        0.5849             nan     0.3000   -0.0039
##    480        0.5809             nan     0.3000   -0.0024
##    500        0.5728             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2040             nan     0.3000    0.0449
##      2        1.1471             nan     0.3000    0.0257
##      3        1.1034             nan     0.3000    0.0156
##      4        1.0656             nan     0.3000    0.0100
##      5        1.0271             nan     0.3000    0.0134
##      6        1.0128             nan     0.3000    0.0009
##      7        0.9940             nan     0.3000    0.0012
##      8        0.9778             nan     0.3000   -0.0000
##      9        0.9590             nan     0.3000    0.0066
##     10        0.9470             nan     0.3000    0.0034
##     20        0.8921             nan     0.3000   -0.0034
##     40        0.8279             nan     0.3000   -0.0003
##     60        0.7985             nan     0.3000   -0.0010
##     80        0.7793             nan     0.3000   -0.0013
##    100        0.7655             nan     0.3000   -0.0003
##    120        0.7474             nan     0.3000   -0.0015
##    140        0.7281             nan     0.3000   -0.0068
##    160        0.7035             nan     0.3000   -0.0027
##    180        0.6919             nan     0.3000   -0.0026
##    200        0.6806             nan     0.3000   -0.0049
##    220        0.6709             nan     0.3000   -0.0020
##    240        0.6603             nan     0.3000   -0.0027
##    260        0.6555             nan     0.3000   -0.0031
##    280        0.6421             nan     0.3000   -0.0052
##    300        0.6377             nan     0.3000   -0.0048
##    320        0.6309             nan     0.3000   -0.0012
##    340        0.6195             nan     0.3000   -0.0024
##    360        0.6164             nan     0.3000   -0.0063
##    380        0.6085             nan     0.3000   -0.0029
##    400        0.5993             nan     0.3000   -0.0038
##    420        0.5926             nan     0.3000   -0.0042
##    440        0.5892             nan     0.3000   -0.0021
##    460        0.5795             nan     0.3000   -0.0047
##    480        0.5719             nan     0.3000   -0.0033
##    500        0.5645             nan     0.3000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1860             nan     0.3000    0.0428
##      2        1.1066             nan     0.3000    0.0318
##      3        1.0503             nan     0.3000    0.0238
##      4        1.0096             nan     0.3000    0.0114
##      5        0.9795             nan     0.3000    0.0112
##      6        0.9464             nan     0.3000    0.0096
##      7        0.9230             nan     0.3000    0.0062
##      8        0.9108             nan     0.3000    0.0007
##      9        0.8943             nan     0.3000    0.0028
##     10        0.8822             nan     0.3000   -0.0025
##     20        0.8036             nan     0.3000   -0.0012
##     40        0.7225             nan     0.3000   -0.0039
##     60        0.6673             nan     0.3000   -0.0033
##     80        0.6140             nan     0.3000   -0.0029
##    100        0.5674             nan     0.3000   -0.0086
##    120        0.5204             nan     0.3000   -0.0051
##    140        0.4811             nan     0.3000   -0.0036
##    160        0.4475             nan     0.3000   -0.0033
##    180        0.4142             nan     0.3000   -0.0037
##    200        0.3902             nan     0.3000   -0.0041
##    220        0.3650             nan     0.3000   -0.0007
##    240        0.3471             nan     0.3000   -0.0035
##    260        0.3224             nan     0.3000   -0.0019
##    280        0.3023             nan     0.3000   -0.0022
##    300        0.2885             nan     0.3000   -0.0019
##    320        0.2721             nan     0.3000   -0.0019
##    340        0.2575             nan     0.3000   -0.0030
##    360        0.2446             nan     0.3000   -0.0030
##    380        0.2313             nan     0.3000   -0.0023
##    400        0.2192             nan     0.3000   -0.0014
##    420        0.2082             nan     0.3000   -0.0019
##    440        0.1965             nan     0.3000   -0.0009
##    460        0.1897             nan     0.3000   -0.0018
##    480        0.1798             nan     0.3000   -0.0003
##    500        0.1720             nan     0.3000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1887             nan     0.3000    0.0514
##      2        1.1216             nan     0.3000    0.0290
##      3        1.0698             nan     0.3000    0.0155
##      4        1.0274             nan     0.3000    0.0183
##      5        0.9922             nan     0.3000    0.0124
##      6        0.9730             nan     0.3000   -0.0025
##      7        0.9525             nan     0.3000    0.0059
##      8        0.9292             nan     0.3000    0.0097
##      9        0.9151             nan     0.3000    0.0020
##     10        0.8972             nan     0.3000    0.0043
##     20        0.8174             nan     0.3000    0.0005
##     40        0.7458             nan     0.3000   -0.0022
##     60        0.6753             nan     0.3000   -0.0037
##     80        0.6175             nan     0.3000   -0.0011
##    100        0.5818             nan     0.3000   -0.0036
##    120        0.5439             nan     0.3000   -0.0072
##    140        0.5130             nan     0.3000   -0.0027
##    160        0.4817             nan     0.3000   -0.0059
##    180        0.4495             nan     0.3000   -0.0038
##    200        0.4236             nan     0.3000   -0.0012
##    220        0.4044             nan     0.3000   -0.0043
##    240        0.3857             nan     0.3000   -0.0002
##    260        0.3524             nan     0.3000   -0.0018
##    280        0.3304             nan     0.3000   -0.0014
##    300        0.3085             nan     0.3000   -0.0015
##    320        0.2834             nan     0.3000   -0.0018
##    340        0.2631             nan     0.3000   -0.0017
##    360        0.2497             nan     0.3000   -0.0014
##    380        0.2322             nan     0.3000   -0.0011
##    400        0.2216             nan     0.3000   -0.0011
##    420        0.2105             nan     0.3000   -0.0006
##    440        0.1969             nan     0.3000   -0.0013
##    460        0.1840             nan     0.3000   -0.0019
##    480        0.1787             nan     0.3000   -0.0005
##    500        0.1687             nan     0.3000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1742             nan     0.3000    0.0513
##      2        1.1012             nan     0.3000    0.0303
##      3        1.0455             nan     0.3000    0.0225
##      4        1.0062             nan     0.3000    0.0164
##      5        0.9746             nan     0.3000    0.0089
##      6        0.9666             nan     0.3000   -0.0059
##      7        0.9454             nan     0.3000    0.0051
##      8        0.9211             nan     0.3000    0.0014
##      9        0.9097             nan     0.3000    0.0005
##     10        0.8998             nan     0.3000   -0.0013
##     20        0.8145             nan     0.3000    0.0044
##     40        0.7350             nan     0.3000   -0.0014
##     60        0.6639             nan     0.3000   -0.0069
##     80        0.6251             nan     0.3000   -0.0024
##    100        0.5675             nan     0.3000   -0.0029
##    120        0.5257             nan     0.3000   -0.0011
##    140        0.4930             nan     0.3000   -0.0027
##    160        0.4660             nan     0.3000   -0.0043
##    180        0.4356             nan     0.3000   -0.0031
##    200        0.4119             nan     0.3000   -0.0032
##    220        0.3896             nan     0.3000   -0.0008
##    240        0.3654             nan     0.3000   -0.0010
##    260        0.3460             nan     0.3000   -0.0019
##    280        0.3294             nan     0.3000   -0.0010
##    300        0.3087             nan     0.3000   -0.0020
##    320        0.2862             nan     0.3000   -0.0020
##    340        0.2681             nan     0.3000   -0.0012
##    360        0.2588             nan     0.3000   -0.0007
##    380        0.2414             nan     0.3000   -0.0008
##    400        0.2257             nan     0.3000   -0.0016
##    420        0.2155             nan     0.3000   -0.0004
##    440        0.2028             nan     0.3000   -0.0025
##    460        0.1935             nan     0.3000   -0.0020
##    480        0.1862             nan     0.3000   -0.0013
##    500        0.1744             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1659             nan     0.3000    0.0424
##      2        1.0755             nan     0.3000    0.0405
##      3        1.0066             nan     0.3000    0.0276
##      4        0.9715             nan     0.3000    0.0103
##      5        0.9369             nan     0.3000    0.0124
##      6        0.9176             nan     0.3000    0.0029
##      7        0.8972             nan     0.3000    0.0045
##      8        0.8838             nan     0.3000    0.0003
##      9        0.8739             nan     0.3000   -0.0043
##     10        0.8609             nan     0.3000    0.0004
##     20        0.7637             nan     0.3000    0.0024
##     40        0.6389             nan     0.3000   -0.0077
##     60        0.5524             nan     0.3000   -0.0038
##     80        0.7896             nan     0.3000   -0.0036
##    100        0.7534             nan     0.3000   -0.0019
##    120        0.7089             nan     0.3000   -0.0044
##    140        0.6808             nan     0.3000   -0.0015
##    160        0.6385             nan     0.3000   -0.0022
##    180        0.6077             nan     0.3000   -0.0027
##    200        0.5791             nan     0.3000   -0.0015
##    220        0.5592             nan     0.3000   -0.0013
##    240        0.5430             nan     0.3000   -0.0008
##    260        0.5231             nan     0.3000   -0.0008
##    280        0.5030             nan     0.3000   -0.0004
##    300        0.4901             nan     0.3000   -0.0007
##    320        0.4756             nan     0.3000   -0.0028
##    340        0.4609             nan     0.3000   -0.0019
##    360        0.4518             nan     0.3000   -0.0009
##    380        0.4412             nan     0.3000   -0.0003
##    400        0.4329             nan     0.3000   -0.0001
##    420        0.4253             nan     0.3000   -0.0011
##    440        0.4172             nan     0.3000   -0.0011
##    460        0.4087             nan     0.3000   -0.0009
##    480        0.4033             nan     0.3000   -0.0013
##    500        0.3961             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1585             nan     0.3000    0.0680
##      2        1.0757             nan     0.3000    0.0408
##      3        1.0192             nan     0.3000    0.0079
##      4        0.9794             nan     0.3000    0.0111
##      5        0.9444             nan     0.3000    0.0095
##      6        0.9245             nan     0.3000    0.0001
##      7        0.8967             nan     0.3000   -0.0003
##      8        0.8801             nan     0.3000    0.0019
##      9        0.8639             nan     0.3000    0.0010
##     10        0.8491             nan     0.3000   -0.0029
##     20        0.7700             nan     0.3000   -0.0088
##     40        0.6317             nan     0.3000   -0.0035
##     60        0.5545             nan     0.3000   -0.0053
##     80        0.4886             nan     0.3000   -0.0039
##    100        0.4333             nan     0.3000   -0.0020
##    120        0.3907             nan     0.3000   -0.0032
##    140        0.3464             nan     0.3000   -0.0019
##    160        0.3108             nan     0.3000   -0.0024
##    180        0.2801             nan     0.3000   -0.0016
##    200        0.2503             nan     0.3000   -0.0035
##    220        0.2232             nan     0.3000   -0.0023
##    240        0.2058             nan     0.3000   -0.0018
##    260        0.1851             nan     0.3000   -0.0016
##    280        0.1640             nan     0.3000   -0.0016
##    300        0.1446             nan     0.3000   -0.0007
##    320        0.1307             nan     0.3000   -0.0008
##    340        0.1203             nan     0.3000   -0.0006
##    360        0.1096             nan     0.3000   -0.0010
##    380        0.1003             nan     0.3000   -0.0003
##    400        0.0930             nan     0.3000   -0.0006
##    420        0.0854             nan     0.3000   -0.0005
##    440        0.0790             nan     0.3000   -0.0003
##    460        0.0719             nan     0.3000   -0.0005
##    480        0.0661             nan     0.3000   -0.0007
##    500        0.0616             nan     0.3000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1535             nan     0.3000    0.0562
##      2        1.0718             nan     0.3000    0.0318
##      3        1.0140             nan     0.3000    0.0160
##      4        0.9742             nan     0.3000    0.0157
##      5        0.9427             nan     0.3000    0.0032
##      6        0.9256             nan     0.3000   -0.0058
##      7        0.9014             nan     0.3000    0.0043
##      8        0.8936             nan     0.3000   -0.0067
##      9        0.8841             nan     0.3000   -0.0033
##     10        0.8731             nan     0.3000   -0.0047
##     20        0.7717             nan     0.3000   -0.0067
##     40        0.6367             nan     0.3000   -0.0021
##     60        0.5568             nan     0.3000   -0.0061
##     80        0.4835             nan     0.3000   -0.0046
##    100        0.4277             nan     0.3000   -0.0024
##    120        0.3776             nan     0.3000   -0.0030
##    140        0.3381             nan     0.3000   -0.0030
##    160        0.2960             nan     0.3000   -0.0024
##    180        0.2593             nan     0.3000   -0.0005
##    200        0.2347             nan     0.3000   -0.0005
##    220        0.2124             nan     0.3000   -0.0007
##    240        0.1892             nan     0.3000   -0.0008
##    260        0.1693             nan     0.3000   -0.0014
##    280        0.1532             nan     0.3000   -0.0009
##    300        0.1414             nan     0.3000   -0.0014
##    320        0.1281             nan     0.3000   -0.0006
##    340        0.1180             nan     0.3000   -0.0013
##    360        0.1087             nan     0.3000   -0.0008
##    380        0.0981             nan     0.3000   -0.0011
##    400        0.0895             nan     0.3000   -0.0003
##    420        0.0816             nan     0.3000   -0.0001
##    440        0.0753             nan     0.3000   -0.0004
##    460        0.0713             nan     0.3000   -0.0010
##    480        0.0651             nan     0.3000   -0.0004
##    500        0.0607             nan     0.3000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1825             nan     0.5000    0.0525
##      2        1.1093             nan     0.5000    0.0255
##      3        1.0472             nan     0.5000    0.0258
##      4        1.0027             nan     0.5000    0.0199
##      5        0.9767             nan     0.5000    0.0058
##      6        0.9643             nan     0.5000   -0.0002
##      7        0.9442             nan     0.5000    0.0058
##      8        0.9366             nan     0.5000   -0.0028
##      9        0.9254             nan     0.5000    0.0036
##     10        0.9144             nan     0.5000    0.0017
##     20        0.8590             nan     0.5000   -0.0043
##     40        0.7871             nan     0.5000   -0.0038
##     60        0.7516             nan     0.5000   -0.0006
##     80        0.7325             nan     0.5000   -0.0054
##    100        0.7046             nan     0.5000   -0.0027
##    120        0.7050             nan     0.5000   -0.0075
##    140        0.6830             nan     0.5000   -0.0001
##    160        0.6526             nan     0.5000   -0.0025
##    180        0.6400             nan     0.5000   -0.0032
##    200        0.6202             nan     0.5000   -0.0048
##    220        0.6093             nan     0.5000   -0.0026
##    240        0.5897             nan     0.5000   -0.0054
##    260        0.5821             nan     0.5000   -0.0028
##    280        0.5686             nan     0.5000   -0.0041
##    300        0.5558             nan     0.5000   -0.0001
##    320        0.5474             nan     0.5000   -0.0018
##    340        0.5419             nan     0.5000   -0.0027
##    360        0.5376             nan     0.5000   -0.0046
##    380        0.5256             nan     0.5000   -0.0017
##    400        0.5123             nan     0.5000   -0.0030
##    420        0.5052             nan     0.5000   -0.0053
##    440        0.4976             nan     0.5000   -0.0075
##    460        0.4842             nan     0.5000   -0.0011
##    480        0.4846             nan     0.5000   -0.0054
##    500        0.4740             nan     0.5000   -0.0036
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1675             nan     0.5000    0.0621
##      2        1.1046             nan     0.5000    0.0249
##      3        1.0431             nan     0.5000    0.0163
##      4        0.9914             nan     0.5000    0.0215
##      5        0.9766             nan     0.5000    0.0000
##      6        0.9599             nan     0.5000    0.0036
##      7        0.9504             nan     0.5000   -0.0018
##      8        0.9393             nan     0.5000    0.0013
##      9        0.9149             nan     0.5000    0.0101
##     10        0.9101             nan     0.5000   -0.0020
##     20        0.8456             nan     0.5000   -0.0020
##     40        0.8006             nan     0.5000   -0.0052
##     60        0.7430             nan     0.5000   -0.0002
##     80        0.7187             nan     0.5000   -0.0065
##    100        0.6974             nan     0.5000   -0.0035
##    120        0.6825             nan     0.5000   -0.0027
##    140        0.6560             nan     0.5000   -0.0053
##    160        0.6557             nan     0.5000   -0.0100
##    180        0.6383             nan     0.5000   -0.0046
##    200        0.6236             nan     0.5000   -0.0074
##    220        0.6095             nan     0.5000   -0.0074
##    240        0.5936             nan     0.5000   -0.0049
##    260        0.5861             nan     0.5000   -0.0087
##    280        0.5672             nan     0.5000   -0.0040
##    300        0.5523             nan     0.5000   -0.0062
##    320        0.5441             nan     0.5000   -0.0041
##    340        0.5318             nan     0.5000   -0.0071
##    360        0.5237             nan     0.5000   -0.0043
##    380        0.5156             nan     0.5000   -0.0079
##    400        0.5032             nan     0.5000   -0.0047
##    420        0.4973             nan     0.5000   -0.0033
##    440        0.4866             nan     0.5000   -0.0063
##    460        0.4730             nan     0.5000   -0.0026
##    480        0.4708             nan     0.5000   -0.0061
##    500        0.4705             nan     0.5000   -0.0039
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1589             nan     0.5000    0.0613
##      2        1.0993             nan     0.5000    0.0212
##      3        1.0445             nan     0.5000    0.0099
##      4        0.9979             nan     0.5000    0.0159
##      5        0.9794             nan     0.5000    0.0011
##      6        0.9602             nan     0.5000    0.0041
##      7        0.9453             nan     0.5000    0.0029
##      8        0.9321             nan     0.5000    0.0025
##      9        0.9169             nan     0.5000    0.0045
##     10        0.9110             nan     0.5000   -0.0031
##     20        0.8550             nan     0.5000   -0.0031
##     40        0.8072             nan     0.5000   -0.0064
##     60        0.7914             nan     0.5000   -0.0122
##     80        0.7605             nan     0.5000    0.0006
##    100        0.7313             nan     0.5000   -0.0028
##    120        0.7099             nan     0.5000   -0.0056
##    140        0.6834             nan     0.5000   -0.0017
##    160        0.6591             nan     0.5000   -0.0026
##    180        0.6409             nan     0.5000   -0.0060
##    200        0.6316             nan     0.5000   -0.0042
##    220        0.6245             nan     0.5000   -0.0034
##    240        0.6135             nan     0.5000   -0.0074
##    260        0.5986             nan     0.5000   -0.0004
##    280        0.5897             nan     0.5000   -0.0042
##    300        0.5747             nan     0.5000   -0.0061
##    320        0.5831             nan     0.5000   -0.0057
##    340        0.5652             nan     0.5000   -0.0039
##    360        0.5581             nan     0.5000   -0.0031
##    380        0.5455             nan     0.5000   -0.0030
##    400        0.5373             nan     0.5000   -0.0092
##    420        0.5408             nan     0.5000   -0.0033
##    440        0.5345             nan     0.5000   -0.0009
##    460        0.5187             nan     0.5000   -0.0028
##    480        0.5074             nan     0.5000   -0.0016
##    500        0.4932             nan     0.5000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1332             nan     0.5000    0.0730
##      2        1.0524             nan     0.5000    0.0302
##      3        0.9787             nan     0.5000    0.0164
##      4        0.9468             nan     0.5000   -0.0020
##      5        0.9244             nan     0.5000   -0.0020
##      6        0.9003             nan     0.5000    0.0023
##      7        0.8720             nan     0.5000    0.0074
##      8        0.8645             nan     0.5000   -0.0021
##      9        0.8500             nan     0.5000   -0.0043
##     10        0.8352             nan     0.5000    0.0008
##     20        0.7728             nan     0.5000   -0.0107
##     40        0.6945             nan     0.5000   -0.0056
##     60        0.6359             nan     0.5000   -0.0063
##     80        0.5822             nan     0.5000   -0.0078
##    100        0.5296             nan     0.5000   -0.0041
##    120        0.4773             nan     0.5000   -0.0069
##    140        0.4358             nan     0.5000   -0.0029
##    160        0.3869             nan     0.5000   -0.0107
##    180        0.3515             nan     0.5000   -0.0044
##    200        0.3253             nan     0.5000   -0.0049
##    220        0.3022             nan     0.5000   -0.0016
##    240        0.2750             nan     0.5000   -0.0013
##    260        0.2538             nan     0.5000   -0.0044
##    280        0.2309             nan     0.5000   -0.0057
##    300        0.2136             nan     0.5000   -0.0007
##    320        0.1954             nan     0.5000   -0.0018
##    340        0.1806             nan     0.5000   -0.0015
##    360        0.1647             nan     0.5000   -0.0037
##    380        0.1545             nan     0.5000   -0.0059
##    400        0.1442             nan     0.5000   -0.0039
##    420        0.1348             nan     0.5000   -0.0022
##    440        0.1232             nan     0.5000   -0.0016
##    460        0.1114             nan     0.5000   -0.0004
##    480        0.1051             nan     0.5000   -0.0024
##    500        0.0983             nan     0.5000   -0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1283             nan     0.5000    0.0821
##      2        1.0481             nan     0.5000    0.0300
##      3        0.9882             nan     0.5000    0.0164
##      4        0.9544             nan     0.5000    0.0029
##      5        0.9245             nan     0.5000    0.0040
##      6        0.8926             nan     0.5000    0.0072
##      7        0.8832             nan     0.5000   -0.0050
##      8        0.8763             nan     0.5000   -0.0035
##      9        0.8698             nan     0.5000   -0.0150
##     10        0.8670             nan     0.5000   -0.0228
##     20        0.8026             nan     0.5000   -0.0029
##     40        0.7026             nan     0.5000   -0.0123
##     60        0.6268             nan     0.5000   -0.0063
##     80        0.5574             nan     0.5000   -0.0135
##    100        0.5001             nan     0.5000   -0.0046
##    120        0.4491             nan     0.5000   -0.0046
##    140        0.4245             nan     0.5000   -0.0122
##    160        0.3717             nan     0.5000   -0.0041
##    180        2.4636             nan     0.5000   -0.0033
##    200        2.4349             nan     0.5000   -0.0032
##    220        2.4107             nan     0.5000   -0.0005
##    240        2.3818             nan     0.5000   -0.0012
##    260        2.3641             nan     0.5000   -0.0025
##    280        2.3491             nan     0.5000   -0.0020
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1151             nan     0.5000    0.0854
##      2        1.0363             nan     0.5000    0.0265
##      3        0.9806             nan     0.5000    0.0247
##      4        0.9561             nan     0.5000    0.0027
##      5        0.9237             nan     0.5000    0.0047
##      6        0.9109             nan     0.5000   -0.0028
##      7        0.8965             nan     0.5000   -0.0020
##      8        0.8776             nan     0.5000   -0.0103
##      9        0.8777             nan     0.5000   -0.0109
##     10        0.8639             nan     0.5000   -0.0079
##     20        0.7820             nan     0.5000   -0.0082
##     40        0.6599             nan     0.5000   -0.0102
##     60        0.5801             nan     0.5000   -0.0055
##     80        0.5293             nan     0.5000   -0.0033
##    100        0.4655             nan     0.5000   -0.0077
##    120        0.4163             nan     0.5000   -0.0050
##    140        0.3786             nan     0.5000   -0.0183
##    160        0.3321             nan     0.5000   -0.0016
##    180        0.3149             nan     0.5000   -0.0065
##    200        0.2702             nan     0.5000   -0.0046
##    220        0.2379             nan     0.5000   -0.0023
##    240        0.2124             nan     0.5000    0.0000
##    260        0.1946             nan     0.5000   -0.0014
##    280        0.1806             nan     0.5000   -0.0038
##    300        0.1614             nan     0.5000   -0.0025
##    320        0.1500             nan     0.5000   -0.0012
##    340        0.1378             nan     0.5000   -0.0010
##    360        0.1239             nan     0.5000   -0.0014
##    380        0.1150             nan     0.5000   -0.0014
##    400        0.1053             nan     0.5000   -0.0004
##    420        0.0976             nan     0.5000   -0.0022
##    440        0.0892             nan     0.5000   -0.0007
##    460        0.0829             nan     0.5000   -0.0016
##    480        0.0755             nan     0.5000   -0.0005
##    500        0.0716             nan     0.5000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1028             nan     0.5000    0.0668
##      2        1.0203             nan     0.5000    0.0151
##      3        0.9526             nan     0.5000    0.0206
##      4        0.9124             nan     0.5000    0.0112
##      5        0.8847             nan     0.5000    0.0048
##      6        0.8653             nan     0.5000   -0.0035
##      7        0.8601             nan     0.5000   -0.0127
##      8        0.8413             nan     0.5000    0.0014
##      9        0.8295             nan     0.5000   -0.0070
##     10        0.8100             nan     0.5000   -0.0013
##     20        0.7436             nan     0.5000   -0.0278
##     40        0.9243             nan     0.5000   -0.0096
##     60        0.8715             nan     0.5000   -0.0052
##     80        0.8021             nan     0.5000   -0.0079
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000   -0.0045
##    140           inf             nan     0.5000    0.0158
##    160           inf             nan     0.5000   -0.0011
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1038             nan     0.5000    0.0846
##      2        1.0199             nan     0.5000    0.0298
##      3        0.9722             nan     0.5000    0.0101
##      4        0.9332             nan     0.5000    0.0009
##      5        0.9069             nan     0.5000   -0.0107
##      6        0.8788             nan     0.5000    0.0027
##      7        0.8601             nan     0.5000   -0.0148
##      8        0.8600             nan     0.5000   -0.0174
##      9        0.8439             nan     0.5000   -0.0078
##     10        0.8352             nan     0.5000   -0.0077
##     20        0.7398             nan     0.5000   -0.0061
##     40        0.6265             nan     0.5000   -0.0053
##     60        0.5152             nan     0.5000   -0.0116
##     80        0.4478             nan     0.5000   -0.0233
##    100        0.3240             nan     0.5000   -0.0052
##    120        0.2625             nan     0.5000   -0.0113
##    140        0.2184             nan     0.5000   -0.0033
##    160        0.1822             nan     0.5000   -0.0030
##    180        0.1523             nan     0.5000   -0.0021
##    200        0.1320             nan     0.5000   -0.0027
##    220        0.1145             nan     0.5000   -0.0006
##    240        0.1025             nan     0.5000   -0.0006
##    260        0.0908             nan     0.5000   -0.0015
##    280        0.0786             nan     0.5000   -0.0011
##    300        0.0659             nan     0.5000   -0.0010
##    320        0.0542             nan     0.5000   -0.0011
##    340        0.0473             nan     0.5000   -0.0008
##    360        0.0414             nan     0.5000   -0.0003
##    380        0.0368             nan     0.5000   -0.0003
##    400        0.0323             nan     0.5000   -0.0007
##    420        0.0284             nan     0.5000   -0.0003
##    440        0.0256             nan     0.5000   -0.0003
##    460        0.0231             nan     0.5000   -0.0005
##    480        0.0207             nan     0.5000   -0.0005
##    500        0.0191             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0790             nan     0.5000    0.1023
##      2        0.9803             nan     0.5000    0.0392
##      3        0.9446             nan     0.5000    0.0029
##      4        0.9131             nan     0.5000    0.0031
##      5        0.8851             nan     0.5000   -0.0017
##      6        0.8558             nan     0.5000    0.0046
##      7        0.8393             nan     0.5000   -0.0028
##      8        0.8349             nan     0.5000   -0.0154
##      9        0.8124             nan     0.5000   -0.0087
##     10        0.8082             nan     0.5000   -0.0124
##     20        0.7281             nan     0.5000   -0.0242
##     40        0.5771             nan     0.5000    0.0007
##     60        0.4912             nan     0.5000   -0.0100
##     80        0.3982             nan     0.5000   -0.0034
##    100        0.3341             nan     0.5000   -0.0040
##    120        0.2716             nan     0.5000   -0.0035
##    140        0.2289             nan     0.5000   -0.0043
##    160        0.1808             nan     0.5000   -0.0026
##    180        0.1494             nan     0.5000   -0.0061
##    200        0.1259             nan     0.5000   -0.0009
##    220        0.1083             nan     0.5000   -0.0016
##    240        0.0971             nan     0.5000   -0.0023
##    260        0.0821             nan     0.5000   -0.0005
##    280        0.0724             nan     0.5000   -0.0005
##    300        0.0627             nan     0.5000   -0.0009
##    320        0.0544             nan     0.5000   -0.0006
##    340        0.0449             nan     0.5000   -0.0005
##    360        0.0390             nan     0.5000   -0.0008
##    380        0.0347             nan     0.5000   -0.0004
##    400        0.0313             nan     0.5000   -0.0004
##    420        0.0283             nan     0.5000   -0.0005
##    440        0.0245             nan     0.5000   -0.0005
##    460        0.0222             nan     0.5000   -0.0002
##    480        0.0196             nan     0.5000   -0.0008
##    500        0.0170             nan     0.5000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1382             nan     1.0000    0.0456
##      2        1.0647             nan     1.0000    0.0250
##      3        1.0083             nan     1.0000    0.0228
##      4        1.0073             nan     1.0000   -0.0351
##      5        0.9985             nan     1.0000   -0.0183
##      6        0.9820             nan     1.0000    0.0015
##      7        1.0001             nan     1.0000   -0.0355
##      8        1.0132             nan     1.0000   -0.0298
##      9        1.0482             nan     1.0000   -0.0527
##     10        1.0352             nan     1.0000   -0.0138
##     20        0.9584             nan     1.0000   -0.0109
##     40      815.8534             nan     1.0000   -0.0309
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1308             nan     1.0000    0.0533
##      2        1.0584             nan     1.0000    0.0227
##      3        0.9932             nan     1.0000    0.0276
##      4        0.9718             nan     1.0000   -0.0053
##      5        0.9512             nan     1.0000    0.0002
##      6        0.9130             nan     1.0000    0.0166
##      7        0.8915             nan     1.0000    0.0050
##      8        0.8981             nan     1.0000   -0.0228
##      9        0.9088             nan     1.0000   -0.0318
##     10        0.9282             nan     1.0000   -0.0361
##     20        0.9302             nan     1.0000   -0.0369
##     40        0.8716             nan     1.0000   -0.0085
##     60        0.8253             nan     1.0000   -0.0137
##     80        1.0821             nan     1.0000   -0.0231
##    100        1.3220             nan     1.0000   -0.0417
##    120        1.2639             nan     1.0000   -0.0006
##    140        1.2501             nan     1.0000    0.0044
##    160        1.1942             nan     1.0000    0.0190
##    180 78097103364806.5625             nan     1.0000   -0.0098
##    200 78097103364806.5156             nan     1.0000   -0.0003
##    220 78097103364806.4844             nan     1.0000    0.0000
##    240 78097103364806.5156             nan     1.0000   -0.0233
##    260 78097103364806.5156             nan     1.0000    0.0005
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1167             nan     1.0000    0.0831
##      2        1.0419             nan     1.0000    0.0380
##      3        0.9908             nan     1.0000    0.0236
##      4        0.9917             nan     1.0000   -0.0167
##      5        0.9664             nan     1.0000    0.0057
##      6        0.9589             nan     1.0000   -0.0172
##      7        0.9616             nan     1.0000   -0.0207
##      8        0.9463             nan     1.0000    0.0029
##      9        0.9403             nan     1.0000   -0.0133
##     10        1.0631             nan     1.0000   -0.1246
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0598             nan     1.0000    0.1045
##      2        0.9947             nan     1.0000    0.0020
##      3        0.9612             nan     1.0000    0.0035
##      4        0.9410             nan     1.0000   -0.0339
##      5        0.9476             nan     1.0000   -0.0347
##      6        0.9557             nan     1.0000   -0.0436
##      7        0.9229             nan     1.0000    0.0012
##      8        0.8753             nan     1.0000    0.0074
##      9        0.8798             nan     1.0000   -0.0419
##     10        0.8735             nan     1.0000   -0.0348
##     20        3.8680             nan     1.0000   -0.0511
##     40 285500944196332567882882242806244086288440602442808646088286286206860608448608268686664888600880622482026826642628008226640.0000             nan     1.0000    0.0286
##     60           inf             nan     1.0000    0.0021
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0902             nan     1.0000    0.0838
##      2        1.0118             nan     1.0000    0.0272
##      3        0.9694             nan     1.0000    0.0114
##      4        0.9320             nan     1.0000   -0.0046
##      5        0.9370             nan     1.0000   -0.0325
##      6        0.8878             nan     1.0000    0.0093
##      7        0.8823             nan     1.0000   -0.0487
##      8        0.8464             nan     1.0000    0.0035
##      9        0.8398             nan     1.0000   -0.0162
##     10        0.8347             nan     1.0000   -0.0349
##     20        0.8021             nan     1.0000   -0.0382
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0768             nan     1.0000    0.0904
##      2        0.9958             nan     1.0000    0.0203
##      3        0.9475             nan     1.0000    0.0173
##      4        0.9598             nan     1.0000   -0.0349
##      5        0.9413             nan     1.0000   -0.0281
##      6        0.9974             nan     1.0000   -0.0638
##      7        0.9811             nan     1.0000   -0.0169
##      8        1.1285             nan     1.0000   -0.1598
##      9        1.0966             nan     1.0000   -0.0142
##     10        1.1016             nan     1.0000   -0.0460
##     20       11.3047             nan     1.0000   -0.0326
##     40      353.1651             nan     1.0000   -0.1139
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0356             nan     1.0000    0.1158
##      2        0.9822             nan     1.0000   -0.0219
##      3        0.9385             nan     1.0000   -0.0199
##      4        0.9529             nan     1.0000   -0.0571
##      5        1.0267             nan     1.0000   -0.1162
##      6        0.9601             nan     1.0000    0.0036
##      7        0.9700             nan     1.0000   -0.0611
##      8        0.9235             nan     1.0000   -0.0014
##      9        1.0007             nan     1.0000   -0.1332
##     10        0.9852             nan     1.0000   -0.0492
##     20        2.2210             nan     1.0000   -0.0629
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0718             nan     1.0000    0.0901
##      2        0.9718             nan     1.0000    0.0124
##      3        0.9451             nan     1.0000   -0.0089
##      4        0.9314             nan     1.0000   -0.0192
##      5        0.9242             nan     1.0000   -0.0412
##      6        0.9078             nan     1.0000   -0.0427
##      7        0.8761             nan     1.0000   -0.0122
##      8        0.8709             nan     1.0000   -0.0317
##      9        0.8684             nan     1.0000   -0.0402
##     10        0.8902             nan     1.0000   -0.0543
##     20        0.8269             nan     1.0000   -0.0252
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0433             nan     1.0000    0.1059
##      2        0.9669             nan     1.0000    0.0247
##      3        0.9227             nan     1.0000    0.0007
##      4        0.9029             nan     1.0000   -0.0140
##      5        0.8680             nan     1.0000   -0.0115
##      6        0.8480             nan     1.0000   -0.0095
##      7        0.8419             nan     1.0000   -0.0313
##      8        0.8454             nan     1.0000   -0.0477
##      9        0.8619             nan     1.0000   -0.0426
##     10        0.8474             nan     1.0000   -0.0180
##     20        1.0227             nan     1.0000   -0.0480
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2859             nan     0.0010    0.0002
##     40        1.2786             nan     0.0010    0.0002
##     60        1.2716             nan     0.0010    0.0002
##     80        1.2647             nan     0.0010    0.0001
##    100        1.2581             nan     0.0010    0.0001
##    120        1.2520             nan     0.0010    0.0001
##    140        1.2457             nan     0.0010    0.0001
##    160        1.2396             nan     0.0010    0.0001
##    180        1.2339             nan     0.0010    0.0001
##    200        1.2285             nan     0.0010    0.0001
##    220        1.2231             nan     0.0010    0.0001
##    240        1.2179             nan     0.0010    0.0001
##    260        1.2128             nan     0.0010    0.0001
##    280        1.2078             nan     0.0010    0.0001
##    300        1.2031             nan     0.0010    0.0001
##    320        1.1986             nan     0.0010    0.0001
##    340        1.1941             nan     0.0010    0.0001
##    360        1.1899             nan     0.0010    0.0001
##    380        1.1856             nan     0.0010    0.0001
##    400        1.1815             nan     0.0010    0.0001
##    420        1.1776             nan     0.0010    0.0001
##    440        1.1738             nan     0.0010    0.0001
##    460        1.1698             nan     0.0010    0.0001
##    480        1.1659             nan     0.0010    0.0001
##    500        1.1622             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2906             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2894             nan     0.0010    0.0002
##     20        1.2856             nan     0.0010    0.0002
##     40        1.2782             nan     0.0010    0.0002
##     60        1.2714             nan     0.0010    0.0002
##     80        1.2647             nan     0.0010    0.0002
##    100        1.2584             nan     0.0010    0.0001
##    120        1.2520             nan     0.0010    0.0001
##    140        1.2459             nan     0.0010    0.0001
##    160        1.2402             nan     0.0010    0.0001
##    180        1.2344             nan     0.0010    0.0001
##    200        1.2289             nan     0.0010    0.0001
##    220        1.2234             nan     0.0010    0.0001
##    240        1.2181             nan     0.0010    0.0001
##    260        1.2130             nan     0.0010    0.0001
##    280        1.2082             nan     0.0010    0.0001
##    300        1.2037             nan     0.0010    0.0001
##    320        1.1991             nan     0.0010    0.0001
##    340        1.1946             nan     0.0010    0.0001
##    360        1.1902             nan     0.0010    0.0001
##    380        1.1859             nan     0.0010    0.0001
##    400        1.1817             nan     0.0010    0.0001
##    420        1.1775             nan     0.0010    0.0001
##    440        1.1735             nan     0.0010    0.0001
##    460        1.1697             nan     0.0010    0.0001
##    480        1.1659             nan     0.0010    0.0001
##    500        1.1623             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2921             nan     0.0010    0.0002
##      4        1.2917             nan     0.0010    0.0002
##      5        1.2913             nan     0.0010    0.0002
##      6        1.2909             nan     0.0010    0.0002
##      7        1.2905             nan     0.0010    0.0002
##      8        1.2901             nan     0.0010    0.0002
##      9        1.2897             nan     0.0010    0.0002
##     10        1.2893             nan     0.0010    0.0002
##     20        1.2854             nan     0.0010    0.0002
##     40        1.2782             nan     0.0010    0.0001
##     60        1.2711             nan     0.0010    0.0002
##     80        1.2643             nan     0.0010    0.0002
##    100        1.2578             nan     0.0010    0.0001
##    120        1.2515             nan     0.0010    0.0001
##    140        1.2455             nan     0.0010    0.0001
##    160        1.2396             nan     0.0010    0.0001
##    180        1.2338             nan     0.0010    0.0001
##    200        1.2282             nan     0.0010    0.0001
##    220        1.2227             nan     0.0010    0.0001
##    240        1.2178             nan     0.0010    0.0001
##    260        1.2127             nan     0.0010    0.0001
##    280        1.2078             nan     0.0010    0.0001
##    300        1.2032             nan     0.0010    0.0001
##    320        1.1985             nan     0.0010    0.0001
##    340        1.1940             nan     0.0010    0.0001
##    360        1.1898             nan     0.0010    0.0001
##    380        1.1857             nan     0.0010    0.0001
##    400        1.1815             nan     0.0010    0.0001
##    420        1.1775             nan     0.0010    0.0001
##    440        1.1735             nan     0.0010    0.0001
##    460        1.1698             nan     0.0010    0.0001
##    480        1.1661             nan     0.0010    0.0001
##    500        1.1624             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2838             nan     0.0010    0.0002
##     40        1.2743             nan     0.0010    0.0002
##     60        1.2653             nan     0.0010    0.0002
##     80        1.2565             nan     0.0010    0.0002
##    100        1.2480             nan     0.0010    0.0002
##    120        1.2397             nan     0.0010    0.0002
##    140        1.2316             nan     0.0010    0.0002
##    160        1.2239             nan     0.0010    0.0002
##    180        1.2165             nan     0.0010    0.0001
##    200        1.2089             nan     0.0010    0.0002
##    220        1.2019             nan     0.0010    0.0002
##    240        1.1950             nan     0.0010    0.0002
##    260        1.1884             nan     0.0010    0.0001
##    280        1.1820             nan     0.0010    0.0001
##    300        1.1758             nan     0.0010    0.0001
##    320        1.1696             nan     0.0010    0.0001
##    340        1.1636             nan     0.0010    0.0001
##    360        1.1580             nan     0.0010    0.0001
##    380        1.1523             nan     0.0010    0.0001
##    400        1.1470             nan     0.0010    0.0001
##    420        1.1414             nan     0.0010    0.0001
##    440        1.1361             nan     0.0010    0.0001
##    460        1.1311             nan     0.0010    0.0001
##    480        1.1262             nan     0.0010    0.0001
##    500        1.1212             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0003
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2836             nan     0.0010    0.0002
##     40        1.2743             nan     0.0010    0.0002
##     60        1.2653             nan     0.0010    0.0002
##     80        1.2566             nan     0.0010    0.0002
##    100        1.2484             nan     0.0010    0.0002
##    120        1.2399             nan     0.0010    0.0002
##    140        1.2318             nan     0.0010    0.0002
##    160        1.2240             nan     0.0010    0.0002
##    180        1.2165             nan     0.0010    0.0002
##    200        1.2093             nan     0.0010    0.0002
##    220        1.2022             nan     0.0010    0.0002
##    240        1.1954             nan     0.0010    0.0001
##    260        1.1886             nan     0.0010    0.0001
##    280        1.1824             nan     0.0010    0.0001
##    300        1.1761             nan     0.0010    0.0001
##    320        1.1700             nan     0.0010    0.0001
##    340        1.1642             nan     0.0010    0.0001
##    360        1.1583             nan     0.0010    0.0001
##    380        1.1525             nan     0.0010    0.0001
##    400        1.1472             nan     0.0010    0.0001
##    420        1.1419             nan     0.0010    0.0001
##    440        1.1366             nan     0.0010    0.0001
##    460        1.1316             nan     0.0010    0.0001
##    480        1.1268             nan     0.0010    0.0001
##    500        1.1219             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0003
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2837             nan     0.0010    0.0002
##     40        1.2744             nan     0.0010    0.0002
##     60        1.2655             nan     0.0010    0.0002
##     80        1.2567             nan     0.0010    0.0002
##    100        1.2484             nan     0.0010    0.0002
##    120        1.2404             nan     0.0010    0.0002
##    140        1.2324             nan     0.0010    0.0002
##    160        1.2247             nan     0.0010    0.0002
##    180        1.2173             nan     0.0010    0.0002
##    200        1.2101             nan     0.0010    0.0002
##    220        1.2032             nan     0.0010    0.0001
##    240        1.1965             nan     0.0010    0.0001
##    260        1.1898             nan     0.0010    0.0002
##    280        1.1833             nan     0.0010    0.0001
##    300        1.1772             nan     0.0010    0.0002
##    320        1.1709             nan     0.0010    0.0001
##    340        1.1649             nan     0.0010    0.0001
##    360        1.1591             nan     0.0010    0.0001
##    380        1.1533             nan     0.0010    0.0001
##    400        1.1478             nan     0.0010    0.0001
##    420        1.1424             nan     0.0010    0.0001
##    440        1.1371             nan     0.0010    0.0001
##    460        1.1320             nan     0.0010    0.0001
##    480        1.1269             nan     0.0010    0.0001
##    500        1.1219             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0003
##      7        1.2895             nan     0.0010    0.0003
##      8        1.2889             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0003
##     20        1.2823             nan     0.0010    0.0003
##     40        1.2715             nan     0.0010    0.0002
##     60        1.2612             nan     0.0010    0.0002
##     80        1.2510             nan     0.0010    0.0002
##    100        1.2413             nan     0.0010    0.0002
##    120        1.2319             nan     0.0010    0.0002
##    140        1.2228             nan     0.0010    0.0002
##    160        1.2139             nan     0.0010    0.0002
##    180        1.2054             nan     0.0010    0.0002
##    200        1.1973             nan     0.0010    0.0002
##    220        1.1893             nan     0.0010    0.0002
##    240        1.1815             nan     0.0010    0.0001
##    260        1.1739             nan     0.0010    0.0001
##    280        1.1665             nan     0.0010    0.0002
##    300        1.1594             nan     0.0010    0.0001
##    320        1.1526             nan     0.0010    0.0001
##    340        1.1459             nan     0.0010    0.0002
##    360        1.1395             nan     0.0010    0.0001
##    380        1.1330             nan     0.0010    0.0001
##    400        1.1268             nan     0.0010    0.0001
##    420        1.1208             nan     0.0010    0.0001
##    440        1.1150             nan     0.0010    0.0001
##    460        1.1092             nan     0.0010    0.0001
##    480        1.1036             nan     0.0010    0.0001
##    500        1.0984             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0003
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0003
##      7        1.2896             nan     0.0010    0.0003
##      8        1.2890             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0003
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2823             nan     0.0010    0.0003
##     40        1.2716             nan     0.0010    0.0002
##     60        1.2612             nan     0.0010    0.0002
##     80        1.2515             nan     0.0010    0.0002
##    100        1.2414             nan     0.0010    0.0002
##    120        1.2320             nan     0.0010    0.0002
##    140        1.2230             nan     0.0010    0.0002
##    160        1.2142             nan     0.0010    0.0002
##    180        1.2058             nan     0.0010    0.0002
##    200        1.1975             nan     0.0010    0.0002
##    220        1.1895             nan     0.0010    0.0002
##    240        1.1817             nan     0.0010    0.0002
##    260        1.1741             nan     0.0010    0.0002
##    280        1.1668             nan     0.0010    0.0001
##    300        1.1597             nan     0.0010    0.0001
##    320        1.1526             nan     0.0010    0.0001
##    340        1.1458             nan     0.0010    0.0002
##    360        1.1394             nan     0.0010    0.0001
##    380        1.1331             nan     0.0010    0.0001
##    400        1.1270             nan     0.0010    0.0001
##    420        1.1209             nan     0.0010    0.0001
##    440        1.1150             nan     0.0010    0.0001
##    460        1.1094             nan     0.0010    0.0001
##    480        1.1038             nan     0.0010    0.0001
##    500        1.0981             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2927             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2916             nan     0.0010    0.0003
##      4        1.2910             nan     0.0010    0.0003
##      5        1.2905             nan     0.0010    0.0002
##      6        1.2899             nan     0.0010    0.0003
##      7        1.2893             nan     0.0010    0.0003
##      8        1.2886             nan     0.0010    0.0003
##      9        1.2881             nan     0.0010    0.0002
##     10        1.2875             nan     0.0010    0.0002
##     20        1.2820             nan     0.0010    0.0003
##     40        1.2713             nan     0.0010    0.0002
##     60        1.2612             nan     0.0010    0.0002
##     80        1.2510             nan     0.0010    0.0002
##    100        1.2411             nan     0.0010    0.0002
##    120        1.2314             nan     0.0010    0.0002
##    140        1.2221             nan     0.0010    0.0002
##    160        1.2133             nan     0.0010    0.0002
##    180        1.2049             nan     0.0010    0.0002
##    200        1.1965             nan     0.0010    0.0002
##    220        1.1885             nan     0.0010    0.0002
##    240        1.1809             nan     0.0010    0.0002
##    260        1.1734             nan     0.0010    0.0001
##    280        1.1659             nan     0.0010    0.0002
##    300        1.1587             nan     0.0010    0.0001
##    320        1.1520             nan     0.0010    0.0001
##    340        1.1454             nan     0.0010    0.0001
##    360        1.1387             nan     0.0010    0.0001
##    380        1.1321             nan     0.0010    0.0002
##    400        1.1259             nan     0.0010    0.0001
##    420        1.1197             nan     0.0010    0.0001
##    440        1.1138             nan     0.0010    0.0001
##    460        1.1080             nan     0.0010    0.0001
##    480        1.1025             nan     0.0010    0.0001
##    500        1.0972             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2547             nan     0.1000    0.0182
##      2        1.2264             nan     0.1000    0.0124
##      3        1.2008             nan     0.1000    0.0118
##      4        1.1806             nan     0.1000    0.0103
##      5        1.1620             nan     0.1000    0.0066
##      6        1.1439             nan     0.1000    0.0075
##      7        1.1264             nan     0.1000    0.0077
##      8        1.1133             nan     0.1000    0.0055
##      9        1.1014             nan     0.1000    0.0042
##     10        1.0897             nan     0.1000    0.0040
##     20        1.0035             nan     0.1000    0.0031
##     40        0.9130             nan     0.1000    0.0007
##     60        0.8755             nan     0.1000   -0.0006
##     80        0.8468             nan     0.1000   -0.0007
##    100        0.8327             nan     0.1000   -0.0016
##    120        0.8198             nan     0.1000   -0.0004
##    140        0.8089             nan     0.1000   -0.0027
##    160        0.8000             nan     0.1000   -0.0012
##    180        0.7909             nan     0.1000   -0.0005
##    200        0.7818             nan     0.1000   -0.0006
##    220        0.7772             nan     0.1000   -0.0014
##    240        0.7703             nan     0.1000   -0.0000
##    260        0.7627             nan     0.1000   -0.0009
##    280        0.7576             nan     0.1000   -0.0008
##    300        0.7512             nan     0.1000   -0.0006
##    320        0.7451             nan     0.1000   -0.0010
##    340        0.7394             nan     0.1000   -0.0011
##    360        0.7334             nan     0.1000   -0.0009
##    380        0.7281             nan     0.1000   -0.0007
##    400        0.7234             nan     0.1000   -0.0001
##    420        0.7175             nan     0.1000   -0.0007
##    440        0.7129             nan     0.1000   -0.0009
##    460        0.7082             nan     0.1000   -0.0007
##    480        0.7040             nan     0.1000   -0.0005
##    500        0.7004             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2580             nan     0.1000    0.0159
##      2        1.2253             nan     0.1000    0.0149
##      3        1.2040             nan     0.1000    0.0078
##      4        1.1829             nan     0.1000    0.0092
##      5        1.1612             nan     0.1000    0.0090
##      6        1.1439             nan     0.1000    0.0070
##      7        1.1294             nan     0.1000    0.0069
##      8        1.1153             nan     0.1000    0.0069
##      9        1.1037             nan     0.1000    0.0051
##     10        1.0903             nan     0.1000    0.0050
##     20        0.9948             nan     0.1000    0.0018
##     40        0.9178             nan     0.1000   -0.0005
##     60        0.8775             nan     0.1000   -0.0010
##     80        0.8538             nan     0.1000   -0.0012
##    100        0.8402             nan     0.1000   -0.0013
##    120        0.8245             nan     0.1000    0.0001
##    140        0.8137             nan     0.1000   -0.0008
##    160        0.8060             nan     0.1000   -0.0003
##    180        0.7973             nan     0.1000   -0.0011
##    200        0.7888             nan     0.1000   -0.0025
##    220        0.7808             nan     0.1000   -0.0007
##    240        0.7750             nan     0.1000   -0.0008
##    260        0.7674             nan     0.1000   -0.0009
##    280        0.7609             nan     0.1000   -0.0010
##    300        0.7551             nan     0.1000   -0.0007
##    320        0.7492             nan     0.1000   -0.0006
##    340        0.7426             nan     0.1000   -0.0008
##    360        0.7367             nan     0.1000   -0.0008
##    380        0.7319             nan     0.1000   -0.0010
##    400        0.7272             nan     0.1000   -0.0007
##    420        0.7222             nan     0.1000   -0.0015
##    440        0.7185             nan     0.1000   -0.0009
##    460        0.7149             nan     0.1000   -0.0006
##    480        0.7105             nan     0.1000   -0.0016
##    500        0.7066             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2506             nan     0.1000    0.0177
##      2        1.2186             nan     0.1000    0.0118
##      3        1.1991             nan     0.1000    0.0074
##      4        1.1770             nan     0.1000    0.0114
##      5        1.1579             nan     0.1000    0.0079
##      6        1.1420             nan     0.1000    0.0075
##      7        1.1223             nan     0.1000    0.0076
##      8        1.1099             nan     0.1000    0.0057
##      9        1.0953             nan     0.1000    0.0054
##     10        1.0826             nan     0.1000    0.0057
##     20        0.9952             nan     0.1000    0.0016
##     40        0.9112             nan     0.1000    0.0003
##     60        0.8740             nan     0.1000    0.0003
##     80        0.8556             nan     0.1000   -0.0003
##    100        0.8361             nan     0.1000   -0.0001
##    120        0.8221             nan     0.1000    0.0005
##    140        0.8089             nan     0.1000   -0.0009
##    160        0.8006             nan     0.1000   -0.0016
##    180        0.7905             nan     0.1000   -0.0018
##    200        0.7840             nan     0.1000   -0.0005
##    220        0.7751             nan     0.1000   -0.0008
##    240        0.7679             nan     0.1000   -0.0008
##    260        0.7613             nan     0.1000    0.0001
##    280        0.7565             nan     0.1000   -0.0006
##    300        0.7476             nan     0.1000   -0.0012
##    320        0.7427             nan     0.1000   -0.0013
##    340        0.7382             nan     0.1000   -0.0008
##    360        0.7322             nan     0.1000   -0.0012
##    380        0.7284             nan     0.1000   -0.0003
##    400        0.7233             nan     0.1000   -0.0010
##    420        0.7214             nan     0.1000   -0.0008
##    440        0.7158             nan     0.1000   -0.0010
##    460        0.7129             nan     0.1000   -0.0004
##    480        0.7061             nan     0.1000   -0.0002
##    500        0.7011             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2421             nan     0.1000    0.0223
##      2        1.2062             nan     0.1000    0.0152
##      3        1.1706             nan     0.1000    0.0142
##      4        1.1384             nan     0.1000    0.0124
##      5        1.1131             nan     0.1000    0.0115
##      6        1.0903             nan     0.1000    0.0093
##      7        1.0700             nan     0.1000    0.0080
##      8        1.0576             nan     0.1000    0.0043
##      9        1.0431             nan     0.1000    0.0058
##     10        1.0263             nan     0.1000    0.0052
##     20        0.9313             nan     0.1000    0.0020
##     40        0.8439             nan     0.1000   -0.0013
##     60        0.8047             nan     0.1000   -0.0016
##     80        0.7774             nan     0.1000   -0.0002
##    100        0.7527             nan     0.1000   -0.0008
##    120        0.7272             nan     0.1000    0.0005
##    140        0.7005             nan     0.1000   -0.0006
##    160        0.6797             nan     0.1000   -0.0008
##    180        0.6593             nan     0.1000   -0.0014
##    200        0.6424             nan     0.1000   -0.0020
##    220        0.6230             nan     0.1000    0.0010
##    240        0.6098             nan     0.1000   -0.0016
##    260        0.5896             nan     0.1000    0.0001
##    280        0.5705             nan     0.1000   -0.0009
##    300        0.5562             nan     0.1000   -0.0005
##    320        0.5430             nan     0.1000   -0.0010
##    340        0.5304             nan     0.1000   -0.0011
##    360        0.5177             nan     0.1000   -0.0013
##    380        0.5065             nan     0.1000   -0.0015
##    400        0.4927             nan     0.1000   -0.0007
##    420        0.4828             nan     0.1000   -0.0009
##    440        0.4721             nan     0.1000   -0.0008
##    460        0.4645             nan     0.1000   -0.0009
##    480        0.4528             nan     0.1000   -0.0006
##    500        0.4418             nan     0.1000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2475             nan     0.1000    0.0208
##      2        1.2082             nan     0.1000    0.0143
##      3        1.1787             nan     0.1000    0.0134
##      4        1.1492             nan     0.1000    0.0115
##      5        1.1239             nan     0.1000    0.0104
##      6        1.1024             nan     0.1000    0.0103
##      7        1.0796             nan     0.1000    0.0085
##      8        1.0628             nan     0.1000    0.0064
##      9        1.0469             nan     0.1000    0.0053
##     10        1.0313             nan     0.1000    0.0061
##     20        0.9315             nan     0.1000    0.0006
##     40        0.8475             nan     0.1000   -0.0001
##     60        0.7973             nan     0.1000   -0.0008
##     80        0.7635             nan     0.1000   -0.0009
##    100        0.7350             nan     0.1000   -0.0019
##    120        0.7147             nan     0.1000   -0.0007
##    140        0.6876             nan     0.1000    0.0001
##    160        0.6634             nan     0.1000   -0.0010
##    180        0.6448             nan     0.1000   -0.0010
##    200        0.6281             nan     0.1000   -0.0014
##    220        0.6113             nan     0.1000   -0.0010
##    240        0.5940             nan     0.1000   -0.0012
##    260        0.5773             nan     0.1000   -0.0011
##    280        0.5609             nan     0.1000   -0.0011
##    300        0.5475             nan     0.1000   -0.0006
##    320        0.5350             nan     0.1000   -0.0008
##    340        0.5234             nan     0.1000   -0.0003
##    360        0.5086             nan     0.1000   -0.0014
##    380        0.4960             nan     0.1000   -0.0009
##    400        0.4837             nan     0.1000   -0.0009
##    420        0.4753             nan     0.1000   -0.0012
##    440        0.4660             nan     0.1000   -0.0010
##    460        0.4546             nan     0.1000   -0.0013
##    480        0.4454             nan     0.1000   -0.0013
##    500        0.4391             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2453             nan     0.1000    0.0213
##      2        1.2037             nan     0.1000    0.0179
##      3        1.1712             nan     0.1000    0.0159
##      4        1.1411             nan     0.1000    0.0108
##      5        1.1147             nan     0.1000    0.0113
##      6        1.0935             nan     0.1000    0.0099
##      7        1.0758             nan     0.1000    0.0054
##      8        1.0610             nan     0.1000    0.0046
##      9        1.0461             nan     0.1000    0.0052
##     10        1.0330             nan     0.1000    0.0044
##     20        0.9333             nan     0.1000    0.0016
##     40        0.8455             nan     0.1000   -0.0005
##     60        0.8018             nan     0.1000   -0.0014
##     80        0.7644             nan     0.1000   -0.0007
##    100        0.7347             nan     0.1000   -0.0003
##    120        0.7092             nan     0.1000   -0.0009
##    140        0.6871             nan     0.1000   -0.0015
##    160        0.6640             nan     0.1000   -0.0009
##    180        0.6460             nan     0.1000   -0.0016
##    200        0.6278             nan     0.1000   -0.0008
##    220        0.6149             nan     0.1000   -0.0024
##    240        0.6000             nan     0.1000   -0.0007
##    260        0.5866             nan     0.1000   -0.0011
##    280        0.5740             nan     0.1000   -0.0013
##    300        0.5586             nan     0.1000   -0.0008
##    320        0.5458             nan     0.1000   -0.0023
##    340        0.5359             nan     0.1000   -0.0011
##    360        0.5254             nan     0.1000   -0.0014
##    380        0.5140             nan     0.1000   -0.0006
##    400        0.5043             nan     0.1000   -0.0013
##    420        0.4914             nan     0.1000   -0.0007
##    440        0.4805             nan     0.1000   -0.0002
##    460        0.4698             nan     0.1000   -0.0011
##    480        0.4597             nan     0.1000   -0.0005
##    500        0.4518             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2329             nan     0.1000    0.0266
##      2        1.1928             nan     0.1000    0.0145
##      3        1.1526             nan     0.1000    0.0169
##      4        1.1197             nan     0.1000    0.0145
##      5        1.0876             nan     0.1000    0.0129
##      6        1.0618             nan     0.1000    0.0080
##      7        1.0387             nan     0.1000    0.0069
##      8        1.0205             nan     0.1000    0.0050
##      9        1.0040             nan     0.1000    0.0045
##     10        0.9882             nan     0.1000    0.0059
##     20        0.8847             nan     0.1000    0.0016
##     40        0.7966             nan     0.1000    0.0002
##     60        0.7446             nan     0.1000   -0.0005
##     80        0.7021             nan     0.1000   -0.0003
##    100        0.6572             nan     0.1000   -0.0018
##    120        0.6272             nan     0.1000   -0.0016
##    140        0.5948             nan     0.1000   -0.0005
##    160        0.5678             nan     0.1000   -0.0010
##    180        0.5382             nan     0.1000   -0.0013
##    200        0.5169             nan     0.1000   -0.0020
##    220        0.4932             nan     0.1000   -0.0000
##    240        0.4707             nan     0.1000   -0.0006
##    260        0.4503             nan     0.1000   -0.0020
##    280        0.4306             nan     0.1000   -0.0007
##    300        0.4122             nan     0.1000   -0.0006
##    320        0.3975             nan     0.1000   -0.0010
##    340        0.3831             nan     0.1000   -0.0005
##    360        0.3703             nan     0.1000   -0.0013
##    380        0.3558             nan     0.1000   -0.0011
##    400        0.3438             nan     0.1000   -0.0005
##    420        0.3308             nan     0.1000   -0.0012
##    440        0.3171             nan     0.1000   -0.0008
##    460        0.3064             nan     0.1000   -0.0007
##    480        0.2965             nan     0.1000   -0.0009
##    500        0.2860             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2392             nan     0.1000    0.0255
##      2        1.1933             nan     0.1000    0.0189
##      3        1.1566             nan     0.1000    0.0173
##      4        1.1231             nan     0.1000    0.0139
##      5        1.0933             nan     0.1000    0.0116
##      6        1.0687             nan     0.1000    0.0085
##      7        1.0451             nan     0.1000    0.0069
##      8        1.0258             nan     0.1000    0.0040
##      9        1.0084             nan     0.1000    0.0058
##     10        0.9922             nan     0.1000    0.0046
##     20        0.8872             nan     0.1000    0.0011
##     40        0.7911             nan     0.1000    0.0001
##     60        0.7350             nan     0.1000   -0.0030
##     80        0.6974             nan     0.1000   -0.0016
##    100        0.6554             nan     0.1000   -0.0018
##    120        0.6245             nan     0.1000   -0.0014
##    140        0.5979             nan     0.1000   -0.0016
##    160        0.5702             nan     0.1000   -0.0018
##    180        0.5394             nan     0.1000   -0.0003
##    200        0.5146             nan     0.1000   -0.0000
##    220        0.4912             nan     0.1000   -0.0004
##    240        0.4704             nan     0.1000   -0.0012
##    260        0.4524             nan     0.1000   -0.0010
##    280        0.4355             nan     0.1000   -0.0012
##    300        0.4188             nan     0.1000   -0.0008
##    320        0.4026             nan     0.1000   -0.0005
##    340        0.3840             nan     0.1000    0.0001
##    360        0.3646             nan     0.1000   -0.0016
##    380        0.3522             nan     0.1000   -0.0007
##    400        0.3406             nan     0.1000   -0.0011
##    420        0.3275             nan     0.1000   -0.0010
##    440        0.3157             nan     0.1000   -0.0002
##    460        0.3040             nan     0.1000   -0.0007
##    480        0.2927             nan     0.1000   -0.0013
##    500        0.2834             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2346             nan     0.1000    0.0284
##      2        1.1926             nan     0.1000    0.0211
##      3        1.1613             nan     0.1000    0.0122
##      4        1.1243             nan     0.1000    0.0174
##      5        1.0955             nan     0.1000    0.0116
##      6        1.0713             nan     0.1000    0.0096
##      7        1.0467             nan     0.1000    0.0094
##      8        1.0274             nan     0.1000    0.0059
##      9        1.0063             nan     0.1000    0.0075
##     10        0.9940             nan     0.1000    0.0045
##     20        0.8901             nan     0.1000    0.0016
##     40        0.7994             nan     0.1000   -0.0004
##     60        0.7333             nan     0.1000   -0.0002
##     80        0.6910             nan     0.1000   -0.0022
##    100        0.6548             nan     0.1000   -0.0005
##    120        0.6222             nan     0.1000   -0.0014
##    140        0.5841             nan     0.1000   -0.0007
##    160        0.5596             nan     0.1000   -0.0018
##    180        0.5337             nan     0.1000   -0.0014
##    200        0.5129             nan     0.1000   -0.0012
##    220        0.4923             nan     0.1000   -0.0005
##    240        0.4705             nan     0.1000   -0.0012
##    260        0.4501             nan     0.1000   -0.0016
##    280        0.4330             nan     0.1000   -0.0010
##    300        0.4167             nan     0.1000   -0.0007
##    320        0.4041             nan     0.1000   -0.0013
##    340        0.3885             nan     0.1000   -0.0010
##    360        0.3730             nan     0.1000   -0.0005
##    380        0.3557             nan     0.1000   -0.0005
##    400        0.3410             nan     0.1000   -0.0011
##    420        0.3294             nan     0.1000   -0.0012
##    440        0.3153             nan     0.1000   -0.0011
##    460        0.3024             nan     0.1000   -0.0002
##    480        0.2912             nan     0.1000   -0.0011
##    500        0.2824             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2259             nan     0.2000    0.0327
##      2        1.1839             nan     0.2000    0.0216
##      3        1.1507             nan     0.2000    0.0149
##      4        1.1216             nan     0.2000    0.0130
##      5        1.0915             nan     0.2000    0.0144
##      6        1.0683             nan     0.2000    0.0070
##      7        1.0526             nan     0.2000    0.0058
##      8        1.0334             nan     0.2000    0.0081
##      9        1.0159             nan     0.2000    0.0028
##     10        1.0063             nan     0.2000    0.0025
##     20        0.9189             nan     0.2000   -0.0003
##     40        0.8570             nan     0.2000   -0.0013
##     60        0.8280             nan     0.2000   -0.0009
##     80        0.8086             nan     0.2000   -0.0024
##    100        0.7876             nan     0.2000   -0.0013
##    120        0.7747             nan     0.2000   -0.0016
##    140        0.7662             nan     0.2000   -0.0010
##    160        0.7545             nan     0.2000   -0.0033
##    180        0.7387             nan     0.2000   -0.0010
##    200        0.7283             nan     0.2000   -0.0043
##    220        0.7175             nan     0.2000   -0.0022
##    240        0.7074             nan     0.2000   -0.0043
##    260        0.7022             nan     0.2000   -0.0013
##    280        0.6972             nan     0.2000   -0.0009
##    300        0.6889             nan     0.2000   -0.0020
##    320        0.6846             nan     0.2000   -0.0024
##    340        0.6786             nan     0.2000   -0.0015
##    360        0.6721             nan     0.2000   -0.0019
##    380        0.6668             nan     0.2000   -0.0015
##    400        0.6602             nan     0.2000   -0.0015
##    420        0.6508             nan     0.2000   -0.0024
##    440        0.6458             nan     0.2000   -0.0028
##    460        0.6439             nan     0.2000   -0.0016
##    480        0.6381             nan     0.2000   -0.0031
##    500        0.6333             nan     0.2000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2248             nan     0.2000    0.0317
##      2        1.1804             nan     0.2000    0.0190
##      3        1.1393             nan     0.2000    0.0159
##      4        1.1043             nan     0.2000    0.0126
##      5        1.0808             nan     0.2000    0.0119
##      6        1.0559             nan     0.2000    0.0101
##      7        1.0381             nan     0.2000    0.0044
##      8        1.0257             nan     0.2000    0.0051
##      9        1.0096             nan     0.2000    0.0036
##     10        0.9983             nan     0.2000    0.0036
##     20        0.9140             nan     0.2000   -0.0005
##     40        0.8509             nan     0.2000   -0.0018
##     60        0.8265             nan     0.2000   -0.0011
##     80        0.8021             nan     0.2000   -0.0023
##    100        0.7879             nan     0.2000   -0.0020
##    120        0.7733             nan     0.2000   -0.0009
##    140        0.7625             nan     0.2000   -0.0021
##    160        0.7516             nan     0.2000   -0.0016
##    180        0.7426             nan     0.2000   -0.0019
##    200        0.7286             nan     0.2000   -0.0007
##    220        0.7166             nan     0.2000   -0.0012
##    240        0.7087             nan     0.2000   -0.0030
##    260        0.7002             nan     0.2000   -0.0016
##    280        0.6937             nan     0.2000   -0.0019
##    300        0.6827             nan     0.2000   -0.0009
##    320        0.6776             nan     0.2000   -0.0034
##    340        0.6717             nan     0.2000   -0.0015
##    360        0.6635             nan     0.2000   -0.0027
##    380        0.6555             nan     0.2000   -0.0040
##    400        0.6483             nan     0.2000   -0.0024
##    420        0.6433             nan     0.2000   -0.0005
##    440        0.6396             nan     0.2000   -0.0013
##    460        0.6340             nan     0.2000   -0.0020
##    480        0.6292             nan     0.2000   -0.0013
##    500        0.6236             nan     0.2000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2158             nan     0.2000    0.0302
##      2        1.1672             nan     0.2000    0.0204
##      3        1.1393             nan     0.2000    0.0125
##      4        1.1095             nan     0.2000    0.0128
##      5        1.0790             nan     0.2000    0.0095
##      6        1.0518             nan     0.2000    0.0098
##      7        1.0305             nan     0.2000    0.0079
##      8        1.0142             nan     0.2000    0.0056
##      9        1.0020             nan     0.2000    0.0024
##     10        0.9907             nan     0.2000    0.0032
##     20        0.9098             nan     0.2000    0.0011
##     40        0.8479             nan     0.2000   -0.0006
##     60        0.8174             nan     0.2000   -0.0018
##     80        0.8013             nan     0.2000   -0.0019
##    100        0.7855             nan     0.2000   -0.0023
##    120        0.7780             nan     0.2000   -0.0015
##    140        0.7605             nan     0.2000   -0.0022
##    160        0.7458             nan     0.2000   -0.0011
##    180        0.7324             nan     0.2000   -0.0005
##    200        0.7182             nan     0.2000   -0.0012
##    220        0.7093             nan     0.2000   -0.0013
##    240        0.7032             nan     0.2000   -0.0029
##    260        0.6927             nan     0.2000   -0.0019
##    280        0.6845             nan     0.2000   -0.0022
##    300        0.6801             nan     0.2000   -0.0036
##    320        0.6720             nan     0.2000   -0.0023
##    340        0.6638             nan     0.2000   -0.0027
##    360        0.6580             nan     0.2000   -0.0019
##    380        0.6522             nan     0.2000   -0.0015
##    400        0.6454             nan     0.2000   -0.0018
##    420        0.6414             nan     0.2000   -0.0020
##    440        0.6332             nan     0.2000   -0.0020
##    460        0.6287             nan     0.2000   -0.0020
##    480        0.6273             nan     0.2000   -0.0011
##    500        0.6200             nan     0.2000   -0.0035
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2101             nan     0.2000    0.0375
##      2        1.1489             nan     0.2000    0.0300
##      3        1.0954             nan     0.2000    0.0250
##      4        1.0574             nan     0.2000    0.0137
##      5        1.0194             nan     0.2000    0.0145
##      6        0.9967             nan     0.2000    0.0103
##      7        0.9709             nan     0.2000    0.0080
##      8        0.9525             nan     0.2000    0.0041
##      9        0.9397             nan     0.2000    0.0030
##     10        0.9291             nan     0.2000    0.0028
##     20        0.8428             nan     0.2000   -0.0007
##     40        0.7707             nan     0.2000   -0.0012
##     60        0.7249             nan     0.2000   -0.0027
##     80        0.6830             nan     0.2000   -0.0029
##    100        0.6416             nan     0.2000   -0.0027
##    120        0.6075             nan     0.2000   -0.0031
##    140        0.5774             nan     0.2000   -0.0024
##    160        0.5467             nan     0.2000   -0.0002
##    180        0.5166             nan     0.2000   -0.0026
##    200        0.4991             nan     0.2000   -0.0028
##    220        0.4788             nan     0.2000   -0.0011
##    240        0.4564             nan     0.2000   -0.0012
##    260        0.4421             nan     0.2000   -0.0003
##    280        0.4243             nan     0.2000   -0.0009
##    300        0.4112             nan     0.2000   -0.0012
##    320        0.3899             nan     0.2000   -0.0042
##    340        0.3758             nan     0.2000   -0.0011
##    360        0.3656             nan     0.2000   -0.0005
##    380        0.3526             nan     0.2000   -0.0015
##    400        0.3415             nan     0.2000   -0.0013
##    420        0.3270             nan     0.2000   -0.0013
##    440        0.3135             nan     0.2000   -0.0023
##    460        0.3034             nan     0.2000   -0.0027
##    480        0.2931             nan     0.2000   -0.0017
##    500        0.2834             nan     0.2000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2014             nan     0.2000    0.0420
##      2        1.1326             nan     0.2000    0.0279
##      3        1.0843             nan     0.2000    0.0194
##      4        1.0540             nan     0.2000    0.0123
##      5        1.0174             nan     0.2000    0.0130
##      6        0.9943             nan     0.2000    0.0082
##      7        0.9769             nan     0.2000    0.0031
##      8        0.9610             nan     0.2000    0.0066
##      9        0.9426             nan     0.2000    0.0076
##     10        0.9280             nan     0.2000    0.0037
##     20        0.8522             nan     0.2000   -0.0000
##     40        0.7831             nan     0.2000    0.0003
##     60        0.7370             nan     0.2000   -0.0007
##     80        0.6959             nan     0.2000   -0.0020
##    100        0.6487             nan     0.2000   -0.0019
##    120        0.6234             nan     0.2000   -0.0032
##    140        0.5898             nan     0.2000   -0.0054
##    160        0.5616             nan     0.2000   -0.0020
##    180        0.5315             nan     0.2000   -0.0007
##    200        0.5140             nan     0.2000   -0.0034
##    220        0.4888             nan     0.2000   -0.0043
##    240        0.4684             nan     0.2000   -0.0041
##    260        0.4412             nan     0.2000   -0.0017
##    280        0.4243             nan     0.2000   -0.0029
##    300        0.4007             nan     0.2000   -0.0023
##    320        0.3854             nan     0.2000   -0.0012
##    340        0.3685             nan     0.2000   -0.0023
##    360        0.3559             nan     0.2000   -0.0011
##    380        0.3417             nan     0.2000   -0.0025
##    400        0.3299             nan     0.2000   -0.0005
##    420        0.3170             nan     0.2000   -0.0014
##    440        0.3046             nan     0.2000   -0.0013
##    460        0.2990             nan     0.2000   -0.0013
##    480        0.2863             nan     0.2000   -0.0009
##    500        0.2743             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2032             nan     0.2000    0.0430
##      2        1.1403             nan     0.2000    0.0317
##      3        1.0918             nan     0.2000    0.0230
##      4        1.0613             nan     0.2000    0.0124
##      5        1.0317             nan     0.2000    0.0118
##      6        0.9997             nan     0.2000    0.0077
##      7        0.9757             nan     0.2000    0.0099
##      8        0.9591             nan     0.2000    0.0025
##      9        0.9366             nan     0.2000    0.0068
##     10        0.9282             nan     0.2000    0.0005
##     20        0.8527             nan     0.2000   -0.0059
##     40        0.7729             nan     0.2000   -0.0029
##     60        0.7293             nan     0.2000   -0.0026
##     80        0.6805             nan     0.2000   -0.0032
##    100        0.6457             nan     0.2000   -0.0018
##    120        0.6108             nan     0.2000   -0.0014
##    140        0.5848             nan     0.2000   -0.0028
##    160        0.5602             nan     0.2000   -0.0014
##    180        0.5383             nan     0.2000   -0.0037
##    200        0.5132             nan     0.2000   -0.0024
##    220        0.4958             nan     0.2000   -0.0018
##    240        0.4739             nan     0.2000   -0.0007
##    260        0.4523             nan     0.2000   -0.0022
##    280        0.4301             nan     0.2000   -0.0012
##    300        0.4131             nan     0.2000   -0.0018
##    320        0.3964             nan     0.2000   -0.0014
##    340        0.3826             nan     0.2000   -0.0005
##    360        0.3677             nan     0.2000   -0.0014
##    380        0.3557             nan     0.2000   -0.0027
##    400        0.3418             nan     0.2000   -0.0007
##    420        0.3298             nan     0.2000   -0.0017
##    440        0.3212             nan     0.2000   -0.0020
##    460        0.3088             nan     0.2000   -0.0028
##    480        0.2983             nan     0.2000   -0.0016
##    500        0.2887             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1782             nan     0.2000    0.0479
##      2        1.1136             nan     0.2000    0.0283
##      3        1.0600             nan     0.2000    0.0167
##      4        1.0213             nan     0.2000    0.0160
##      5        0.9848             nan     0.2000    0.0118
##      6        0.9589             nan     0.2000    0.0078
##      7        0.9395             nan     0.2000    0.0050
##      8        0.9165             nan     0.2000    0.0070
##      9        0.8997             nan     0.2000    0.0061
##     10        0.8829             nan     0.2000    0.0031
##     20        0.7958             nan     0.2000   -0.0065
##     40        0.7275             nan     0.2000   -0.0026
##     60        0.6338             nan     0.2000   -0.0027
##     80        0.5681             nan     0.2000    0.0001
##    100        0.5196             nan     0.2000   -0.0013
##    120        0.4734             nan     0.2000   -0.0014
##    140        0.4356             nan     0.2000   -0.0004
##    160        0.3966             nan     0.2000   -0.0034
##    180        0.3596             nan     0.2000   -0.0010
##    200        0.3392             nan     0.2000   -0.0025
##    220        0.3152             nan     0.2000   -0.0023
##    240        0.2963             nan     0.2000   -0.0033
##    260        0.2723             nan     0.2000   -0.0008
##    280        0.2562             nan     0.2000   -0.0004
##    300        0.2345             nan     0.2000   -0.0014
##    320        0.2206             nan     0.2000   -0.0008
##    340        0.2085             nan     0.2000   -0.0006
##    360        0.1946             nan     0.2000   -0.0009
##    380        0.1822             nan     0.2000   -0.0011
##    400        0.1725             nan     0.2000   -0.0009
##    420        0.1639             nan     0.2000   -0.0011
##    440        0.1564             nan     0.2000   -0.0023
##    460        0.1456             nan     0.2000   -0.0007
##    480        0.1377             nan     0.2000   -0.0003
##    500        0.1305             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1962             nan     0.2000    0.0474
##      2        1.1187             nan     0.2000    0.0310
##      3        1.0663             nan     0.2000    0.0108
##      4        1.0292             nan     0.2000    0.0168
##      5        0.9945             nan     0.2000    0.0134
##      6        0.9642             nan     0.2000    0.0093
##      7        0.9458             nan     0.2000    0.0049
##      8        0.9255             nan     0.2000    0.0010
##      9        0.9077             nan     0.2000    0.0008
##     10        0.8900             nan     0.2000    0.0019
##     20        0.8016             nan     0.2000   -0.0044
##     40        0.7081             nan     0.2000   -0.0025
##     60        0.6448             nan     0.2000   -0.0031
##     80        0.5851             nan     0.2000   -0.0012
##    100        0.5394             nan     0.2000   -0.0020
##    120        0.5033             nan     0.2000   -0.0032
##    140        0.4644             nan     0.2000   -0.0024
##    160        0.4261             nan     0.2000   -0.0017
##    180        0.3862             nan     0.2000   -0.0003
##    200        0.3532             nan     0.2000   -0.0007
##    220        0.3289             nan     0.2000   -0.0010
##    240        0.3086             nan     0.2000   -0.0003
##    260        0.2835             nan     0.2000   -0.0005
##    280        0.2681             nan     0.2000   -0.0026
##    300        0.2513             nan     0.2000   -0.0013
##    320        0.2359             nan     0.2000   -0.0017
##    340        0.2160             nan     0.2000   -0.0006
##    360        0.2033             nan     0.2000   -0.0015
##    380        0.1916             nan     0.2000   -0.0007
##    400        0.1801             nan     0.2000   -0.0004
##    420        0.1691             nan     0.2000   -0.0010
##    440        0.1587             nan     0.2000   -0.0001
##    460        0.1468             nan     0.2000   -0.0008
##    480        0.1373             nan     0.2000   -0.0004
##    500        0.1313             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1869             nan     0.2000    0.0445
##      2        1.1132             nan     0.2000    0.0285
##      3        1.0644             nan     0.2000    0.0171
##      4        1.0195             nan     0.2000    0.0203
##      5        0.9850             nan     0.2000    0.0127
##      6        0.9580             nan     0.2000    0.0037
##      7        0.9354             nan     0.2000    0.0066
##      8        0.9207             nan     0.2000    0.0003
##      9        0.9063             nan     0.2000   -0.0013
##     10        0.8923             nan     0.2000    0.0011
##     20        0.8036             nan     0.2000   -0.0026
##     40        0.7150             nan     0.2000   -0.0042
##     60        0.6376             nan     0.2000   -0.0017
##     80        0.5825             nan     0.2000   -0.0022
##    100        0.5295             nan     0.2000   -0.0018
##    120        0.4794             nan     0.2000   -0.0012
##    140        0.4418             nan     0.2000   -0.0024
##    160        0.4093             nan     0.2000   -0.0034
##    180        0.3816             nan     0.2000   -0.0024
##    200        0.3479             nan     0.2000   -0.0015
##    220        0.3170             nan     0.2000   -0.0034
##    240        0.2946             nan     0.2000   -0.0005
##    260        0.2781             nan     0.2000   -0.0016
##    280        0.2613             nan     0.2000   -0.0025
##    300        0.2426             nan     0.2000   -0.0009
##    320        0.2255             nan     0.2000   -0.0015
##    340        0.2094             nan     0.2000   -0.0017
##    360        0.1981             nan     0.2000   -0.0007
##    380        0.1871             nan     0.2000   -0.0003
##    400        0.1771             nan     0.2000   -0.0012
##    420        0.1668             nan     0.2000   -0.0015
##    440        0.1568             nan     0.2000   -0.0004
##    460        0.1488             nan     0.2000   -0.0006
##    480        0.1382             nan     0.2000   -0.0016
##    500        0.1306             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1938             nan     0.3000    0.0445
##      2        1.1434             nan     0.3000    0.0217
##      3        1.0966             nan     0.3000    0.0151
##      4        1.0644             nan     0.3000    0.0129
##      5        1.0261             nan     0.3000    0.0160
##      6        1.0064             nan     0.3000    0.0058
##      7        0.9882             nan     0.3000    0.0059
##      8        0.9688             nan     0.3000    0.0070
##      9        0.9591             nan     0.3000   -0.0019
##     10        0.9513             nan     0.3000    0.0018
##     20        0.8823             nan     0.3000   -0.0006
##     40        0.8337             nan     0.3000   -0.0066
##     60        0.8123             nan     0.3000   -0.0017
##     80        0.7875             nan     0.3000   -0.0038
##    100        0.7713             nan     0.3000   -0.0030
##    120        0.7553             nan     0.3000   -0.0050
##    140        0.7353             nan     0.3000   -0.0020
##    160        0.7214             nan     0.3000   -0.0026
##    180        0.7080             nan     0.3000   -0.0031
##    200        0.6993             nan     0.3000   -0.0056
##    220        0.6867             nan     0.3000   -0.0039
##    240        0.6823             nan     0.3000   -0.0004
##    260        0.6666             nan     0.3000   -0.0036
##    280        0.6583             nan     0.3000   -0.0018
##    300        0.6500             nan     0.3000   -0.0025
##    320        0.6442             nan     0.3000   -0.0008
##    340        0.6328             nan     0.3000   -0.0038
##    360        0.6246             nan     0.3000   -0.0066
##    380        0.6170             nan     0.3000   -0.0024
##    400        0.6118             nan     0.3000   -0.0019
##    420        0.6054             nan     0.3000   -0.0020
##    440        0.6003             nan     0.3000   -0.0021
##    460        0.5964             nan     0.3000   -0.0015
##    480        0.5849             nan     0.3000   -0.0022
##    500        0.5780             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2117             nan     0.3000    0.0382
##      2        1.1409             nan     0.3000    0.0260
##      3        1.1007             nan     0.3000    0.0176
##      4        1.0598             nan     0.3000    0.0170
##      5        1.0323             nan     0.3000    0.0125
##      6        1.0079             nan     0.3000    0.0100
##      7        0.9820             nan     0.3000    0.0051
##      8        0.9687             nan     0.3000   -0.0010
##      9        0.9535             nan     0.3000    0.0016
##     10        0.9402             nan     0.3000    0.0013
##     20        0.8717             nan     0.3000    0.0009
##     40        0.8323             nan     0.3000   -0.0073
##     60        0.8163             nan     0.3000   -0.0038
##     80        0.7914             nan     0.3000   -0.0030
##    100        0.7730             nan     0.3000   -0.0061
##    120        0.7545             nan     0.3000   -0.0029
##    140        0.7412             nan     0.3000   -0.0009
##    160        0.7299             nan     0.3000   -0.0028
##    180        0.7104             nan     0.3000   -0.0032
##    200        0.7056             nan     0.3000   -0.0060
##    220        0.6940             nan     0.3000   -0.0025
##    240        0.6854             nan     0.3000   -0.0047
##    260        0.6736             nan     0.3000   -0.0027
##    280        0.6680             nan     0.3000   -0.0030
##    300        0.6593             nan     0.3000   -0.0038
##    320        0.6499             nan     0.3000   -0.0045
##    340        0.6388             nan     0.3000   -0.0033
##    360        0.6298             nan     0.3000   -0.0022
##    380        0.6273             nan     0.3000   -0.0018
##    400        0.6193             nan     0.3000   -0.0001
##    420        0.6130             nan     0.3000   -0.0008
##    440        0.6137             nan     0.3000   -0.0099
##    460        0.6051             nan     0.3000   -0.0047
##    480        0.5969             nan     0.3000   -0.0015
##    500        0.5907             nan     0.3000   -0.0038
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1948             nan     0.3000    0.0445
##      2        1.1399             nan     0.3000    0.0237
##      3        1.1048             nan     0.3000    0.0126
##      4        1.0741             nan     0.3000    0.0097
##      5        1.0406             nan     0.3000    0.0128
##      6        1.0159             nan     0.3000    0.0086
##      7        0.9865             nan     0.3000    0.0137
##      8        0.9787             nan     0.3000    0.0001
##      9        0.9637             nan     0.3000    0.0062
##     10        0.9509             nan     0.3000    0.0027
##     20        0.8732             nan     0.3000   -0.0017
##     40        0.8246             nan     0.3000   -0.0018
##     60        0.8021             nan     0.3000   -0.0006
##     80        0.7859             nan     0.3000   -0.0029
##    100        0.7621             nan     0.3000   -0.0016
##    120        0.7436             nan     0.3000   -0.0026
##    140        0.7295             nan     0.3000   -0.0035
##    160        0.7158             nan     0.3000   -0.0022
##    180        0.7057             nan     0.3000   -0.0019
##    200        0.6952             nan     0.3000   -0.0042
##    220        0.6846             nan     0.3000   -0.0013
##    240        0.6743             nan     0.3000   -0.0019
##    260        0.6657             nan     0.3000   -0.0030
##    280        0.6564             nan     0.3000   -0.0012
##    300        0.6481             nan     0.3000   -0.0001
##    320        0.6394             nan     0.3000   -0.0009
##    340        0.6278             nan     0.3000   -0.0012
##    360        0.6198             nan     0.3000   -0.0018
##    380        0.6143             nan     0.3000   -0.0050
##    400        0.6076             nan     0.3000   -0.0015
##    420        0.6024             nan     0.3000   -0.0014
##    440        0.5981             nan     0.3000   -0.0017
##    460        0.5948             nan     0.3000   -0.0052
##    480        0.5823             nan     0.3000   -0.0006
##    500        0.5771             nan     0.3000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1654             nan     0.3000    0.0540
##      2        1.0882             nan     0.3000    0.0327
##      3        1.0377             nan     0.3000    0.0226
##      4        0.9995             nan     0.3000    0.0119
##      5        0.9666             nan     0.3000    0.0151
##      6        0.9463             nan     0.3000    0.0019
##      7        0.9265             nan     0.3000    0.0041
##      8        0.9120             nan     0.3000    0.0002
##      9        0.9059             nan     0.3000   -0.0089
##     10        0.8984             nan     0.3000   -0.0010
##     20        0.8159             nan     0.3000   -0.0033
##     40        0.7303             nan     0.3000   -0.0030
##     60        0.6693             nan     0.3000   -0.0044
##     80        0.6194             nan     0.3000   -0.0025
##    100        0.5767             nan     0.3000   -0.0003
##    120        0.5395             nan     0.3000   -0.0018
##    140        0.5124             nan     0.3000   -0.0078
##    160        0.4776             nan     0.3000    0.0001
##    180        0.4560             nan     0.3000   -0.0028
##    200        0.4222             nan     0.3000   -0.0020
##    220        0.3914             nan     0.3000   -0.0004
##    240        0.3708             nan     0.3000   -0.0070
##    260        0.3479             nan     0.3000   -0.0023
##    280        0.3201             nan     0.3000   -0.0003
##    300        0.3035             nan     0.3000   -0.0023
##    320        0.2851             nan     0.3000   -0.0001
##    340        0.2737             nan     0.3000   -0.0036
##    360        0.2610             nan     0.3000   -0.0019
##    380        0.2445             nan     0.3000   -0.0007
##    400        0.2316             nan     0.3000   -0.0007
##    420        0.2217             nan     0.3000   -0.0022
##    440        0.2097             nan     0.3000   -0.0006
##    460        0.2004             nan     0.3000   -0.0007
##    480        0.1963             nan     0.3000   -0.0009
##    500        0.1860             nan     0.3000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1546             nan     0.3000    0.0611
##      2        1.0836             nan     0.3000    0.0296
##      3        1.0251             nan     0.3000    0.0204
##      4        0.9940             nan     0.3000    0.0092
##      5        0.9704             nan     0.3000    0.0074
##      6        0.9485             nan     0.3000    0.0011
##      7        0.9312             nan     0.3000    0.0008
##      8        0.9089             nan     0.3000    0.0078
##      9        0.8997             nan     0.3000   -0.0025
##     10        0.8871             nan     0.3000    0.0025
##     20        0.8125             nan     0.3000   -0.0038
##     40        0.7357             nan     0.3000   -0.0016
##     60        0.6873             nan     0.3000   -0.0043
##     80        0.6371             nan     0.3000   -0.0044
##    100        0.5992             nan     0.3000   -0.0038
##    120        0.5602             nan     0.3000   -0.0074
##    140        0.5261             nan     0.3000   -0.0035
##    160        0.4924             nan     0.3000   -0.0030
##    180        0.4631             nan     0.3000   -0.0030
##    200        0.4357             nan     0.3000   -0.0020
##    220        0.4054             nan     0.3000   -0.0027
##    240        0.3768             nan     0.3000   -0.0019
##    260        0.3597             nan     0.3000   -0.0044
##    280        0.3418             nan     0.3000   -0.0022
##    300        0.3256             nan     0.3000   -0.0032
##    320        0.3095             nan     0.3000   -0.0016
##    340        0.2937             nan     0.3000   -0.0014
##    360        0.2783             nan     0.3000   -0.0023
##    380        0.2632             nan     0.3000   -0.0022
##    400        0.2475             nan     0.3000   -0.0018
##    420        0.2322             nan     0.3000   -0.0001
##    440        0.2173             nan     0.3000   -0.0011
##    460        0.2062             nan     0.3000   -0.0021
##    480        0.1966             nan     0.3000   -0.0002
##    500        0.1839             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1902             nan     0.3000    0.0426
##      2        1.1005             nan     0.3000    0.0359
##      3        1.0525             nan     0.3000    0.0122
##      4        1.0093             nan     0.3000    0.0176
##      5        0.9817             nan     0.3000    0.0037
##      6        0.9561             nan     0.3000    0.0079
##      7        0.9305             nan     0.3000    0.0120
##      8        0.9191             nan     0.3000   -0.0024
##      9        0.8998             nan     0.3000    0.0054
##     10        0.8902             nan     0.3000    0.0013
##     20        0.8142             nan     0.3000   -0.0050
##     40        0.7431             nan     0.3000   -0.0026
##     60        0.6758             nan     0.3000   -0.0023
##     80        0.6160             nan     0.3000   -0.0012
##    100        0.5831             nan     0.3000   -0.0044
##    120        0.5303             nan     0.3000   -0.0028
##    140        0.5063             nan     0.3000   -0.0040
##    160        0.4783             nan     0.3000   -0.0017
##    180        0.4456             nan     0.3000   -0.0024
##    200        0.4260             nan     0.3000   -0.0047
##    220        0.3900             nan     0.3000   -0.0012
##    240        0.3626             nan     0.3000   -0.0037
##    260        0.3401             nan     0.3000   -0.0022
##    280        0.3251             nan     0.3000   -0.0014
##    300        0.3096             nan     0.3000   -0.0028
##    320        0.2895             nan     0.3000   -0.0012
##    340        0.2731             nan     0.3000   -0.0028
##    360        0.2583             nan     0.3000   -0.0023
##    380        0.2487             nan     0.3000   -0.0041
##    400        0.2347             nan     0.3000   -0.0009
##    420        0.2241             nan     0.3000   -0.0025
##    440        0.2133             nan     0.3000   -0.0007
##    460        0.2054             nan     0.3000   -0.0017
##    480        0.1952             nan     0.3000   -0.0003
##    500        0.1876             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1462             nan     0.3000    0.0636
##      2        1.0671             nan     0.3000    0.0319
##      3        1.0143             nan     0.3000    0.0169
##      4        0.9766             nan     0.3000    0.0099
##      5        0.9425             nan     0.3000    0.0104
##      6        0.9194             nan     0.3000   -0.0028
##      7        0.8989             nan     0.3000    0.0011
##      8        0.8872             nan     0.3000   -0.0061
##      9        0.8681             nan     0.3000   -0.0013
##     10        0.8534             nan     0.3000   -0.0024
##     20        0.7738             nan     0.3000   -0.0058
##     40        0.6842             nan     0.3000   -0.0042
##     60        0.5655             nan     0.3000   -0.0069
##     80        0.4840             nan     0.3000   -0.0026
##    100        0.4161             nan     0.3000   -0.0022
##    120        0.3731             nan     0.3000   -0.0022
##    140        0.3281             nan     0.3000   -0.0011
##    160        0.2949             nan     0.3000   -0.0037
##    180        0.2621             nan     0.3000   -0.0021
##    200        0.2357             nan     0.3000   -0.0019
##    220        0.2063             nan     0.3000   -0.0008
##    240        0.1820             nan     0.3000   -0.0016
##    260        0.1645             nan     0.3000   -0.0019
##    280        0.1523             nan     0.3000   -0.0008
##    300        0.1406             nan     0.3000   -0.0017
##    320        0.1284             nan     0.3000   -0.0010
##    340        0.1195             nan     0.3000   -0.0019
##    360        0.1111             nan     0.3000   -0.0006
##    380        0.1020             nan     0.3000   -0.0001
##    400        0.0920             nan     0.3000   -0.0002
##    420        0.0854             nan     0.3000   -0.0007
##    440        0.0804             nan     0.3000   -0.0006
##    460        0.0737             nan     0.3000   -0.0004
##    480        0.0684             nan     0.3000   -0.0002
##    500        0.0629             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1600             nan     0.3000    0.0658
##      2        1.0646             nan     0.3000    0.0409
##      3        1.0160             nan     0.3000    0.0103
##      4        0.9747             nan     0.3000    0.0156
##      5        0.9332             nan     0.3000    0.0107
##      6        0.9018             nan     0.3000    0.0059
##      7        0.8791             nan     0.3000    0.0058
##      8        0.8677             nan     0.3000   -0.0052
##      9        0.8515             nan     0.3000   -0.0019
##     10        0.8349             nan     0.3000   -0.0003
##     20        0.7542             nan     0.3000   -0.0064
##     40        0.6344             nan     0.3000   -0.0064
##     60        0.5638             nan     0.3000   -0.0012
##     80        0.4905             nan     0.3000   -0.0085
##    100        0.4374             nan     0.3000   -0.0006
##    120        0.3859             nan     0.3000   -0.0042
##    140        0.3445             nan     0.3000   -0.0026
##    160        0.3076             nan     0.3000   -0.0025
##    180        0.2829             nan     0.3000   -0.0015
##    200        0.2483             nan     0.3000   -0.0000
##    220        0.2237             nan     0.3000   -0.0021
##    240        0.2072             nan     0.3000   -0.0025
##    260        0.1915             nan     0.3000   -0.0011
##    280        0.1770             nan     0.3000   -0.0011
##    300        0.1563             nan     0.3000   -0.0010
##    320        0.1445             nan     0.3000   -0.0013
##    340        0.1299             nan     0.3000   -0.0005
##    360        0.1190             nan     0.3000   -0.0004
##    380        0.1062             nan     0.3000   -0.0005
##    400        0.0971             nan     0.3000   -0.0002
##    420        0.0886             nan     0.3000   -0.0005
##    440        0.0825             nan     0.3000   -0.0011
##    460        0.0762             nan     0.3000   -0.0005
##    480        0.0686             nan     0.3000   -0.0008
##    500        0.0631             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1662             nan     0.3000    0.0509
##      2        1.0914             nan     0.3000    0.0305
##      3        1.0282             nan     0.3000    0.0207
##      4        0.9831             nan     0.3000    0.0152
##      5        0.9499             nan     0.3000    0.0104
##      6        0.9291             nan     0.3000   -0.0029
##      7        0.9082             nan     0.3000    0.0032
##      8        0.8950             nan     0.3000   -0.0017
##      9        0.8877             nan     0.3000   -0.0084
##     10        0.8726             nan     0.3000   -0.0041
##     20        0.7765             nan     0.3000   -0.0051
##     40        0.6636             nan     0.3000   -0.0132
##     60        0.5813             nan     0.3000   -0.0003
##     80        0.5012             nan     0.3000   -0.0017
##    100        0.4428             nan     0.3000   -0.0019
##    120        0.4011             nan     0.3000   -0.0021
##    140        0.3671             nan     0.3000   -0.0019
##    160        0.3309             nan     0.3000   -0.0020
##    180        0.2875             nan     0.3000   -0.0002
##    200        0.2552             nan     0.3000   -0.0004
##    220        0.2204             nan     0.3000   -0.0023
##    240        0.2013             nan     0.3000   -0.0014
##    260        0.1820             nan     0.3000   -0.0023
##    280        0.1641             nan     0.3000   -0.0015
##    300        0.1514             nan     0.3000   -0.0014
##    320        0.1377             nan     0.3000   -0.0001
##    340        0.1262             nan     0.3000   -0.0003
##    360        0.1170             nan     0.3000   -0.0019
##    380        0.1058             nan     0.3000   -0.0005
##    400        0.0955             nan     0.3000   -0.0008
##    420        0.0876             nan     0.3000   -0.0002
##    440        0.0808             nan     0.3000   -0.0011
##    460        0.0736             nan     0.3000   -0.0003
##    480        0.0675             nan     0.3000   -0.0003
##    500        0.0627             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1495             nan     0.5000    0.0649
##      2        1.0875             nan     0.5000    0.0263
##      3        1.0420             nan     0.5000    0.0203
##      4        0.9980             nan     0.5000    0.0143
##      5        0.9799             nan     0.5000    0.0016
##      6        0.9725             nan     0.5000   -0.0056
##      7        0.9555             nan     0.5000    0.0052
##      8        0.9455             nan     0.5000   -0.0053
##      9        0.9289             nan     0.5000    0.0051
##     10        0.9133             nan     0.5000    0.0036
##     20        0.8578             nan     0.5000    0.0011
##     40        0.7879             nan     0.5000   -0.0053
##     60        0.7552             nan     0.5000   -0.0087
##     80        0.7269             nan     0.5000   -0.0038
##    100        0.7065             nan     0.5000   -0.0016
##    120        0.6870             nan     0.5000   -0.0069
##    140        0.6795             nan     0.5000   -0.0015
##    160        0.6616             nan     0.5000   -0.0047
##    180        0.6445             nan     0.5000   -0.0019
##    200        0.6299             nan     0.5000   -0.0058
##    220        0.6167             nan     0.5000   -0.0012
##    240        0.6046             nan     0.5000   -0.0049
##    260        0.6024             nan     0.5000   -0.0079
##    280        0.5921             nan     0.5000   -0.0048
##    300        0.5830             nan     0.5000   -0.0140
##    320        0.5702             nan     0.5000   -0.0025
##    340        0.5612             nan     0.5000   -0.0035
##    360        0.5606             nan     0.5000   -0.0109
##    380        0.5514             nan     0.5000   -0.0101
##    400        0.5405             nan     0.5000   -0.0028
##    420        0.5327             nan     0.5000   -0.0045
##    440        0.5454             nan     0.5000   -0.0198
##    460        0.5204             nan     0.5000   -0.0013
##    480        0.5116             nan     0.5000   -0.0022
##    500        0.5025             nan     0.5000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1401             nan     0.5000    0.0565
##      2        1.0762             nan     0.5000    0.0267
##      3        1.0214             nan     0.5000    0.0187
##      4        0.9764             nan     0.5000    0.0157
##      5        0.9655             nan     0.5000    0.0003
##      6        0.9468             nan     0.5000    0.0043
##      7        0.9320             nan     0.5000    0.0044
##      8        0.9104             nan     0.5000    0.0022
##      9        0.9139             nan     0.5000   -0.0113
##     10        0.9095             nan     0.5000   -0.0049
##     20        0.8467             nan     0.5000   -0.0046
##     40        0.7873             nan     0.5000   -0.0049
##     60        0.7627             nan     0.5000   -0.0046
##     80        0.7472             nan     0.5000   -0.0044
##    100        0.7340             nan     0.5000   -0.0031
##    120        0.7095             nan     0.5000   -0.0034
##    140        0.6966             nan     0.5000   -0.0046
##    160        0.6748             nan     0.5000   -0.0056
##    180        0.6629             nan     0.5000   -0.0082
##    200        0.6443             nan     0.5000   -0.0040
##    220        0.6271             nan     0.5000   -0.0045
##    240        0.6286             nan     0.5000   -0.0058
##    260        0.6078             nan     0.5000   -0.0065
##    280        0.6002             nan     0.5000   -0.0036
##    300        0.5846             nan     0.5000   -0.0022
##    320        0.5727             nan     0.5000   -0.0051
##    340        0.5637             nan     0.5000   -0.0039
##    360        0.5534             nan     0.5000   -0.0055
##    380        0.5427             nan     0.5000    0.0007
##    400        0.5416             nan     0.5000   -0.0028
##    420        0.5241             nan     0.5000   -0.0029
##    440        0.5201             nan     0.5000   -0.0020
##    460        0.5233             nan     0.5000   -0.0048
##    480        0.5124             nan     0.5000   -0.0005
##    500        0.5026             nan     0.5000   -0.0058
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1579             nan     0.5000    0.0642
##      2        1.0900             nan     0.5000    0.0260
##      3        1.0309             nan     0.5000    0.0196
##      4        1.0028             nan     0.5000    0.0126
##      5        0.9837             nan     0.5000    0.0011
##      6        0.9725             nan     0.5000    0.0021
##      7        0.9521             nan     0.5000    0.0022
##      8        0.9419             nan     0.5000   -0.0016
##      9        0.9363             nan     0.5000   -0.0048
##     10        0.9225             nan     0.5000    0.0015
##     20        0.8767             nan     0.5000    0.0001
##     40        0.8337             nan     0.5000   -0.0023
##     60        0.7960             nan     0.5000   -0.0042
##     80        0.7708             nan     0.5000    0.0005
##    100        0.7391             nan     0.5000   -0.0076
##    120        0.7223             nan     0.5000   -0.0028
##    140        0.7108             nan     0.5000   -0.0035
##    160        0.6948             nan     0.5000   -0.0137
##    180        0.6762             nan     0.5000   -0.0096
##    200        0.6418             nan     0.5000   -0.0018
##    220        0.6386             nan     0.5000   -0.0038
##    240        0.6288             nan     0.5000   -0.0090
##    260        0.6156             nan     0.5000   -0.0039
##    280        0.6018             nan     0.5000   -0.0039
##    300        0.5976             nan     0.5000   -0.0121
##    320        0.5902             nan     0.5000   -0.0113
##    340        0.5751             nan     0.5000   -0.0085
##    360        0.5621             nan     0.5000   -0.0055
##    380        0.5536             nan     0.5000   -0.0040
##    400        0.5463             nan     0.5000   -0.0103
##    420        0.5335             nan     0.5000   -0.0013
##    440        0.5277             nan     0.5000   -0.0053
##    460        0.5120             nan     0.5000   -0.0015
##    480        0.5115             nan     0.5000   -0.0116
##    500        0.4916             nan     0.5000   -0.0051
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1259             nan     0.5000    0.0789
##      2        1.0393             nan     0.5000    0.0374
##      3        0.9861             nan     0.5000    0.0218
##      4        0.9363             nan     0.5000    0.0167
##      5        0.9175             nan     0.5000   -0.0122
##      6        0.8911             nan     0.5000   -0.0016
##      7        0.8789             nan     0.5000   -0.0018
##      8        0.8732             nan     0.5000   -0.0103
##      9        0.8653             nan     0.5000   -0.0153
##     10        0.8524             nan     0.5000   -0.0002
##     20        0.7874             nan     0.5000   -0.0038
##     40        0.7052             nan     0.5000   -0.0132
##     60        0.6022             nan     0.5000   -0.0114
##     80        0.6109             nan     0.5000   -0.0006
##    100        0.5789             nan     0.5000   -0.0460
##    120        0.4534             nan     0.5000   -0.0085
##    140        0.4035             nan     0.5000   -0.0023
##    160        0.3442             nan     0.5000   -0.0040
##    180        0.3067             nan     0.5000   -0.0034
##    200        0.2694             nan     0.5000   -0.0038
##    220        0.2327             nan     0.5000   -0.0011
##    240        0.2215             nan     0.5000   -0.0057
##    260        0.1898             nan     0.5000   -0.0004
##    280        0.1631             nan     0.5000   -0.0014
##    300        0.1450             nan     0.5000   -0.0007
##    320        0.1261             nan     0.5000   -0.0010
##    340        0.1180             nan     0.5000   -0.0012
##    360        0.1066             nan     0.5000   -0.0009
##    380        0.0981             nan     0.5000   -0.0010
##    400        0.0906             nan     0.5000   -0.0007
##    420        0.0856             nan     0.5000   -0.0016
##    440        0.0798             nan     0.5000   -0.0009
##    460        0.0754             nan     0.5000    0.0001
##    480        0.0717             nan     0.5000   -0.0011
##    500        0.0644             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1294             nan     0.5000    0.0783
##      2        1.0382             nan     0.5000    0.0438
##      3        0.9760             nan     0.5000    0.0277
##      4        0.9521             nan     0.5000   -0.0050
##      5        0.9201             nan     0.5000    0.0035
##      6        0.8932             nan     0.5000    0.0054
##      7        0.8699             nan     0.5000    0.0035
##      8        0.8568             nan     0.5000    0.0019
##      9        0.8404             nan     0.5000   -0.0060
##     10        0.8291             nan     0.5000   -0.0092
##     20        0.7749             nan     0.5000   -0.0148
##     40        0.6899             nan     0.5000    0.0009
##     60        0.6123             nan     0.5000    0.0012
##     80        0.5464             nan     0.5000   -0.0049
##    100        0.4719             nan     0.5000   -0.0037
##    120        0.4447             nan     0.5000   -0.0164
##    140        0.4059             nan     0.5000   -0.0062
##    160        0.3713             nan     0.5000   -0.0013
##    180        0.3277             nan     0.5000   -0.0095
##    200        0.2854             nan     0.5000   -0.0016
##    220        0.2586             nan     0.5000   -0.0029
##    240        0.2424             nan     0.5000   -0.0049
##    260        0.2178             nan     0.5000   -0.0011
##    280        0.1881             nan     0.5000   -0.0023
##    300        0.1619             nan     0.5000   -0.0038
##    320        0.1501             nan     0.5000   -0.0019
##    340        0.1362             nan     0.5000   -0.0029
##    360        0.1281             nan     0.5000   -0.0008
##    380        0.1168             nan     0.5000   -0.0009
##    400        0.1087             nan     0.5000   -0.0011
##    420        0.1013             nan     0.5000   -0.0002
##    440        0.0920             nan     0.5000   -0.0014
##    460        0.0860             nan     0.5000   -0.0008
##    480        0.0804             nan     0.5000   -0.0006
##    500        0.0780             nan     0.5000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1086             nan     0.5000    0.0875
##      2        1.0377             nan     0.5000    0.0214
##      3        0.9617             nan     0.5000    0.0316
##      4        0.9233             nan     0.5000    0.0108
##      5        0.9022             nan     0.5000   -0.0016
##      6        0.8849             nan     0.5000   -0.0024
##      7        0.8716             nan     0.5000   -0.0029
##      8        0.8608             nan     0.5000   -0.0032
##      9        0.8477             nan     0.5000    0.0015
##     10        0.8440             nan     0.5000   -0.0096
##     20        0.7874             nan     0.5000   -0.0096
##     40        0.7081             nan     0.5000   -0.0121
##     60        0.6314             nan     0.5000   -0.0109
##     80        0.5548             nan     0.5000   -0.0038
##    100        0.5263             nan     0.5000   -0.0038
##    120        0.4902             nan     0.5000   -0.0033
##    140        0.4407             nan     0.5000   -0.0053
##    160        0.3897             nan     0.5000   -0.0062
##    180        0.3525             nan     0.5000   -0.0045
##    200        0.3285             nan     0.5000   -0.0023
##    220        0.3077             nan     0.5000   -0.0053
##    240        0.2773             nan     0.5000   -0.0062
##    260        0.2519             nan     0.5000   -0.0022
##    280        0.2237             nan     0.5000   -0.0035
##    300        0.2007             nan     0.5000   -0.0014
##    320        0.1884             nan     0.5000   -0.0042
##    340        0.1767             nan     0.5000   -0.0034
##    360        0.1624             nan     0.5000   -0.0015
##    380        0.1500             nan     0.5000   -0.0031
##    400        0.1335             nan     0.5000   -0.0005
##    420        0.1221             nan     0.5000   -0.0033
##    440        0.1149             nan     0.5000   -0.0021
##    460        0.1078             nan     0.5000   -0.0014
##    480        0.0988             nan     0.5000   -0.0021
##    500        0.0941             nan     0.5000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0828             nan     0.5000    0.1073
##      2        1.0048             nan     0.5000    0.0037
##      3        0.9465             nan     0.5000    0.0048
##      4        0.9207             nan     0.5000   -0.0110
##      5        0.9075             nan     0.5000   -0.0164
##      6        0.8876             nan     0.5000   -0.0057
##      7        0.8836             nan     0.5000   -0.0222
##      8        0.8517             nan     0.5000   -0.0024
##      9        0.8820             nan     0.5000   -0.0511
##     10        0.8554             nan     0.5000    0.0048
##     20        0.7915             nan     0.5000   -0.0137
##     40        0.6373             nan     0.5000    0.0027
##     60        0.5351             nan     0.5000   -0.0058
##     80        0.7450             nan     0.5000   -0.0112
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0870             nan     0.5000    0.0947
##      2        0.9938             nan     0.5000    0.0299
##      3        0.9479             nan     0.5000    0.0153
##      4        0.9094             nan     0.5000    0.0017
##      5        0.9019             nan     0.5000   -0.0137
##      6        0.8774             nan     0.5000   -0.0055
##      7        0.8618             nan     0.5000   -0.0075
##      8        0.8520             nan     0.5000   -0.0115
##      9        0.8349             nan     0.5000   -0.0069
##     10        0.8248             nan     0.5000   -0.0065
##     20        0.7488             nan     0.5000   -0.0147
##     40        0.5872             nan     0.5000   -0.0003
##     60        0.4806             nan     0.5000   -0.0151
##     80        0.3911             nan     0.5000   -0.0059
##    100        0.3049             nan     0.5000   -0.0072
##    120        0.2502             nan     0.5000   -0.0023
##    140        0.2044             nan     0.5000    0.0001
##    160        0.1759             nan     0.5000   -0.0020
##    180        0.1525             nan     0.5000   -0.0026
##    200        0.1289             nan     0.5000   -0.0023
##    220        0.1104             nan     0.5000   -0.0011
##    240        0.0976             nan     0.5000   -0.0012
##    260        0.0856             nan     0.5000   -0.0011
##    280        0.0745             nan     0.5000   -0.0003
##    300        0.0649             nan     0.5000   -0.0006
##    320        0.0585             nan     0.5000   -0.0019
##    340        0.0499             nan     0.5000   -0.0007
##    360        0.0442             nan     0.5000   -0.0008
##    380        0.0374             nan     0.5000   -0.0003
##    400        0.0337             nan     0.5000   -0.0006
##    420        0.0300             nan     0.5000   -0.0001
##    440        0.0268             nan     0.5000   -0.0005
##    460        0.0244             nan     0.5000   -0.0007
##    480        0.0214             nan     0.5000    0.0002
##    500        0.0192             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0951             nan     0.5000    0.0876
##      2        1.0055             nan     0.5000    0.0299
##      3        0.9596             nan     0.5000   -0.0005
##      4        0.9137             nan     0.5000    0.0221
##      5        0.8873             nan     0.5000    0.0045
##      6        0.8766             nan     0.5000   -0.0120
##      7        0.8543             nan     0.5000   -0.0019
##      8        0.8218             nan     0.5000   -0.0007
##      9        0.8138             nan     0.5000   -0.0121
##     10        0.8031             nan     0.5000   -0.0124
##     20        0.7179             nan     0.5000   -0.0040
##     40        0.6052             nan     0.5000   -0.0148
##     60        0.4815             nan     0.5000   -0.0128
##     80        0.4005             nan     0.5000   -0.0038
##    100        0.3291             nan     0.5000   -0.0086
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000   -0.0010
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000   -0.0006
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1052             nan     1.0000    0.0915
##      2        1.0293             nan     1.0000    0.0097
##      3        0.9846             nan     1.0000    0.0166
##      4        0.9658             nan     1.0000   -0.0016
##      5        0.9719             nan     1.0000   -0.0230
##      6        0.9619             nan     1.0000   -0.0153
##      7        0.9259             nan     1.0000    0.0082
##      8        0.9378             nan     1.0000   -0.0250
##      9        1.0770             nan     1.0000   -0.1611
##     10     1600.9974             nan     1.0000    0.0401
##     20     1600.9939             nan     1.0000   -0.0075
##     40   204070.0254             nan     1.0000   -0.0012
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000   -0.0015
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000    0.0016
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000   -0.0022
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1169             nan     1.0000    0.0596
##      2        1.0384             nan     1.0000    0.0348
##      3        0.9964             nan     1.0000    0.0114
##      4        0.9862             nan     1.0000   -0.0030
##      5        0.9792             nan     1.0000   -0.0116
##      6        0.9736             nan     1.0000   -0.0152
##      7        0.9650             nan     1.0000   -0.0182
##      8        0.9601             nan     1.0000   -0.0057
##      9        0.9300             nan     1.0000    0.0060
##     10        0.9181             nan     1.0000   -0.0003
##     20        0.8895             nan     1.0000   -0.0052
##     40        0.8366             nan     1.0000   -0.0158
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1091             nan     1.0000    0.0818
##      2        1.0335             nan     1.0000    0.0373
##      3        1.0102             nan     1.0000   -0.0093
##      4        0.9735             nan     1.0000    0.0126
##      5        0.9718             nan     1.0000   -0.0133
##      6        0.9553             nan     1.0000   -0.0019
##      7        0.9498             nan     1.0000   -0.0175
##      8        0.9368             nan     1.0000   -0.0134
##      9        0.9347             nan     1.0000   -0.0055
##     10        0.9413             nan     1.0000   -0.0209
##     20        0.9194             nan     1.0000   -0.0185
##     40           inf             nan     1.0000       nan
##     60 46073074850.7146             nan     1.0000   -0.4585
##     80 46073074851.7939             nan     1.0000   -0.0034
##    100 46073080803.2583             nan     1.0000 -145.3566
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0550             nan     1.0000    0.1031
##      2        0.9934             nan     1.0000    0.0220
##      3        0.9770             nan     1.0000   -0.0213
##      4        0.9603             nan     1.0000   -0.0184
##      5        0.9533             nan     1.0000   -0.0358
##      6        0.9465             nan     1.0000   -0.0199
##      7        1.0447             nan     1.0000   -0.1426
##      8        0.9964             nan     1.0000    0.0143
##      9        0.9773             nan     1.0000   -0.0054
##     10        0.9718             nan     1.0000   -0.0233
##     20        1.0984             nan     1.0000   -0.1315
##     40  6546938.5132             nan     1.0000    0.0033
##     60  6546938.0698             nan     1.0000   -0.0026
##     80  6549694.2249             nan     1.0000   -0.2204
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0601             nan     1.0000    0.1224
##      2        0.9886             nan     1.0000    0.0221
##      3        0.9339             nan     1.0000    0.0197
##      4        0.9083             nan     1.0000   -0.0087
##      5        0.9177             nan     1.0000   -0.0291
##      6        0.9270             nan     1.0000   -0.0284
##      7        0.9009             nan     1.0000   -0.0000
##      8        0.8565             nan     1.0000    0.0117
##      9        0.8708             nan     1.0000   -0.0392
##     10        0.8881             nan     1.0000   -0.0355
##     20        0.8126             nan     1.0000   -0.0501
##     40 914738289765.2891             nan     1.0000   -0.0137
##     60 914738289765.1559             nan     1.0000   -0.0161
##     80 914738289765.1772             nan     1.0000   -0.0098
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0606             nan     1.0000    0.0904
##      2        0.9622             nan     1.0000    0.0435
##      3        0.9412             nan     1.0000   -0.0142
##      4        0.9173             nan     1.0000   -0.0021
##      5        0.9025             nan     1.0000   -0.0067
##      6        0.9058             nan     1.0000   -0.0261
##      7        0.8888             nan     1.0000   -0.0153
##      8        0.9150             nan     1.0000   -0.0470
##      9        0.9267             nan     1.0000   -0.0365
##     10        0.8939             nan     1.0000   -0.0103
##     20        0.9896             nan     1.0000   -0.0726
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        0.9921             nan     1.0000    0.1450
##      2        0.9267             nan     1.0000    0.0206
##      3        0.9218             nan     1.0000   -0.0479
##      4        0.9224             nan     1.0000   -0.0522
##      5        0.9008             nan     1.0000   -0.0258
##      6        0.9223             nan     1.0000   -0.0618
##      7        0.9334             nan     1.0000   -0.0671
##      8        0.9207             nan     1.0000   -0.0325
##      9        0.8915             nan     1.0000   -0.0188
##     10        0.8970             nan     1.0000   -0.0555
##     20        2.3337             nan     1.0000   -0.0284
##     40        4.3426             nan     1.0000   -0.0052
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0306             nan     1.0000    0.1127
##      2        0.9492             nan     1.0000    0.0042
##      3        0.9292             nan     1.0000   -0.0376
##      4        0.9185             nan     1.0000   -0.0399
##      5        0.9414             nan     1.0000   -0.0671
##      6        0.9513             nan     1.0000   -0.0555
##      7        0.9515             nan     1.0000   -0.0307
##      8        0.9443             nan     1.0000   -0.0387
##      9        0.9119             nan     1.0000   -0.0128
##     10        0.9021             nan     1.0000   -0.0225
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0219             nan     1.0000    0.1064
##      2        0.9360             nan     1.0000    0.0274
##      3        0.9362             nan     1.0000   -0.0443
##      4        0.9026             nan     1.0000   -0.0101
##      5        0.8893             nan     1.0000   -0.0163
##      6        0.8932             nan     1.0000   -0.0272
##      7        0.8879             nan     1.0000   -0.0245
##      8        0.8995             nan     1.0000   -0.0517
##      9        0.8728             nan     1.0000   -0.0229
##     10        0.8635             nan     1.0000   -0.0411
##     20           inf             nan     1.0000      -inf
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0001
##      6        1.2913             nan     0.0010    0.0002
##      7        1.2910             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0001
##     40        1.2792             nan     0.0010    0.0001
##     60        1.2724             nan     0.0010    0.0002
##     80        1.2659             nan     0.0010    0.0001
##    100        1.2595             nan     0.0010    0.0001
##    120        1.2533             nan     0.0010    0.0002
##    140        1.2474             nan     0.0010    0.0001
##    160        1.2417             nan     0.0010    0.0001
##    180        1.2363             nan     0.0010    0.0001
##    200        1.2309             nan     0.0010    0.0001
##    220        1.2258             nan     0.0010    0.0001
##    240        1.2209             nan     0.0010    0.0001
##    260        1.2162             nan     0.0010    0.0001
##    280        1.2116             nan     0.0010    0.0001
##    300        1.2071             nan     0.0010    0.0001
##    320        1.2026             nan     0.0010    0.0001
##    340        1.1982             nan     0.0010    0.0001
##    360        1.1941             nan     0.0010    0.0001
##    380        1.1900             nan     0.0010    0.0001
##    400        1.1860             nan     0.0010    0.0001
##    420        1.1822             nan     0.0010    0.0001
##    440        1.1785             nan     0.0010    0.0001
##    460        1.1749             nan     0.0010    0.0001
##    480        1.1712             nan     0.0010    0.0001
##    500        1.1676             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2858             nan     0.0010    0.0002
##     40        1.2789             nan     0.0010    0.0002
##     60        1.2722             nan     0.0010    0.0001
##     80        1.2657             nan     0.0010    0.0001
##    100        1.2596             nan     0.0010    0.0001
##    120        1.2534             nan     0.0010    0.0001
##    140        1.2474             nan     0.0010    0.0001
##    160        1.2418             nan     0.0010    0.0001
##    180        1.2367             nan     0.0010    0.0001
##    200        1.2314             nan     0.0010    0.0001
##    220        1.2261             nan     0.0010    0.0001
##    240        1.2212             nan     0.0010    0.0001
##    260        1.2163             nan     0.0010    0.0001
##    280        1.2117             nan     0.0010    0.0001
##    300        1.2072             nan     0.0010    0.0001
##    320        1.2026             nan     0.0010    0.0001
##    340        1.1985             nan     0.0010    0.0001
##    360        1.1943             nan     0.0010    0.0001
##    380        1.1903             nan     0.0010    0.0001
##    400        1.1863             nan     0.0010    0.0001
##    420        1.1825             nan     0.0010    0.0001
##    440        1.1787             nan     0.0010    0.0001
##    460        1.1749             nan     0.0010    0.0001
##    480        1.1713             nan     0.0010    0.0001
##    500        1.1678             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0001
##     20        1.2864             nan     0.0010    0.0002
##     40        1.2791             nan     0.0010    0.0002
##     60        1.2723             nan     0.0010    0.0001
##     80        1.2659             nan     0.0010    0.0001
##    100        1.2596             nan     0.0010    0.0001
##    120        1.2537             nan     0.0010    0.0001
##    140        1.2478             nan     0.0010    0.0001
##    160        1.2421             nan     0.0010    0.0001
##    180        1.2367             nan     0.0010    0.0001
##    200        1.2315             nan     0.0010    0.0001
##    220        1.2262             nan     0.0010    0.0001
##    240        1.2213             nan     0.0010    0.0001
##    260        1.2163             nan     0.0010    0.0001
##    280        1.2116             nan     0.0010    0.0001
##    300        1.2072             nan     0.0010    0.0001
##    320        1.2026             nan     0.0010    0.0001
##    340        1.1982             nan     0.0010    0.0001
##    360        1.1940             nan     0.0010    0.0001
##    380        1.1899             nan     0.0010    0.0001
##    400        1.1860             nan     0.0010    0.0001
##    420        1.1821             nan     0.0010    0.0001
##    440        1.1783             nan     0.0010    0.0001
##    460        1.1746             nan     0.0010    0.0001
##    480        1.1710             nan     0.0010    0.0001
##    500        1.1674             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0003
##     20        1.2840             nan     0.0010    0.0002
##     40        1.2749             nan     0.0010    0.0002
##     60        1.2662             nan     0.0010    0.0002
##     80        1.2577             nan     0.0010    0.0002
##    100        1.2491             nan     0.0010    0.0002
##    120        1.2410             nan     0.0010    0.0002
##    140        1.2332             nan     0.0010    0.0001
##    160        1.2258             nan     0.0010    0.0002
##    180        1.2185             nan     0.0010    0.0001
##    200        1.2114             nan     0.0010    0.0002
##    220        1.2047             nan     0.0010    0.0001
##    240        1.1979             nan     0.0010    0.0001
##    260        1.1913             nan     0.0010    0.0001
##    280        1.1847             nan     0.0010    0.0002
##    300        1.1784             nan     0.0010    0.0001
##    320        1.1724             nan     0.0010    0.0001
##    340        1.1664             nan     0.0010    0.0001
##    360        1.1607             nan     0.0010    0.0001
##    380        1.1551             nan     0.0010    0.0001
##    400        1.1497             nan     0.0010    0.0001
##    420        1.1444             nan     0.0010    0.0001
##    440        1.1392             nan     0.0010    0.0001
##    460        1.1340             nan     0.0010    0.0001
##    480        1.1291             nan     0.0010    0.0001
##    500        1.1242             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2838             nan     0.0010    0.0002
##     40        1.2746             nan     0.0010    0.0002
##     60        1.2658             nan     0.0010    0.0002
##     80        1.2573             nan     0.0010    0.0002
##    100        1.2492             nan     0.0010    0.0002
##    120        1.2412             nan     0.0010    0.0002
##    140        1.2334             nan     0.0010    0.0002
##    160        1.2259             nan     0.0010    0.0002
##    180        1.2185             nan     0.0010    0.0002
##    200        1.2113             nan     0.0010    0.0002
##    220        1.2043             nan     0.0010    0.0001
##    240        1.1977             nan     0.0010    0.0001
##    260        1.1911             nan     0.0010    0.0002
##    280        1.1848             nan     0.0010    0.0001
##    300        1.1786             nan     0.0010    0.0001
##    320        1.1726             nan     0.0010    0.0001
##    340        1.1667             nan     0.0010    0.0001
##    360        1.1611             nan     0.0010    0.0001
##    380        1.1556             nan     0.0010    0.0001
##    400        1.1503             nan     0.0010    0.0001
##    420        1.1450             nan     0.0010    0.0001
##    440        1.1398             nan     0.0010    0.0001
##    460        1.1346             nan     0.0010    0.0001
##    480        1.1297             nan     0.0010    0.0001
##    500        1.1250             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2839             nan     0.0010    0.0002
##     40        1.2747             nan     0.0010    0.0002
##     60        1.2660             nan     0.0010    0.0002
##     80        1.2573             nan     0.0010    0.0002
##    100        1.2491             nan     0.0010    0.0002
##    120        1.2409             nan     0.0010    0.0002
##    140        1.2330             nan     0.0010    0.0002
##    160        1.2254             nan     0.0010    0.0002
##    180        1.2182             nan     0.0010    0.0001
##    200        1.2113             nan     0.0010    0.0002
##    220        1.2041             nan     0.0010    0.0001
##    240        1.1974             nan     0.0010    0.0001
##    260        1.1910             nan     0.0010    0.0001
##    280        1.1846             nan     0.0010    0.0001
##    300        1.1786             nan     0.0010    0.0001
##    320        1.1725             nan     0.0010    0.0001
##    340        1.1668             nan     0.0010    0.0001
##    360        1.1612             nan     0.0010    0.0001
##    380        1.1557             nan     0.0010    0.0001
##    400        1.1503             nan     0.0010    0.0001
##    420        1.1452             nan     0.0010    0.0001
##    440        1.1399             nan     0.0010    0.0001
##    460        1.1349             nan     0.0010    0.0001
##    480        1.1299             nan     0.0010    0.0001
##    500        1.1253             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2916             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2905             nan     0.0010    0.0003
##      6        1.2899             nan     0.0010    0.0003
##      7        1.2894             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0002
##      9        1.2883             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2824             nan     0.0010    0.0002
##     40        1.2719             nan     0.0010    0.0002
##     60        1.2617             nan     0.0010    0.0002
##     80        1.2514             nan     0.0010    0.0002
##    100        1.2420             nan     0.0010    0.0002
##    120        1.2326             nan     0.0010    0.0002
##    140        1.2237             nan     0.0010    0.0002
##    160        1.2150             nan     0.0010    0.0002
##    180        1.2067             nan     0.0010    0.0001
##    200        1.1981             nan     0.0010    0.0002
##    220        1.1903             nan     0.0010    0.0002
##    240        1.1826             nan     0.0010    0.0001
##    260        1.1751             nan     0.0010    0.0001
##    280        1.1680             nan     0.0010    0.0002
##    300        1.1610             nan     0.0010    0.0002
##    320        1.1540             nan     0.0010    0.0001
##    340        1.1473             nan     0.0010    0.0002
##    360        1.1409             nan     0.0010    0.0001
##    380        1.1345             nan     0.0010    0.0001
##    400        1.1282             nan     0.0010    0.0001
##    420        1.1224             nan     0.0010    0.0001
##    440        1.1166             nan     0.0010    0.0001
##    460        1.1109             nan     0.0010    0.0001
##    480        1.1052             nan     0.0010    0.0001
##    500        1.0998             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2916             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2894             nan     0.0010    0.0002
##      8        1.2888             nan     0.0010    0.0003
##      9        1.2883             nan     0.0010    0.0003
##     10        1.2877             nan     0.0010    0.0003
##     20        1.2823             nan     0.0010    0.0002
##     40        1.2716             nan     0.0010    0.0002
##     60        1.2614             nan     0.0010    0.0002
##     80        1.2515             nan     0.0010    0.0002
##    100        1.2421             nan     0.0010    0.0002
##    120        1.2330             nan     0.0010    0.0002
##    140        1.2241             nan     0.0010    0.0002
##    160        1.2151             nan     0.0010    0.0002
##    180        1.2065             nan     0.0010    0.0002
##    200        1.1982             nan     0.0010    0.0002
##    220        1.1903             nan     0.0010    0.0001
##    240        1.1822             nan     0.0010    0.0002
##    260        1.1746             nan     0.0010    0.0001
##    280        1.1672             nan     0.0010    0.0002
##    300        1.1602             nan     0.0010    0.0001
##    320        1.1532             nan     0.0010    0.0002
##    340        1.1468             nan     0.0010    0.0001
##    360        1.1402             nan     0.0010    0.0001
##    380        1.1339             nan     0.0010    0.0001
##    400        1.1277             nan     0.0010    0.0001
##    420        1.1215             nan     0.0010    0.0001
##    440        1.1156             nan     0.0010    0.0001
##    460        1.1097             nan     0.0010    0.0001
##    480        1.1041             nan     0.0010    0.0001
##    500        1.0986             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2894             nan     0.0010    0.0003
##      8        1.2888             nan     0.0010    0.0002
##      9        1.2882             nan     0.0010    0.0003
##     10        1.2877             nan     0.0010    0.0003
##     20        1.2821             nan     0.0010    0.0003
##     40        1.2718             nan     0.0010    0.0002
##     60        1.2616             nan     0.0010    0.0002
##     80        1.2518             nan     0.0010    0.0002
##    100        1.2422             nan     0.0010    0.0002
##    120        1.2331             nan     0.0010    0.0002
##    140        1.2242             nan     0.0010    0.0002
##    160        1.2156             nan     0.0010    0.0001
##    180        1.2069             nan     0.0010    0.0002
##    200        1.1985             nan     0.0010    0.0002
##    220        1.1906             nan     0.0010    0.0002
##    240        1.1829             nan     0.0010    0.0002
##    260        1.1754             nan     0.0010    0.0001
##    280        1.1681             nan     0.0010    0.0002
##    300        1.1610             nan     0.0010    0.0001
##    320        1.1542             nan     0.0010    0.0001
##    340        1.1477             nan     0.0010    0.0001
##    360        1.1412             nan     0.0010    0.0002
##    380        1.1347             nan     0.0010    0.0001
##    400        1.1285             nan     0.0010    0.0001
##    420        1.1225             nan     0.0010    0.0001
##    440        1.1166             nan     0.0010    0.0001
##    460        1.1111             nan     0.0010    0.0001
##    480        1.1055             nan     0.0010    0.0001
##    500        1.1003             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2567             nan     0.1000    0.0151
##      2        1.2252             nan     0.1000    0.0122
##      3        1.2029             nan     0.1000    0.0122
##      4        1.1788             nan     0.1000    0.0088
##      5        1.1593             nan     0.1000    0.0064
##      6        1.1446             nan     0.1000    0.0058
##      7        1.1291             nan     0.1000    0.0064
##      8        1.1167             nan     0.1000    0.0056
##      9        1.1028             nan     0.1000    0.0060
##     10        1.0936             nan     0.1000    0.0039
##     20        1.0079             nan     0.1000    0.0017
##     40        0.9284             nan     0.1000    0.0013
##     60        0.8847             nan     0.1000   -0.0002
##     80        0.8571             nan     0.1000   -0.0005
##    100        0.8313             nan     0.1000   -0.0011
##    120        0.8184             nan     0.1000   -0.0004
##    140        0.8090             nan     0.1000   -0.0011
##    160        0.7959             nan     0.1000   -0.0003
##    180        0.7889             nan     0.1000   -0.0005
##    200        0.7773             nan     0.1000   -0.0005
##    220        0.7684             nan     0.1000   -0.0003
##    240        0.7610             nan     0.1000   -0.0015
##    260        0.7534             nan     0.1000   -0.0017
##    280        0.7451             nan     0.1000   -0.0005
##    300        0.7404             nan     0.1000   -0.0001
##    320        0.7343             nan     0.1000   -0.0016
##    340        0.7276             nan     0.1000   -0.0014
##    360        0.7219             nan     0.1000   -0.0016
##    380        0.7162             nan     0.1000   -0.0006
##    400        0.7122             nan     0.1000   -0.0018
##    420        0.7072             nan     0.1000   -0.0006
##    440        0.7019             nan     0.1000   -0.0012
##    460        0.6984             nan     0.1000   -0.0009
##    480        0.6917             nan     0.1000   -0.0002
##    500        0.6877             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2583             nan     0.1000    0.0164
##      2        1.2251             nan     0.1000    0.0115
##      3        1.2009             nan     0.1000    0.0103
##      4        1.1785             nan     0.1000    0.0083
##      5        1.1612             nan     0.1000    0.0071
##      6        1.1428             nan     0.1000    0.0062
##      7        1.1308             nan     0.1000    0.0057
##      8        1.1170             nan     0.1000    0.0067
##      9        1.1021             nan     0.1000    0.0056
##     10        1.0900             nan     0.1000    0.0048
##     20        1.0058             nan     0.1000    0.0020
##     40        0.9251             nan     0.1000    0.0001
##     60        0.8808             nan     0.1000   -0.0003
##     80        0.8507             nan     0.1000   -0.0006
##    100        0.8295             nan     0.1000    0.0000
##    120        0.8132             nan     0.1000   -0.0020
##    140        0.7972             nan     0.1000   -0.0007
##    160        0.7881             nan     0.1000   -0.0002
##    180        0.7806             nan     0.1000   -0.0009
##    200        0.7723             nan     0.1000   -0.0008
##    220        0.7661             nan     0.1000   -0.0008
##    240        0.7597             nan     0.1000   -0.0010
##    260        0.7508             nan     0.1000   -0.0005
##    280        0.7435             nan     0.1000   -0.0015
##    300        0.7374             nan     0.1000   -0.0013
##    320        0.7311             nan     0.1000   -0.0005
##    340        0.7235             nan     0.1000   -0.0007
##    360        0.7163             nan     0.1000   -0.0012
##    380        0.7110             nan     0.1000   -0.0011
##    400        0.7057             nan     0.1000    0.0000
##    420        0.7007             nan     0.1000   -0.0014
##    440        0.6972             nan     0.1000   -0.0016
##    460        0.6926             nan     0.1000   -0.0011
##    480        0.6890             nan     0.1000   -0.0008
##    500        0.6839             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2634             nan     0.1000    0.0133
##      2        1.2362             nan     0.1000    0.0136
##      3        1.2102             nan     0.1000    0.0119
##      4        1.1892             nan     0.1000    0.0112
##      5        1.1706             nan     0.1000    0.0086
##      6        1.1535             nan     0.1000    0.0071
##      7        1.1365             nan     0.1000    0.0078
##      8        1.1235             nan     0.1000    0.0054
##      9        1.1083             nan     0.1000    0.0040
##     10        1.0944             nan     0.1000    0.0047
##     20        1.0045             nan     0.1000    0.0020
##     40        0.9232             nan     0.1000   -0.0006
##     60        0.8845             nan     0.1000   -0.0013
##     80        0.8559             nan     0.1000    0.0001
##    100        0.8387             nan     0.1000   -0.0007
##    120        0.8238             nan     0.1000   -0.0007
##    140        0.8095             nan     0.1000   -0.0009
##    160        0.7996             nan     0.1000   -0.0004
##    180        0.7883             nan     0.1000   -0.0008
##    200        0.7787             nan     0.1000   -0.0002
##    220        0.7701             nan     0.1000   -0.0012
##    240        0.7626             nan     0.1000    0.0000
##    260        0.7553             nan     0.1000   -0.0006
##    280        0.7491             nan     0.1000   -0.0007
##    300        0.7417             nan     0.1000   -0.0012
##    320        0.7351             nan     0.1000   -0.0006
##    340        0.7294             nan     0.1000   -0.0010
##    360        0.7232             nan     0.1000   -0.0011
##    380        0.7174             nan     0.1000   -0.0008
##    400        0.7111             nan     0.1000   -0.0010
##    420        0.7082             nan     0.1000   -0.0005
##    440        0.7033             nan     0.1000   -0.0001
##    460        0.6974             nan     0.1000   -0.0006
##    480        0.6936             nan     0.1000   -0.0009
##    500        0.6889             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2483             nan     0.1000    0.0185
##      2        1.2139             nan     0.1000    0.0127
##      3        1.1811             nan     0.1000    0.0163
##      4        1.1479             nan     0.1000    0.0127
##      5        1.1247             nan     0.1000    0.0079
##      6        1.1008             nan     0.1000    0.0105
##      7        1.0810             nan     0.1000    0.0069
##      8        1.0638             nan     0.1000    0.0060
##      9        1.0467             nan     0.1000    0.0051
##     10        1.0319             nan     0.1000    0.0062
##     20        0.9344             nan     0.1000    0.0006
##     40        0.8481             nan     0.1000   -0.0011
##     60        0.7972             nan     0.1000    0.0002
##     80        0.7666             nan     0.1000   -0.0014
##    100        0.7353             nan     0.1000   -0.0014
##    120        0.7114             nan     0.1000    0.0002
##    140        0.6917             nan     0.1000   -0.0008
##    160        0.6674             nan     0.1000   -0.0009
##    180        0.6468             nan     0.1000   -0.0005
##    200        0.6294             nan     0.1000   -0.0014
##    220        0.6057             nan     0.1000   -0.0011
##    240        0.5891             nan     0.1000   -0.0005
##    260        0.5723             nan     0.1000   -0.0009
##    280        0.5579             nan     0.1000   -0.0007
##    300        0.5406             nan     0.1000   -0.0013
##    320        0.5222             nan     0.1000   -0.0010
##    340        0.5104             nan     0.1000   -0.0003
##    360        0.4958             nan     0.1000   -0.0007
##    380        0.4833             nan     0.1000   -0.0001
##    400        0.4739             nan     0.1000   -0.0018
##    420        0.4604             nan     0.1000   -0.0006
##    440        0.4481             nan     0.1000   -0.0009
##    460        0.4389             nan     0.1000   -0.0007
##    480        0.4294             nan     0.1000   -0.0005
##    500        0.4200             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2485             nan     0.1000    0.0179
##      2        1.2119             nan     0.1000    0.0154
##      3        1.1793             nan     0.1000    0.0149
##      4        1.1485             nan     0.1000    0.0132
##      5        1.1230             nan     0.1000    0.0110
##      6        1.1009             nan     0.1000    0.0085
##      7        1.0830             nan     0.1000    0.0076
##      8        1.0656             nan     0.1000    0.0079
##      9        1.0537             nan     0.1000    0.0022
##     10        1.0372             nan     0.1000    0.0067
##     20        0.9370             nan     0.1000    0.0015
##     40        0.8576             nan     0.1000   -0.0008
##     60        0.8017             nan     0.1000   -0.0009
##     80        0.7719             nan     0.1000   -0.0022
##    100        0.7471             nan     0.1000   -0.0015
##    120        0.7172             nan     0.1000   -0.0007
##    140        0.6933             nan     0.1000   -0.0007
##    160        0.6724             nan     0.1000   -0.0018
##    180        0.6490             nan     0.1000   -0.0005
##    200        0.6258             nan     0.1000   -0.0015
##    220        0.6072             nan     0.1000   -0.0007
##    240        0.5897             nan     0.1000   -0.0012
##    260        0.5741             nan     0.1000   -0.0019
##    280        0.5586             nan     0.1000   -0.0014
##    300        0.5435             nan     0.1000   -0.0003
##    320        0.5327             nan     0.1000   -0.0013
##    340        0.5204             nan     0.1000   -0.0008
##    360        0.5072             nan     0.1000   -0.0003
##    380        0.4959             nan     0.1000   -0.0004
##    400        0.4857             nan     0.1000   -0.0009
##    420        0.4755             nan     0.1000   -0.0005
##    440        0.4646             nan     0.1000   -0.0011
##    460        0.4535             nan     0.1000   -0.0011
##    480        0.4405             nan     0.1000   -0.0023
##    500        0.4330             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2489             nan     0.1000    0.0207
##      2        1.2143             nan     0.1000    0.0169
##      3        1.1834             nan     0.1000    0.0147
##      4        1.1512             nan     0.1000    0.0128
##      5        1.1301             nan     0.1000    0.0105
##      6        1.1072             nan     0.1000    0.0076
##      7        1.0853             nan     0.1000    0.0083
##      8        1.0664             nan     0.1000    0.0076
##      9        1.0528             nan     0.1000    0.0052
##     10        1.0375             nan     0.1000    0.0067
##     20        0.9411             nan     0.1000    0.0009
##     40        0.8567             nan     0.1000   -0.0013
##     60        0.8035             nan     0.1000   -0.0016
##     80        0.7736             nan     0.1000   -0.0004
##    100        0.7465             nan     0.1000   -0.0019
##    120        0.7181             nan     0.1000   -0.0011
##    140        0.6949             nan     0.1000   -0.0017
##    160        0.6721             nan     0.1000   -0.0007
##    180        0.6478             nan     0.1000   -0.0020
##    200        0.6298             nan     0.1000   -0.0020
##    220        0.6094             nan     0.1000   -0.0013
##    240        0.5921             nan     0.1000   -0.0002
##    260        0.5767             nan     0.1000   -0.0023
##    280        0.5618             nan     0.1000   -0.0020
##    300        0.5480             nan     0.1000   -0.0019
##    320        0.5310             nan     0.1000   -0.0014
##    340        0.5177             nan     0.1000   -0.0003
##    360        0.5024             nan     0.1000   -0.0015
##    380        0.4906             nan     0.1000   -0.0016
##    400        0.4784             nan     0.1000   -0.0004
##    420        0.4662             nan     0.1000   -0.0003
##    440        0.4583             nan     0.1000   -0.0000
##    460        0.4492             nan     0.1000   -0.0005
##    480        0.4384             nan     0.1000   -0.0011
##    500        0.4277             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2416             nan     0.1000    0.0227
##      2        1.1956             nan     0.1000    0.0190
##      3        1.1565             nan     0.1000    0.0156
##      4        1.1234             nan     0.1000    0.0146
##      5        1.0931             nan     0.1000    0.0128
##      6        1.0694             nan     0.1000    0.0103
##      7        1.0498             nan     0.1000    0.0056
##      8        1.0302             nan     0.1000    0.0058
##      9        1.0114             nan     0.1000    0.0049
##     10        0.9965             nan     0.1000    0.0054
##     20        0.8951             nan     0.1000    0.0013
##     40        0.7985             nan     0.1000   -0.0007
##     60        0.7344             nan     0.1000   -0.0028
##     80        0.6896             nan     0.1000   -0.0013
##    100        0.6417             nan     0.1000   -0.0015
##    120        0.6069             nan     0.1000   -0.0011
##    140        0.5737             nan     0.1000   -0.0016
##    160        0.5464             nan     0.1000   -0.0016
##    180        0.5205             nan     0.1000   -0.0004
##    200        0.4924             nan     0.1000   -0.0002
##    220        0.4716             nan     0.1000   -0.0002
##    240        0.4438             nan     0.1000   -0.0013
##    260        0.4247             nan     0.1000   -0.0005
##    280        0.4079             nan     0.1000   -0.0012
##    300        0.3905             nan     0.1000   -0.0005
##    320        0.3748             nan     0.1000   -0.0009
##    340        0.3597             nan     0.1000   -0.0010
##    360        0.3437             nan     0.1000   -0.0011
##    380        0.3312             nan     0.1000   -0.0005
##    400        0.3167             nan     0.1000   -0.0001
##    420        0.3058             nan     0.1000   -0.0006
##    440        0.2948             nan     0.1000   -0.0007
##    460        0.2859             nan     0.1000   -0.0009
##    480        0.2752             nan     0.1000   -0.0005
##    500        0.2661             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2391             nan     0.1000    0.0205
##      2        1.1914             nan     0.1000    0.0212
##      3        1.1541             nan     0.1000    0.0165
##      4        1.1216             nan     0.1000    0.0123
##      5        1.0873             nan     0.1000    0.0128
##      6        1.0671             nan     0.1000    0.0064
##      7        1.0478             nan     0.1000    0.0056
##      8        1.0245             nan     0.1000    0.0088
##      9        1.0051             nan     0.1000    0.0066
##     10        0.9946             nan     0.1000    0.0008
##     20        0.8939             nan     0.1000    0.0020
##     40        0.7965             nan     0.1000   -0.0015
##     60        0.7364             nan     0.1000   -0.0013
##     80        0.6868             nan     0.1000    0.0000
##    100        0.6406             nan     0.1000   -0.0007
##    120        0.6090             nan     0.1000   -0.0019
##    140        0.5793             nan     0.1000   -0.0006
##    160        0.5553             nan     0.1000   -0.0011
##    180        0.5273             nan     0.1000   -0.0013
##    200        0.5046             nan     0.1000    0.0003
##    220        0.4823             nan     0.1000   -0.0010
##    240        0.4619             nan     0.1000   -0.0008
##    260        0.4404             nan     0.1000   -0.0005
##    280        0.4200             nan     0.1000   -0.0017
##    300        0.4022             nan     0.1000   -0.0006
##    320        0.3879             nan     0.1000   -0.0007
##    340        0.3724             nan     0.1000   -0.0005
##    360        0.3577             nan     0.1000   -0.0007
##    380        0.3429             nan     0.1000   -0.0008
##    400        0.3310             nan     0.1000   -0.0010
##    420        0.3204             nan     0.1000   -0.0011
##    440        0.3060             nan     0.1000   -0.0009
##    460        0.2914             nan     0.1000   -0.0003
##    480        0.2812             nan     0.1000   -0.0005
##    500        0.2706             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2421             nan     0.1000    0.0236
##      2        1.2030             nan     0.1000    0.0150
##      3        1.1665             nan     0.1000    0.0159
##      4        1.1342             nan     0.1000    0.0110
##      5        1.1050             nan     0.1000    0.0119
##      6        1.0745             nan     0.1000    0.0121
##      7        1.0532             nan     0.1000    0.0080
##      8        1.0325             nan     0.1000    0.0078
##      9        1.0159             nan     0.1000    0.0054
##     10        0.9962             nan     0.1000    0.0045
##     20        0.8983             nan     0.1000    0.0007
##     40        0.7928             nan     0.1000   -0.0007
##     60        0.7352             nan     0.1000   -0.0016
##     80        0.6923             nan     0.1000   -0.0018
##    100        0.6518             nan     0.1000   -0.0019
##    120        0.6198             nan     0.1000    0.0001
##    140        0.5857             nan     0.1000   -0.0010
##    160        0.5593             nan     0.1000   -0.0013
##    180        0.5325             nan     0.1000   -0.0002
##    200        0.5050             nan     0.1000   -0.0013
##    220        0.4834             nan     0.1000   -0.0009
##    240        0.4625             nan     0.1000   -0.0003
##    260        0.4454             nan     0.1000   -0.0008
##    280        0.4240             nan     0.1000   -0.0006
##    300        0.4081             nan     0.1000   -0.0008
##    320        0.3895             nan     0.1000   -0.0013
##    340        0.3710             nan     0.1000   -0.0010
##    360        0.3531             nan     0.1000   -0.0015
##    380        0.3383             nan     0.1000   -0.0010
##    400        0.3258             nan     0.1000   -0.0011
##    420        0.3126             nan     0.1000   -0.0012
##    440        0.3023             nan     0.1000   -0.0008
##    460        0.2909             nan     0.1000   -0.0004
##    480        0.2807             nan     0.1000   -0.0005
##    500        0.2696             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2266             nan     0.2000    0.0333
##      2        1.1838             nan     0.2000    0.0182
##      3        1.1482             nan     0.2000    0.0169
##      4        1.1204             nan     0.2000    0.0115
##      5        1.0973             nan     0.2000    0.0103
##      6        1.0719             nan     0.2000    0.0102
##      7        1.0542             nan     0.2000    0.0080
##      8        1.0405             nan     0.2000    0.0010
##      9        1.0301             nan     0.2000    0.0016
##     10        1.0121             nan     0.2000    0.0058
##     20        0.9238             nan     0.2000   -0.0009
##     40        0.8507             nan     0.2000    0.0002
##     60        0.8156             nan     0.2000   -0.0020
##     80        0.7960             nan     0.2000   -0.0030
##    100        0.7757             nan     0.2000   -0.0013
##    120        0.7598             nan     0.2000   -0.0010
##    140        0.7428             nan     0.2000   -0.0028
##    160        0.7277             nan     0.2000   -0.0014
##    180        0.7176             nan     0.2000   -0.0016
##    200        0.7054             nan     0.2000   -0.0008
##    220        0.6967             nan     0.2000   -0.0017
##    240        0.6850             nan     0.2000   -0.0009
##    260        0.6782             nan     0.2000   -0.0021
##    280        0.6735             nan     0.2000   -0.0013
##    300        0.6675             nan     0.2000   -0.0036
##    320        0.6592             nan     0.2000   -0.0025
##    340        0.6558             nan     0.2000   -0.0027
##    360        0.6469             nan     0.2000   -0.0033
##    380        0.6410             nan     0.2000   -0.0002
##    400        0.6352             nan     0.2000   -0.0021
##    420        0.6279             nan     0.2000   -0.0014
##    440        0.6234             nan     0.2000   -0.0013
##    460        0.6159             nan     0.2000   -0.0005
##    480        0.6068             nan     0.2000   -0.0014
##    500        0.6034             nan     0.2000   -0.0031
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2330             nan     0.2000    0.0268
##      2        1.1858             nan     0.2000    0.0234
##      3        1.1441             nan     0.2000    0.0183
##      4        1.1182             nan     0.2000    0.0088
##      5        1.0902             nan     0.2000    0.0121
##      6        1.0650             nan     0.2000    0.0108
##      7        1.0489             nan     0.2000    0.0044
##      8        1.0336             nan     0.2000    0.0066
##      9        1.0175             nan     0.2000    0.0041
##     10        1.0113             nan     0.2000    0.0005
##     20        0.9236             nan     0.2000    0.0020
##     40        0.8536             nan     0.2000   -0.0009
##     60        0.8295             nan     0.2000   -0.0029
##     80        0.8012             nan     0.2000   -0.0012
##    100        0.7827             nan     0.2000   -0.0014
##    120        0.7693             nan     0.2000   -0.0012
##    140        0.7541             nan     0.2000   -0.0022
##    160        0.7446             nan     0.2000   -0.0027
##    180        0.7296             nan     0.2000   -0.0018
##    200        0.7231             nan     0.2000   -0.0024
##    220        0.7079             nan     0.2000   -0.0007
##    240        0.6991             nan     0.2000   -0.0020
##    260        0.6916             nan     0.2000   -0.0032
##    280        0.6822             nan     0.2000   -0.0011
##    300        0.6725             nan     0.2000   -0.0026
##    320        0.6628             nan     0.2000   -0.0009
##    340        0.6549             nan     0.2000   -0.0017
##    360        0.6489             nan     0.2000   -0.0009
##    380        0.6419             nan     0.2000   -0.0013
##    400        0.6359             nan     0.2000   -0.0003
##    420        0.6294             nan     0.2000   -0.0018
##    440        0.6220             nan     0.2000   -0.0021
##    460        0.6164             nan     0.2000   -0.0017
##    480        0.6115             nan     0.2000   -0.0028
##    500        0.6076             nan     0.2000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2276             nan     0.2000    0.0331
##      2        1.1831             nan     0.2000    0.0169
##      3        1.1463             nan     0.2000    0.0139
##      4        1.1206             nan     0.2000    0.0078
##      5        1.0923             nan     0.2000    0.0113
##      6        1.0706             nan     0.2000    0.0071
##      7        1.0511             nan     0.2000    0.0103
##      8        1.0334             nan     0.2000    0.0064
##      9        1.0225             nan     0.2000    0.0042
##     10        1.0080             nan     0.2000    0.0061
##     20        0.9297             nan     0.2000    0.0016
##     40        0.8638             nan     0.2000   -0.0023
##     60        0.8224             nan     0.2000   -0.0024
##     80        0.7935             nan     0.2000   -0.0024
##    100        0.7823             nan     0.2000   -0.0027
##    120        0.7683             nan     0.2000   -0.0020
##    140        0.7586             nan     0.2000   -0.0023
##    160        0.7420             nan     0.2000   -0.0024
##    180        0.7295             nan     0.2000   -0.0021
##    200        0.7182             nan     0.2000   -0.0026
##    220        0.7075             nan     0.2000   -0.0029
##    240        0.6990             nan     0.2000   -0.0003
##    260        0.6934             nan     0.2000   -0.0025
##    280        0.6848             nan     0.2000   -0.0008
##    300        0.6747             nan     0.2000   -0.0038
##    320        0.6664             nan     0.2000   -0.0019
##    340        0.6600             nan     0.2000   -0.0007
##    360        0.6529             nan     0.2000   -0.0012
##    380        0.6461             nan     0.2000   -0.0021
##    400        0.6403             nan     0.2000   -0.0023
##    420        0.6330             nan     0.2000   -0.0017
##    440        0.6298             nan     0.2000   -0.0010
##    460        0.6241             nan     0.2000   -0.0013
##    480        0.6190             nan     0.2000   -0.0008
##    500        0.6141             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2034             nan     0.2000    0.0388
##      2        1.1403             nan     0.2000    0.0233
##      3        1.0958             nan     0.2000    0.0184
##      4        1.0595             nan     0.2000    0.0156
##      5        1.0294             nan     0.2000    0.0113
##      6        1.0054             nan     0.2000    0.0090
##      7        0.9812             nan     0.2000    0.0073
##      8        0.9652             nan     0.2000    0.0025
##      9        0.9509             nan     0.2000    0.0013
##     10        0.9410             nan     0.2000    0.0007
##     20        0.8579             nan     0.2000    0.0005
##     40        0.7771             nan     0.2000   -0.0014
##     60        0.7236             nan     0.2000   -0.0027
##     80        0.6754             nan     0.2000   -0.0030
##    100        0.6375             nan     0.2000   -0.0018
##    120        0.5984             nan     0.2000   -0.0018
##    140        0.5615             nan     0.2000   -0.0031
##    160        0.5295             nan     0.2000   -0.0021
##    180        0.5011             nan     0.2000   -0.0004
##    200        0.4773             nan     0.2000   -0.0025
##    220        0.4521             nan     0.2000   -0.0013
##    240        0.4272             nan     0.2000   -0.0016
##    260        0.4055             nan     0.2000   -0.0009
##    280        0.3897             nan     0.2000   -0.0008
##    300        0.3742             nan     0.2000   -0.0004
##    320        0.3565             nan     0.2000   -0.0015
##    340        0.3448             nan     0.2000   -0.0012
##    360        0.3305             nan     0.2000   -0.0005
##    380        0.3178             nan     0.2000   -0.0012
##    400        0.3069             nan     0.2000   -0.0011
##    420        0.2959             nan     0.2000   -0.0015
##    440        0.2846             nan     0.2000   -0.0013
##    460        0.2720             nan     0.2000   -0.0021
##    480        0.2615             nan     0.2000   -0.0011
##    500        0.2530             nan     0.2000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1997             nan     0.2000    0.0376
##      2        1.1463             nan     0.2000    0.0239
##      3        1.0949             nan     0.2000    0.0202
##      4        1.0573             nan     0.2000    0.0168
##      5        1.0234             nan     0.2000    0.0133
##      6        1.0007             nan     0.2000    0.0074
##      7        0.9798             nan     0.2000    0.0064
##      8        0.9647             nan     0.2000    0.0009
##      9        0.9538             nan     0.2000    0.0015
##     10        0.9445             nan     0.2000    0.0002
##     20        0.8574             nan     0.2000    0.0001
##     40        0.7754             nan     0.2000   -0.0046
##     60        0.7198             nan     0.2000   -0.0024
##     80        0.6777             nan     0.2000   -0.0043
##    100        0.6313             nan     0.2000   -0.0011
##    120        0.5990             nan     0.2000   -0.0018
##    140        0.5653             nan     0.2000   -0.0034
##    160        0.5245             nan     0.2000   -0.0037
##    180        0.5024             nan     0.2000   -0.0016
##    200        0.4748             nan     0.2000   -0.0028
##    220        0.4497             nan     0.2000   -0.0025
##    240        0.4360             nan     0.2000   -0.0021
##    260        0.4179             nan     0.2000   -0.0039
##    280        0.4067             nan     0.2000   -0.0024
##    300        0.3882             nan     0.2000   -0.0036
##    320        0.3711             nan     0.2000   -0.0008
##    340        0.3553             nan     0.2000   -0.0021
##    360        0.3420             nan     0.2000   -0.0011
##    380        0.3302             nan     0.2000   -0.0002
##    400        0.3169             nan     0.2000   -0.0010
##    420        0.3043             nan     0.2000   -0.0012
##    440        0.2914             nan     0.2000   -0.0014
##    460        0.2770             nan     0.2000   -0.0015
##    480        0.2658             nan     0.2000   -0.0018
##    500        0.2567             nan     0.2000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2139             nan     0.2000    0.0355
##      2        1.1434             nan     0.2000    0.0341
##      3        1.0941             nan     0.2000    0.0164
##      4        1.0554             nan     0.2000    0.0144
##      5        1.0267             nan     0.2000    0.0137
##      6        1.0044             nan     0.2000    0.0074
##      7        0.9819             nan     0.2000    0.0049
##      8        0.9649             nan     0.2000    0.0041
##      9        0.9532             nan     0.2000    0.0011
##     10        0.9372             nan     0.2000    0.0038
##     20        0.8504             nan     0.2000   -0.0008
##     40        0.7573             nan     0.2000    0.0001
##     60        0.7201             nan     0.2000   -0.0031
##     80        0.6638             nan     0.2000   -0.0028
##    100        0.6175             nan     0.2000   -0.0020
##    120        0.5893             nan     0.2000   -0.0042
##    140        0.5673             nan     0.2000   -0.0016
##    160        0.5421             nan     0.2000   -0.0023
##    180        0.5145             nan     0.2000   -0.0029
##    200        0.4885             nan     0.2000   -0.0013
##    220        0.4660             nan     0.2000   -0.0010
##    240        0.4465             nan     0.2000   -0.0026
##    260        0.4264             nan     0.2000   -0.0002
##    280        0.4046             nan     0.2000   -0.0011
##    300        0.3876             nan     0.2000   -0.0015
##    320        0.3740             nan     0.2000   -0.0028
##    340        0.3610             nan     0.2000   -0.0002
##    360        0.3454             nan     0.2000   -0.0024
##    380        0.3300             nan     0.2000   -0.0001
##    400        0.3138             nan     0.2000   -0.0017
##    420        0.3027             nan     0.2000   -0.0010
##    440        0.2921             nan     0.2000   -0.0007
##    460        0.2808             nan     0.2000   -0.0024
##    480        0.2718             nan     0.2000   -0.0014
##    500        0.2643             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1968             nan     0.2000    0.0432
##      2        1.1341             nan     0.2000    0.0253
##      3        1.0793             nan     0.2000    0.0208
##      4        1.0479             nan     0.2000    0.0088
##      5        1.0135             nan     0.2000    0.0142
##      6        0.9799             nan     0.2000    0.0133
##      7        0.9530             nan     0.2000    0.0067
##      8        0.9338             nan     0.2000    0.0048
##      9        0.9167             nan     0.2000    0.0033
##     10        0.9016             nan     0.2000    0.0020
##     20        0.8102             nan     0.2000   -0.0011
##     40        0.7096             nan     0.2000   -0.0030
##     60        0.6517             nan     0.2000   -0.0033
##     80        0.5936             nan     0.2000   -0.0045
##    100        0.5358             nan     0.2000   -0.0004
##    120        0.4884             nan     0.2000   -0.0036
##    140        0.4514             nan     0.2000   -0.0040
##    160        0.4112             nan     0.2000   -0.0050
##    180        0.3710             nan     0.2000   -0.0028
##    200        0.3461             nan     0.2000   -0.0014
##    220        0.3155             nan     0.2000   -0.0025
##    240        0.2917             nan     0.2000   -0.0004
##    260        0.2683             nan     0.2000   -0.0014
##    280        0.2466             nan     0.2000   -0.0010
##    300        0.2283             nan     0.2000   -0.0016
##    320        0.2114             nan     0.2000   -0.0012
##    340        0.1947             nan     0.2000   -0.0004
##    360        0.1807             nan     0.2000   -0.0006
##    380        0.1661             nan     0.2000   -0.0010
##    400        0.1563             nan     0.2000   -0.0002
##    420        0.1473             nan     0.2000   -0.0007
##    440        0.1381             nan     0.2000   -0.0008
##    460        0.1309             nan     0.2000   -0.0010
##    480        0.1206             nan     0.2000   -0.0002
##    500        0.1148             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1961             nan     0.2000    0.0478
##      2        1.1317             nan     0.2000    0.0228
##      3        1.0873             nan     0.2000    0.0153
##      4        1.0409             nan     0.2000    0.0160
##      5        1.0038             nan     0.2000    0.0098
##      6        0.9733             nan     0.2000    0.0063
##      7        0.9529             nan     0.2000    0.0046
##      8        0.9375             nan     0.2000    0.0043
##      9        0.9175             nan     0.2000    0.0065
##     10        0.9032             nan     0.2000   -0.0001
##     20        0.8120             nan     0.2000   -0.0015
##     40        0.6958             nan     0.2000   -0.0035
##     60        0.6141             nan     0.2000   -0.0044
##     80        0.5535             nan     0.2000   -0.0026
##    100        0.5019             nan     0.2000   -0.0013
##    120        0.4632             nan     0.2000   -0.0055
##    140        0.4284             nan     0.2000   -0.0010
##    160        0.3946             nan     0.2000   -0.0034
##    180        0.3632             nan     0.2000   -0.0009
##    200        0.3350             nan     0.2000   -0.0026
##    220        0.3092             nan     0.2000   -0.0013
##    240        0.2838             nan     0.2000   -0.0010
##    260        0.2612             nan     0.2000   -0.0009
##    280        0.2402             nan     0.2000   -0.0011
##    300        0.2253             nan     0.2000   -0.0015
##    320        0.2080             nan     0.2000   -0.0008
##    340        0.1955             nan     0.2000   -0.0007
##    360        0.1842             nan     0.2000   -0.0013
##    380        0.1715             nan     0.2000   -0.0008
##    400        0.1624             nan     0.2000   -0.0008
##    420        0.1542             nan     0.2000   -0.0013
##    440        0.1451             nan     0.2000   -0.0007
##    460        0.1350             nan     0.2000   -0.0004
##    480        0.1273             nan     0.2000   -0.0005
##    500        0.1194             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1978             nan     0.2000    0.0422
##      2        1.1265             nan     0.2000    0.0261
##      3        1.0804             nan     0.2000    0.0151
##      4        1.0491             nan     0.2000    0.0079
##      5        1.0130             nan     0.2000    0.0125
##      6        0.9784             nan     0.2000    0.0117
##      7        0.9503             nan     0.2000    0.0024
##      8        0.9253             nan     0.2000    0.0074
##      9        0.9104             nan     0.2000   -0.0006
##     10        0.8998             nan     0.2000   -0.0024
##     20        0.8040             nan     0.2000   -0.0004
##     40        0.7034             nan     0.2000   -0.0012
##     60        0.6273             nan     0.2000   -0.0027
##     80        0.5700             nan     0.2000   -0.0020
##    100        0.5107             nan     0.2000   -0.0030
##    120        0.4711             nan     0.2000   -0.0020
##    140        0.4381             nan     0.2000   -0.0029
##    160        0.4026             nan     0.2000   -0.0030
##    180        0.3631             nan     0.2000   -0.0019
##    200        0.3396             nan     0.2000   -0.0025
##    220        0.3145             nan     0.2000   -0.0031
##    240        0.2918             nan     0.2000   -0.0008
##    260        0.2702             nan     0.2000   -0.0019
##    280        0.2498             nan     0.2000   -0.0014
##    300        0.2312             nan     0.2000   -0.0008
##    320        0.2163             nan     0.2000   -0.0010
##    340        0.2032             nan     0.2000   -0.0008
##    360        0.1897             nan     0.2000   -0.0009
##    380        0.1785             nan     0.2000   -0.0004
##    400        0.1680             nan     0.2000   -0.0019
##    420        0.1570             nan     0.2000   -0.0002
##    440        0.1484             nan     0.2000   -0.0010
##    460        0.1401             nan     0.2000   -0.0010
##    480        0.1298             nan     0.2000   -0.0001
##    500        0.1224             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2030             nan     0.3000    0.0373
##      2        1.1451             nan     0.3000    0.0188
##      3        1.1163             nan     0.3000    0.0094
##      4        1.0825             nan     0.3000    0.0093
##      5        1.0509             nan     0.3000    0.0083
##      6        1.0211             nan     0.3000    0.0159
##      7        1.0008             nan     0.3000    0.0076
##      8        0.9915             nan     0.3000   -0.0004
##      9        0.9806             nan     0.3000    0.0016
##     10        0.9716             nan     0.3000   -0.0012
##     20        0.9009             nan     0.3000    0.0007
##     40        0.8310             nan     0.3000   -0.0042
##     60        0.8035             nan     0.3000   -0.0013
##     80        0.7835             nan     0.3000   -0.0008
##    100        0.7705             nan     0.3000   -0.0037
##    120        0.7559             nan     0.3000   -0.0065
##    140        0.7297             nan     0.3000   -0.0022
##    160        0.7114             nan     0.3000   -0.0021
##    180        0.6966             nan     0.3000   -0.0035
##    200        0.6829             nan     0.3000   -0.0023
##    220        0.6677             nan     0.3000   -0.0037
##    240        0.6596             nan     0.3000   -0.0009
##    260        0.6476             nan     0.3000   -0.0031
##    280        0.6368             nan     0.3000   -0.0044
##    300        0.6293             nan     0.3000   -0.0018
##    320        0.6178             nan     0.3000   -0.0011
##    340        0.6027             nan     0.3000   -0.0041
##    360        0.5983             nan     0.3000   -0.0017
##    380        0.5866             nan     0.3000   -0.0028
##    400        0.5807             nan     0.3000   -0.0033
##    420        0.5690             nan     0.3000   -0.0040
##    440        0.5617             nan     0.3000   -0.0011
##    460        0.5571             nan     0.3000   -0.0031
##    480        0.5492             nan     0.3000   -0.0005
##    500        0.5462             nan     0.3000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1994             nan     0.3000    0.0383
##      2        1.1580             nan     0.3000    0.0188
##      3        1.1063             nan     0.3000    0.0217
##      4        1.0716             nan     0.3000    0.0154
##      5        1.0464             nan     0.3000    0.0112
##      6        1.0289             nan     0.3000    0.0030
##      7        1.0039             nan     0.3000    0.0054
##      8        0.9854             nan     0.3000    0.0063
##      9        0.9742             nan     0.3000   -0.0001
##     10        0.9561             nan     0.3000    0.0069
##     20        0.8826             nan     0.3000   -0.0030
##     40        0.8326             nan     0.3000   -0.0036
##     60        0.7972             nan     0.3000   -0.0023
##     80        0.7702             nan     0.3000   -0.0025
##    100        0.7558             nan     0.3000   -0.0021
##    120        0.7349             nan     0.3000   -0.0041
##    140        0.7213             nan     0.3000   -0.0021
##    160        0.6967             nan     0.3000   -0.0041
##    180        0.6870             nan     0.3000   -0.0058
##    200        0.6743             nan     0.3000   -0.0022
##    220        0.6633             nan     0.3000   -0.0017
##    240        0.6537             nan     0.3000   -0.0034
##    260        0.6422             nan     0.3000   -0.0024
##    280        0.6375             nan     0.3000   -0.0060
##    300        0.6271             nan     0.3000   -0.0040
##    320        0.6198             nan     0.3000   -0.0025
##    340        0.6045             nan     0.3000   -0.0005
##    360        0.6001             nan     0.3000   -0.0026
##    380        0.5916             nan     0.3000   -0.0019
##    400        0.5845             nan     0.3000   -0.0019
##    420        0.5810             nan     0.3000    0.0003
##    440        0.5706             nan     0.3000   -0.0016
##    460        0.5638             nan     0.3000   -0.0015
##    480        0.5616             nan     0.3000   -0.0031
##    500        0.5534             nan     0.3000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1948             nan     0.3000    0.0419
##      2        1.1401             nan     0.3000    0.0232
##      3        1.1038             nan     0.3000    0.0176
##      4        1.0743             nan     0.3000    0.0082
##      5        1.0357             nan     0.3000    0.0132
##      6        1.0126             nan     0.3000    0.0067
##      7        0.9991             nan     0.3000    0.0009
##      8        0.9859             nan     0.3000    0.0038
##      9        0.9684             nan     0.3000    0.0035
##     10        0.9522             nan     0.3000    0.0047
##     20        0.8846             nan     0.3000   -0.0026
##     40        0.8171             nan     0.3000   -0.0022
##     60        0.7870             nan     0.3000   -0.0007
##     80        0.7586             nan     0.3000   -0.0010
##    100        0.7455             nan     0.3000   -0.0007
##    120        0.7276             nan     0.3000   -0.0051
##    140        0.7131             nan     0.3000   -0.0028
##    160        0.6984             nan     0.3000   -0.0018
##    180        0.6857             nan     0.3000   -0.0023
##    200        0.6778             nan     0.3000   -0.0044
##    220        0.6656             nan     0.3000   -0.0047
##    240        0.6515             nan     0.3000   -0.0024
##    260        0.6437             nan     0.3000   -0.0027
##    280        0.6335             nan     0.3000   -0.0034
##    300        0.6251             nan     0.3000   -0.0023
##    320        0.6169             nan     0.3000   -0.0016
##    340        0.6110             nan     0.3000   -0.0025
##    360        0.6045             nan     0.3000   -0.0021
##    380        0.5963             nan     0.3000   -0.0027
##    400        0.5923             nan     0.3000   -0.0016
##    420        0.5848             nan     0.3000   -0.0057
##    440        0.5776             nan     0.3000   -0.0033
##    460        0.5723             nan     0.3000   -0.0035
##    480        0.5645             nan     0.3000   -0.0040
##    500        0.5607             nan     0.3000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1852             nan     0.3000    0.0545
##      2        1.0894             nan     0.3000    0.0458
##      3        1.0433             nan     0.3000    0.0182
##      4        1.0215             nan     0.3000    0.0045
##      5        0.9944             nan     0.3000    0.0011
##      6        0.9618             nan     0.3000    0.0078
##      7        0.9367             nan     0.3000    0.0067
##      8        0.9253             nan     0.3000   -0.0009
##      9        0.9048             nan     0.3000    0.0031
##     10        0.8945             nan     0.3000   -0.0008
##     20        0.8068             nan     0.3000   -0.0034
##     40        0.7332             nan     0.3000   -0.0062
##     60        0.6815             nan     0.3000   -0.0053
##     80        0.6216             nan     0.3000   -0.0027
##    100        1.2472             nan     0.3000   -0.0014
##    120        1.2267             nan     0.3000   -0.0007
##    140        1.1739             nan     0.3000   -0.0022
##    160        1.1577             nan     0.3000   -0.0000
##    180        1.1399             nan     0.3000   -0.0014
##    200        1.1259             nan     0.3000   -0.0028
##    220        1.1023             nan     0.3000   -0.0030
##    240        1.0910             nan     0.3000   -0.0019
##    260        1.0806             nan     0.3000   -0.0022
##    280        1.0577             nan     0.3000   -0.0009
##    300        1.0467             nan     0.3000   -0.0005
##    320        1.0389             nan     0.3000   -0.0019
##    340        0.3961             nan     0.3000   -0.0011
##    360        0.3655             nan     0.3000   -0.0008
##    380        0.3451             nan     0.3000   -0.0036
##    400        0.3217             nan     0.3000   -0.0011
##    420        0.2989             nan     0.3000   -0.0014
##    440        0.2816             nan     0.3000   -0.0009
##    460        0.2669             nan     0.3000   -0.0033
##    480           inf             nan     0.3000      -inf
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1722             nan     0.3000    0.0463
##      2        1.0913             nan     0.3000    0.0395
##      3        1.0300             nan     0.3000    0.0166
##      4        0.9903             nan     0.3000    0.0105
##      5        0.9700             nan     0.3000    0.0035
##      6        0.9474             nan     0.3000    0.0059
##      7        0.9299             nan     0.3000    0.0045
##      8        0.9115             nan     0.3000    0.0016
##      9        0.9029             nan     0.3000   -0.0039
##     10        0.8920             nan     0.3000   -0.0019
##     20        0.7993             nan     0.3000   -0.0031
##     40        0.7068             nan     0.3000   -0.0050
##     60        0.6428             nan     0.3000   -0.0013
##     80        0.5892             nan     0.3000   -0.0028
##    100        0.5511             nan     0.3000   -0.0054
##    120        0.4967             nan     0.3000   -0.0011
##    140        0.4594             nan     0.3000   -0.0030
##    160        0.4317             nan     0.3000   -0.0018
##    180        0.4096             nan     0.3000   -0.0031
##    200        0.3847             nan     0.3000    0.0001
##    220        0.3649             nan     0.3000   -0.0053
##    240        0.3452             nan     0.3000   -0.0013
##    260        0.3265             nan     0.3000   -0.0024
##    280        0.3099             nan     0.3000   -0.0021
##    300        0.2930             nan     0.3000   -0.0028
##    320        0.2742             nan     0.3000   -0.0018
##    340        0.2560             nan     0.3000   -0.0014
##    360        0.2408             nan     0.3000   -0.0019
##    380        0.2307             nan     0.3000   -0.0011
##    400        0.2129             nan     0.3000   -0.0017
##    420        0.2011             nan     0.3000   -0.0013
##    440        0.1918             nan     0.3000   -0.0004
##    460        0.1821             nan     0.3000   -0.0022
##    480        0.1732             nan     0.3000   -0.0001
##    500        0.1641             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1744             nan     0.3000    0.0534
##      2        1.0866             nan     0.3000    0.0348
##      3        1.0383             nan     0.3000    0.0201
##      4        0.9995             nan     0.3000    0.0176
##      5        0.9666             nan     0.3000    0.0138
##      6        0.9403             nan     0.3000    0.0068
##      7        0.9168             nan     0.3000    0.0066
##      8        0.9062             nan     0.3000   -0.0022
##      9        0.9004             nan     0.3000   -0.0071
##     10        0.8935             nan     0.3000   -0.0095
##     20        0.7978             nan     0.3000   -0.0013
##     40        0.6967             nan     0.3000   -0.0083
##     60        0.6405             nan     0.3000   -0.0028
##     80        0.5877             nan     0.3000   -0.0043
##    100        0.5427             nan     0.3000   -0.0046
##    120        0.5076             nan     0.3000   -0.0052
##    140        0.4735             nan     0.3000   -0.0038
##    160        0.4418             nan     0.3000   -0.0035
##    180        0.4126             nan     0.3000   -0.0034
##    200        0.3884             nan     0.3000   -0.0017
##    220        0.3676             nan     0.3000   -0.0032
##    240        0.3465             nan     0.3000   -0.0037
##    260        0.3258             nan     0.3000   -0.0017
##    280        0.3070             nan     0.3000   -0.0029
##    300        0.2895             nan     0.3000   -0.0015
##    320        0.2758             nan     0.3000   -0.0016
##    340        0.2612             nan     0.3000   -0.0020
##    360        0.2455             nan     0.3000   -0.0013
##    380        0.2294             nan     0.3000   -0.0016
##    400        0.2147             nan     0.3000   -0.0008
##    420        0.2034             nan     0.3000   -0.0016
##    440        0.1916             nan     0.3000   -0.0024
##    460        0.1814             nan     0.3000   -0.0012
##    480        0.1718             nan     0.3000   -0.0027
##    500        0.1649             nan     0.3000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1407             nan     0.3000    0.0647
##      2        1.0593             nan     0.3000    0.0354
##      3        0.9999             nan     0.3000    0.0214
##      4        0.9622             nan     0.3000    0.0104
##      5        0.9295             nan     0.3000    0.0107
##      6        0.9059             nan     0.3000    0.0045
##      7        0.8886             nan     0.3000    0.0008
##      8        0.8695             nan     0.3000   -0.0029
##      9        0.8503             nan     0.3000    0.0021
##     10        0.8417             nan     0.3000   -0.0020
##     20        0.7401             nan     0.3000   -0.0077
##     40        0.6307             nan     0.3000   -0.0067
##     60        0.5491             nan     0.3000   -0.0073
##     80        0.4746             nan     0.3000   -0.0027
##    100        0.4056             nan     0.3000   -0.0030
##    120        0.3557             nan     0.3000   -0.0018
##    140        0.3111             nan     0.3000   -0.0034
##    160        0.2878             nan     0.3000   -0.0047
##    180        0.2520             nan     0.3000   -0.0007
##    200        0.2240             nan     0.3000   -0.0017
##    220        0.2031             nan     0.3000   -0.0008
##    240        0.1792             nan     0.3000    0.0004
##    260        0.1628             nan     0.3000   -0.0004
##    280        0.1490             nan     0.3000   -0.0015
##    300        0.1381             nan     0.3000   -0.0006
##    320        0.1242             nan     0.3000   -0.0008
##    340        0.1162             nan     0.3000   -0.0011
##    360        0.1056             nan     0.3000   -0.0002
##    380        0.0958             nan     0.3000   -0.0005
##    400        0.0867             nan     0.3000   -0.0000
##    420        0.0813             nan     0.3000   -0.0015
##    440        0.0742             nan     0.3000   -0.0004
##    460        0.0694             nan     0.3000   -0.0005
##    480        0.0641             nan     0.3000   -0.0004
##    500        0.0595             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1586             nan     0.3000    0.0570
##      2        1.0711             nan     0.3000    0.0398
##      3        1.0177             nan     0.3000    0.0119
##      4        0.9729             nan     0.3000    0.0140
##      5        0.9482             nan     0.3000   -0.0012
##      6        0.9195             nan     0.3000    0.0104
##      7        0.8944             nan     0.3000    0.0042
##      8        0.8837             nan     0.3000   -0.0066
##      9        0.8675             nan     0.3000   -0.0039
##     10        0.8527             nan     0.3000   -0.0010
##     20        0.7414             nan     0.3000   -0.0024
##     40        0.6376             nan     0.3000   -0.0057
##     60        0.5519             nan     0.3000   -0.0065
##     80        0.4744             nan     0.3000   -0.0035
##    100        0.4289             nan     0.3000   -0.0044
##    120        0.3733             nan     0.3000   -0.0037
##    140        0.3170             nan     0.3000   -0.0013
##    160        0.2833             nan     0.3000   -0.0034
##    180        0.2553             nan     0.3000   -0.0020
##    200        0.2261             nan     0.3000   -0.0025
##    220        0.2028             nan     0.3000   -0.0014
##    240        0.1869             nan     0.3000   -0.0021
##    260        0.1667             nan     0.3000   -0.0004
##    280        0.1503             nan     0.3000   -0.0004
##    300        0.1380             nan     0.3000   -0.0012
##    320        0.1250             nan     0.3000   -0.0010
##    340        0.1133             nan     0.3000   -0.0006
##    360        0.1027             nan     0.3000   -0.0006
##    380        0.0934             nan     0.3000   -0.0009
##    400        0.0837             nan     0.3000   -0.0005
##    420        0.0780             nan     0.3000   -0.0004
##    440        0.0721             nan     0.3000   -0.0005
##    460        0.0658             nan     0.3000   -0.0004
##    480        0.0596             nan     0.3000   -0.0004
##    500        0.0547             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1639             nan     0.3000    0.0568
##      2        1.0672             nan     0.3000    0.0384
##      3        1.0076             nan     0.3000    0.0237
##      4        0.9690             nan     0.3000    0.0049
##      5        0.9305             nan     0.3000    0.0169
##      6        0.9111             nan     0.3000    0.0049
##      7        0.8838             nan     0.3000    0.0100
##      8        0.8628             nan     0.3000   -0.0023
##      9        0.8540             nan     0.3000   -0.0084
##     10        0.8483             nan     0.3000   -0.0065
##     20        0.7672             nan     0.3000   -0.0073
##     40        0.6502             nan     0.3000   -0.0025
##     60        0.5610             nan     0.3000   -0.0020
##     80        0.4851             nan     0.3000   -0.0044
##    100        0.4201             nan     0.3000   -0.0009
##    120        0.3746             nan     0.3000   -0.0029
##    140        0.3403             nan     0.3000   -0.0046
##    160        0.3032             nan     0.3000   -0.0016
##    180        0.2588             nan     0.3000   -0.0002
##    200        0.2352             nan     0.3000   -0.0013
##    220        0.2128             nan     0.3000   -0.0015
##    240        0.1925             nan     0.3000   -0.0011
##    260        0.1747             nan     0.3000   -0.0003
##    280        0.1604             nan     0.3000   -0.0029
##    300        0.1456             nan     0.3000   -0.0011
##    320        0.1319             nan     0.3000   -0.0018
##    340        0.1212             nan     0.3000   -0.0005
##    360        0.1097             nan     0.3000   -0.0019
##    380        0.1013             nan     0.3000   -0.0003
##    400        0.0924             nan     0.3000   -0.0012
##    420        0.0858             nan     0.3000   -0.0016
##    440        0.0788             nan     0.3000   -0.0001
##    460        0.0710             nan     0.3000   -0.0003
##    480        0.0641             nan     0.3000   -0.0002
##    500        0.0592             nan     0.3000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1554             nan     0.5000    0.0580
##      2        1.1020             nan     0.5000    0.0247
##      3        1.0488             nan     0.5000    0.0145
##      4        1.0175             nan     0.5000    0.0102
##      5        0.9929             nan     0.5000    0.0103
##      6        0.9674             nan     0.5000    0.0057
##      7        0.9485             nan     0.5000    0.0003
##      8        0.9368             nan     0.5000    0.0004
##      9        0.9253             nan     0.5000    0.0007
##     10        0.9106             nan     0.5000    0.0006
##     20        0.8604             nan     0.5000   -0.0021
##     40        0.7985             nan     0.5000   -0.0080
##     60        0.7688             nan     0.5000   -0.0082
##     80        0.7321             nan     0.5000   -0.0013
##    100        0.7015             nan     0.5000   -0.0024
##    120        0.6906             nan     0.5000   -0.0024
##    140        0.6768             nan     0.5000   -0.0041
##    160        0.6555             nan     0.5000    0.0008
##    180        0.6451             nan     0.5000   -0.0059
##    200        0.6298             nan     0.5000   -0.0079
##    220        0.6141             nan     0.5000   -0.0023
##    240        0.6027             nan     0.5000   -0.0028
##    260        0.5924             nan     0.5000   -0.0051
##    280        0.5772             nan     0.5000   -0.0094
##    300        0.5655             nan     0.5000   -0.0016
##    320        0.5478             nan     0.5000   -0.0069
##    340        0.5398             nan     0.5000    0.0055
##    360        0.5282             nan     0.5000   -0.0023
##    380        0.5114             nan     0.5000   -0.0025
##    400        0.5106             nan     0.5000   -0.0035
##    420        0.4956             nan     0.5000   -0.0039
##    440        0.4939             nan     0.5000   -0.0072
##    460        0.4892             nan     0.5000   -0.0057
##    480        0.4833             nan     0.5000   -0.0046
##    500        0.4637             nan     0.5000   -0.0042
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1678             nan     0.5000    0.0642
##      2        1.1045             nan     0.5000    0.0157
##      3        1.0502             nan     0.5000    0.0181
##      4        1.0069             nan     0.5000    0.0179
##      5        0.9842             nan     0.5000    0.0050
##      6        0.9670             nan     0.5000    0.0004
##      7        0.9540             nan     0.5000   -0.0026
##      8        0.9377             nan     0.5000    0.0015
##      9        0.9242             nan     0.5000    0.0013
##     10        0.9136             nan     0.5000    0.0002
##     20        0.8673             nan     0.5000   -0.0096
##     40        0.8017             nan     0.5000   -0.0066
##     60        0.7644             nan     0.5000   -0.0015
##     80        0.7362             nan     0.5000   -0.0050
##    100        0.7210             nan     0.5000   -0.0107
##    120        0.6855             nan     0.5000   -0.0020
##    140        0.6775             nan     0.5000   -0.0076
##    160        0.6560             nan     0.5000   -0.0059
##    180        0.6379             nan     0.5000   -0.0086
##    200        0.6321             nan     0.5000   -0.0027
##    220        0.6202             nan     0.5000   -0.0042
##    240        0.6030             nan     0.5000   -0.0060
##    260        0.5949             nan     0.5000   -0.0161
##    280        0.5773             nan     0.5000   -0.0017
##    300        0.5653             nan     0.5000   -0.0034
##    320        1.1336             nan     0.5000   -0.0005
##    340 83384773687.4878             nan     0.5000   -0.0000
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440 2057629316492615546448660.0000             nan     0.5000 -23463.1675
##    460 2057629316492615546448660.0000             nan     0.5000    0.0000
##    480 2057629316492615546448660.0000             nan     0.5000    0.0003
##    500 2057629316492615546448660.0000             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1449             nan     0.5000    0.0476
##      2        1.0776             nan     0.5000    0.0187
##      3        1.0283             nan     0.5000    0.0212
##      4        0.9924             nan     0.5000    0.0155
##      5        0.9774             nan     0.5000    0.0017
##      6        0.9578             nan     0.5000    0.0038
##      7        0.9478             nan     0.5000    0.0007
##      8        0.9431             nan     0.5000   -0.0056
##      9        0.9318             nan     0.5000   -0.0062
##     10        0.9118             nan     0.5000    0.0038
##     20        0.8583             nan     0.5000   -0.0050
##     40        0.8031             nan     0.5000   -0.0040
##     60        0.7519             nan     0.5000   -0.0017
##     80        0.7309             nan     0.5000   -0.0054
##    100        0.6950             nan     0.5000   -0.0015
##    120        0.6877             nan     0.5000   -0.0002
##    140        0.6645             nan     0.5000   -0.0054
##    160        0.6416             nan     0.5000    0.0016
##    180        0.6281             nan     0.5000   -0.0034
##    200        0.6267             nan     0.5000   -0.0052
##    220        0.6036             nan     0.5000   -0.0049
##    240        0.5926             nan     0.5000   -0.0101
##    260        0.5794             nan     0.5000    0.0007
##    280        0.5737             nan     0.5000   -0.0069
##    300        0.5634             nan     0.5000   -0.0040
##    320        0.5546             nan     0.5000   -0.0062
##    340        0.5464             nan     0.5000   -0.0079
##    360        0.5329             nan     0.5000   -0.0038
##    380        0.5332             nan     0.5000   -0.0063
##    400        0.5291             nan     0.5000   -0.0051
##    420        0.5176             nan     0.5000   -0.0019
##    440        0.5130             nan     0.5000   -0.0075
##    460        0.5056             nan     0.5000   -0.0040
##    480        0.4972             nan     0.5000   -0.0102
##    500        0.4884             nan     0.5000   -0.0059
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1103             nan     0.5000    0.0505
##      2        1.0278             nan     0.5000    0.0363
##      3        0.9739             nan     0.5000    0.0106
##      4        0.9277             nan     0.5000    0.0170
##      5        0.9060             nan     0.5000    0.0019
##      6        0.8945             nan     0.5000   -0.0095
##      7        0.8749             nan     0.5000   -0.0017
##      8        0.8540             nan     0.5000   -0.0026
##      9        0.8532             nan     0.5000   -0.0179
##     10        0.8402             nan     0.5000   -0.0014
##     20        0.7885             nan     0.5000   -0.0073
##     40        0.7477             nan     0.5000   -0.0071
##     60        1.3496             nan     0.5000   -0.0014
##     80        1.2614             nan     0.5000   -0.0082
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1173             nan     0.5000    0.0766
##      2        1.0388             nan     0.5000    0.0337
##      3        0.9878             nan     0.5000    0.0172
##      4        0.9563             nan     0.5000    0.0084
##      5        0.9329             nan     0.5000    0.0021
##      6        0.9113             nan     0.5000   -0.0079
##      7        0.8947             nan     0.5000   -0.0080
##      8        0.8867             nan     0.5000   -0.0103
##      9        0.8595             nan     0.5000   -0.0022
##     10        0.8537             nan     0.5000   -0.0069
##     20        0.7908             nan     0.5000   -0.0026
##     40        0.6608             nan     0.5000   -0.0064
##     60        0.6018             nan     0.5000   -0.0038
##     80        0.5409             nan     0.5000   -0.0091
##    100        0.4762             nan     0.5000   -0.0068
##    120        0.4322             nan     0.5000   -0.0048
##    140        0.3888             nan     0.5000   -0.0066
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1204             nan     0.5000    0.0828
##      2        1.0218             nan     0.5000    0.0424
##      3        0.9780             nan     0.5000    0.0083
##      4        0.9385             nan     0.5000    0.0057
##      5        0.9089             nan     0.5000    0.0035
##      6        0.8935             nan     0.5000   -0.0060
##      7        0.8701             nan     0.5000    0.0064
##      8        0.8595             nan     0.5000   -0.0015
##      9        0.8448             nan     0.5000    0.0013
##     10        0.8380             nan     0.5000   -0.0020
##     20        0.7715             nan     0.5000   -0.0026
##     40        0.7002             nan     0.5000   -0.0060
##     60        0.6303             nan     0.5000   -0.0122
##     80        0.5535             nan     0.5000   -0.0044
##    100        0.4908             nan     0.5000   -0.0029
##    120        0.4407             nan     0.5000   -0.0060
##    140        0.3923             nan     0.5000   -0.0051
##    160        0.3487             nan     0.5000   -0.0032
##    180      160.2560             nan     0.5000   -0.0057
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0908             nan     0.5000    0.1030
##      2        0.9874             nan     0.5000    0.0411
##      3        0.9432             nan     0.5000    0.0058
##      4        0.9146             nan     0.5000   -0.0144
##      5        0.8902             nan     0.5000    0.0005
##      6        0.8578             nan     0.5000    0.0030
##      7        0.8315             nan     0.5000   -0.0047
##      8        0.8095             nan     0.5000   -0.0018
##      9        0.7978             nan     0.5000   -0.0058
##     10        0.7742             nan     0.5000    0.0057
##     20        0.7157             nan     0.5000   -0.0505
##     40        0.6226             nan     0.5000   -0.0105
##     60        5.5910             nan     0.5000   -0.0042
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1019             nan     0.5000    0.0770
##      2        0.9988             nan     0.5000    0.0510
##      3        0.9631             nan     0.5000    0.0031
##      4        0.9285             nan     0.5000    0.0013
##      5        0.9064             nan     0.5000    0.0011
##      6        0.8773             nan     0.5000    0.0008
##      7        0.8674             nan     0.5000   -0.0069
##      8        0.8479             nan     0.5000   -0.0016
##      9        0.8194             nan     0.5000    0.0058
##     10        0.8109             nan     0.5000   -0.0134
##     20        0.7064             nan     0.5000   -0.0084
##     40        0.5321             nan     0.5000   -0.0044
##     60        0.4483             nan     0.5000   -0.0112
##     80        0.3520             nan     0.5000   -0.0044
##    100        0.2885             nan     0.5000   -0.0026
##    120        0.2352             nan     0.5000   -0.0048
##    140        0.1842             nan     0.5000   -0.0038
##    160        0.1555             nan     0.5000   -0.0012
##    180        0.1336             nan     0.5000   -0.0005
##    200        0.1140             nan     0.5000   -0.0019
##    220        0.0977             nan     0.5000   -0.0008
##    240        0.0840             nan     0.5000   -0.0007
##    260        0.0744             nan     0.5000   -0.0018
##    280        0.0658             nan     0.5000   -0.0010
##    300        0.0582             nan     0.5000   -0.0007
##    320        0.0505             nan     0.5000   -0.0009
##    340        0.0447             nan     0.5000   -0.0003
##    360        0.0389             nan     0.5000   -0.0004
##    380        0.0345             nan     0.5000   -0.0006
##    400        0.0312             nan     0.5000   -0.0004
##    420        0.0292             nan     0.5000   -0.0008
##    440        0.0248             nan     0.5000   -0.0003
##    460        0.0219             nan     0.5000   -0.0004
##    480        0.0198             nan     0.5000   -0.0003
##    500        0.0171             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0947             nan     0.5000    0.1042
##      2        0.9885             nan     0.5000    0.0426
##      3        0.9478             nan     0.5000    0.0119
##      4        0.9151             nan     0.5000    0.0026
##      5        0.8799             nan     0.5000    0.0086
##      6        0.8513             nan     0.5000    0.0017
##      7        0.8383             nan     0.5000   -0.0101
##      8        0.8258             nan     0.5000   -0.0112
##      9        0.8123             nan     0.5000   -0.0041
##     10        0.8007             nan     0.5000   -0.0079
##     20        0.7015             nan     0.5000   -0.0108
##     40        0.5807             nan     0.5000   -0.0038
##     60        0.4848             nan     0.5000   -0.0078
##     80        0.4019             nan     0.5000   -0.0099
##    100        0.3465             nan     0.5000   -0.0039
##    120        0.2725             nan     0.5000   -0.0040
##    140        0.2395             nan     0.5000   -0.0006
##    160        0.1805             nan     0.5000   -0.0074
##    180        0.1454             nan     0.5000   -0.0028
##    200        0.1206             nan     0.5000   -0.0020
##    220        0.1000             nan     0.5000   -0.0005
##    240        0.0876             nan     0.5000   -0.0007
##    260        0.0746             nan     0.5000   -0.0013
##    280        0.0643             nan     0.5000   -0.0011
##    300        0.0556             nan     0.5000   -0.0022
##    320        0.0477             nan     0.5000   -0.0002
##    340        0.0406             nan     0.5000   -0.0003
##    360        0.0359             nan     0.5000   -0.0004
##    380        0.0323             nan     0.5000   -0.0005
##    400        0.0282             nan     0.5000   -0.0002
##    420        0.0252             nan     0.5000   -0.0002
##    440        0.0229             nan     0.5000   -0.0006
##    460        0.0204             nan     0.5000   -0.0002
##    480        0.0185             nan     0.5000   -0.0005
##    500        0.0161             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1228             nan     1.0000    0.0711
##      2        1.0716             nan     1.0000   -0.0005
##      3        1.0375             nan     1.0000    0.0050
##      4        1.0041             nan     1.0000    0.0082
##      5        1.0095             nan     1.0000   -0.0276
##      6        1.0020             nan     1.0000   -0.0176
##      7        0.9693             nan     1.0000    0.0169
##      8        0.9604             nan     1.0000   -0.0122
##      9        1.0494             nan     1.0000   -0.1044
##     10        1.0319             nan     1.0000    0.0014
##     20     8672.6516             nan     1.0000   -0.1412
##     40 82536747.5347             nan     1.0000   -0.0054
##     60 82536747.5419             nan     1.0000   -0.0516
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1556             nan     1.0000    0.0501
##      2        1.0650             nan     1.0000    0.0300
##      3        1.0012             nan     1.0000    0.0314
##      4        0.9441             nan     1.0000    0.0280
##      5        0.9299             nan     1.0000   -0.0067
##      6        0.9255             nan     1.0000   -0.0077
##      7        0.9350             nan     1.0000   -0.0183
##      8        0.9200             nan     1.0000    0.0038
##      9        0.9295             nan     1.0000   -0.0240
##     10        0.9198             nan     1.0000   -0.0039
##     20        0.8886             nan     1.0000   -0.0132
##     40        0.8197             nan     1.0000    0.0008
##     60        0.7890             nan     1.0000   -0.0013
##     80        0.7631             nan     1.0000   -0.0249
##    100        0.7520             nan     1.0000   -0.0115
##    120        0.6915             nan     1.0000   -0.0019
##    140        3.1163             nan     1.0000   -0.0082
##    160        3.1058             nan     1.0000   -0.0019
##    180        3.1117             nan     1.0000   -0.0004
##    200        3.0599             nan     1.0000   -0.0033
##    220        3.0518             nan     1.0000   -0.0047
##    240        3.0419             nan     1.0000    0.0022
##    260        3.0454             nan     1.0000   -0.0209
##    280        3.0229             nan     1.0000    0.0018
##    300        3.0225             nan     1.0000   -0.0298
##    320        3.0873             nan     1.0000    0.0020
##    340        2.7514             nan     1.0000    0.0024
##    360        2.7578             nan     1.0000   -0.0038
##    380        2.7305             nan     1.0000   -0.0004
##    400        2.7319             nan     1.0000   -0.0143
##    420        2.8077             nan     1.0000   -0.0010
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1234             nan     1.0000    0.0601
##      2        1.0455             nan     1.0000    0.0219
##      3        0.9994             nan     1.0000    0.0131
##      4        0.9811             nan     1.0000   -0.0022
##      5        0.9495             nan     1.0000    0.0138
##      6        0.9585             nan     1.0000   -0.0254
##      7        0.9680             nan     1.0000   -0.0227
##      8        0.9373             nan     1.0000   -0.0052
##      9        0.9265             nan     1.0000   -0.0058
##     10        0.9225             nan     1.0000   -0.0137
##     20           inf             nan     1.0000       nan
##     40     9667.4060             nan     1.0000   -0.0210
##     60     9667.3836             nan     1.0000   -0.0151
##     80     9667.3099             nan     1.0000   -0.0118
##    100     9667.2865             nan     1.0000   -0.0100
##    120     9667.2583             nan     1.0000    0.0001
##    140     9667.2500             nan     1.0000   -0.0324
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0741             nan     1.0000    0.0946
##      2        0.9690             nan     1.0000    0.0422
##      3        0.9448             nan     1.0000   -0.0174
##      4        0.9656             nan     1.0000   -0.0712
##      5        0.9663             nan     1.0000   -0.0333
##      6        0.9835             nan     1.0000   -0.0469
##      7        1.0388             nan     1.0000   -0.1105
##      8        0.9186             nan     1.0000   -0.0090
##      9        0.8826             nan     1.0000    0.0018
##     10        0.9116             nan     1.0000   -0.0464
##     20        0.9300             nan     1.0000   -0.0135
##     40           inf             nan     1.0000      -inf
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0548             nan     1.0000    0.0971
##      2        0.9746             nan     1.0000    0.0167
##      3        0.9573             nan     1.0000   -0.0068
##      4        0.9443             nan     1.0000   -0.0186
##      5        0.9067             nan     1.0000    0.0068
##      6        0.9038             nan     1.0000   -0.0237
##      7        0.9179             nan     1.0000   -0.0479
##      8        0.9178             nan     1.0000   -0.0254
##      9        0.9014             nan     1.0000   -0.0108
##     10        0.9081             nan     1.0000   -0.0531
##     20        0.8904             nan     1.0000   -0.0370
##     40        6.3216             nan     1.0000   -0.0523
##     60        8.1270             nan     1.0000   -0.0214
##     80        9.0762             nan     1.0000    0.0058
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0784             nan     1.0000    0.0552
##      2        1.0107             nan     1.0000    0.0166
##      3        0.9907             nan     1.0000   -0.0218
##      4        0.9679             nan     1.0000   -0.0288
##      5        1.1076             nan     1.0000   -0.1699
##      6        3.6409             nan     1.0000   -2.6389
##      7        3.6380             nan     1.0000   -0.0245
##      8        3.5963             nan     1.0000   -0.0037
##      9        3.6063             nan     1.0000   -0.0221
##     10        3.6760             nan     1.0000   -0.1221
##     20       45.6147             nan     1.0000   -2.7568
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0513             nan     1.0000    0.0539
##      2        0.9869             nan     1.0000    0.0016
##      3        0.9226             nan     1.0000    0.0067
##      4        0.9090             nan     1.0000   -0.0157
##      5        0.8854             nan     1.0000   -0.0190
##      6        0.8667             nan     1.0000   -0.0165
##      7        0.8604             nan     1.0000   -0.0375
##      8        0.8270             nan     1.0000   -0.0041
##      9        0.8160             nan     1.0000   -0.0158
##     10        0.7888             nan     1.0000   -0.0067
##     20           inf             nan     1.0000       inf
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0189             nan     1.0000    0.1050
##      2        0.9545             nan     1.0000    0.0047
##      3        0.9235             nan     1.0000   -0.0074
##      4        0.9104             nan     1.0000   -0.0361
##      5        0.8653             nan     1.0000   -0.0034
##      6        0.8511             nan     1.0000   -0.0372
##      7        0.8509             nan     1.0000   -0.0538
##      8        0.8958             nan     1.0000   -0.0619
##      9        0.8925             nan     1.0000   -0.0399
##     10        0.9032             nan     1.0000   -0.0682
##     20           inf             nan     1.0000      -inf
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0510             nan     1.0000    0.0792
##      2        0.9851             nan     1.0000    0.0125
##      3        0.9474             nan     1.0000   -0.0020
##      4        0.9358             nan     1.0000   -0.0178
##      5        0.9549             nan     1.0000   -0.0644
##      6        0.9648             nan     1.0000   -0.0540
##      7        0.9399             nan     1.0000   -0.0138
##      8        1.0775             nan     1.0000   -0.1676
##      9        1.0306             nan     1.0000   -0.0091
##     10        1.0144             nan     1.0000   -0.0452
##     20       45.5424             nan     1.0000   -0.0438
##     40 2607004125874.7700             nan     1.0000    0.2182
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2790             nan     0.0010    0.0002
##     60        1.2721             nan     0.0010    0.0002
##     80        1.2654             nan     0.0010    0.0002
##    100        1.2592             nan     0.0010    0.0001
##    120        1.2531             nan     0.0010    0.0001
##    140        1.2469             nan     0.0010    0.0001
##    160        1.2411             nan     0.0010    0.0001
##    180        1.2354             nan     0.0010    0.0001
##    200        1.2297             nan     0.0010    0.0001
##    220        1.2246             nan     0.0010    0.0001
##    240        1.2195             nan     0.0010    0.0001
##    260        1.2144             nan     0.0010    0.0001
##    280        1.2095             nan     0.0010    0.0001
##    300        1.2046             nan     0.0010    0.0001
##    320        1.2002             nan     0.0010    0.0001
##    340        1.1957             nan     0.0010    0.0001
##    360        1.1914             nan     0.0010    0.0001
##    380        1.1872             nan     0.0010    0.0001
##    400        1.1830             nan     0.0010    0.0001
##    420        1.1791             nan     0.0010    0.0001
##    440        1.1750             nan     0.0010    0.0001
##    460        1.1711             nan     0.0010    0.0001
##    480        1.1673             nan     0.0010    0.0001
##    500        1.1636             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2859             nan     0.0010    0.0002
##     40        1.2788             nan     0.0010    0.0002
##     60        1.2718             nan     0.0010    0.0002
##     80        1.2652             nan     0.0010    0.0001
##    100        1.2587             nan     0.0010    0.0001
##    120        1.2525             nan     0.0010    0.0001
##    140        1.2462             nan     0.0010    0.0001
##    160        1.2404             nan     0.0010    0.0001
##    180        1.2347             nan     0.0010    0.0001
##    200        1.2292             nan     0.0010    0.0001
##    220        1.2240             nan     0.0010    0.0001
##    240        1.2189             nan     0.0010    0.0001
##    260        1.2140             nan     0.0010    0.0001
##    280        1.2091             nan     0.0010    0.0001
##    300        1.2045             nan     0.0010    0.0001
##    320        1.1999             nan     0.0010    0.0001
##    340        1.1955             nan     0.0010    0.0001
##    360        1.1912             nan     0.0010    0.0001
##    380        1.1870             nan     0.0010    0.0001
##    400        1.1829             nan     0.0010    0.0001
##    420        1.1787             nan     0.0010    0.0001
##    440        1.1747             nan     0.0010    0.0001
##    460        1.1710             nan     0.0010    0.0001
##    480        1.1671             nan     0.0010    0.0001
##    500        1.1635             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2789             nan     0.0010    0.0002
##     60        1.2720             nan     0.0010    0.0001
##     80        1.2651             nan     0.0010    0.0001
##    100        1.2585             nan     0.0010    0.0002
##    120        1.2522             nan     0.0010    0.0002
##    140        1.2463             nan     0.0010    0.0001
##    160        1.2405             nan     0.0010    0.0001
##    180        1.2351             nan     0.0010    0.0001
##    200        1.2298             nan     0.0010    0.0001
##    220        1.2246             nan     0.0010    0.0001
##    240        1.2193             nan     0.0010    0.0001
##    260        1.2143             nan     0.0010    0.0001
##    280        1.2095             nan     0.0010    0.0001
##    300        1.2048             nan     0.0010    0.0001
##    320        1.2002             nan     0.0010    0.0001
##    340        1.1956             nan     0.0010    0.0001
##    360        1.1911             nan     0.0010    0.0001
##    380        1.1870             nan     0.0010    0.0001
##    400        1.1829             nan     0.0010    0.0001
##    420        1.1787             nan     0.0010    0.0001
##    440        1.1748             nan     0.0010    0.0001
##    460        1.1709             nan     0.0010    0.0001
##    480        1.1670             nan     0.0010    0.0001
##    500        1.1633             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2837             nan     0.0010    0.0002
##     40        1.2747             nan     0.0010    0.0002
##     60        1.2656             nan     0.0010    0.0002
##     80        1.2570             nan     0.0010    0.0002
##    100        1.2486             nan     0.0010    0.0001
##    120        1.2405             nan     0.0010    0.0001
##    140        1.2324             nan     0.0010    0.0002
##    160        1.2247             nan     0.0010    0.0002
##    180        1.2173             nan     0.0010    0.0001
##    200        1.2100             nan     0.0010    0.0002
##    220        1.2030             nan     0.0010    0.0002
##    240        1.1962             nan     0.0010    0.0001
##    260        1.1897             nan     0.0010    0.0001
##    280        1.1834             nan     0.0010    0.0001
##    300        1.1772             nan     0.0010    0.0001
##    320        1.1712             nan     0.0010    0.0001
##    340        1.1652             nan     0.0010    0.0001
##    360        1.1595             nan     0.0010    0.0001
##    380        1.1539             nan     0.0010    0.0001
##    400        1.1484             nan     0.0010    0.0001
##    420        1.1430             nan     0.0010    0.0001
##    440        1.1378             nan     0.0010    0.0001
##    460        1.1327             nan     0.0010    0.0001
##    480        1.1275             nan     0.0010    0.0001
##    500        1.1225             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2889             nan     0.0010    0.0002
##     10        1.2884             nan     0.0010    0.0002
##     20        1.2838             nan     0.0010    0.0002
##     40        1.2744             nan     0.0010    0.0002
##     60        1.2656             nan     0.0010    0.0002
##     80        1.2571             nan     0.0010    0.0002
##    100        1.2488             nan     0.0010    0.0002
##    120        1.2407             nan     0.0010    0.0002
##    140        1.2327             nan     0.0010    0.0002
##    160        1.2252             nan     0.0010    0.0002
##    180        1.2179             nan     0.0010    0.0002
##    200        1.2105             nan     0.0010    0.0001
##    220        1.2036             nan     0.0010    0.0001
##    240        1.1967             nan     0.0010    0.0001
##    260        1.1901             nan     0.0010    0.0002
##    280        1.1836             nan     0.0010    0.0001
##    300        1.1773             nan     0.0010    0.0001
##    320        1.1711             nan     0.0010    0.0001
##    340        1.1651             nan     0.0010    0.0001
##    360        1.1592             nan     0.0010    0.0001
##    380        1.1536             nan     0.0010    0.0001
##    400        1.1483             nan     0.0010    0.0001
##    420        1.1430             nan     0.0010    0.0001
##    440        1.1379             nan     0.0010    0.0001
##    460        1.1326             nan     0.0010    0.0001
##    480        1.1275             nan     0.0010    0.0001
##    500        1.1225             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2916             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2902             nan     0.0010    0.0002
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2841             nan     0.0010    0.0002
##     40        1.2748             nan     0.0010    0.0002
##     60        1.2657             nan     0.0010    0.0002
##     80        1.2571             nan     0.0010    0.0002
##    100        1.2487             nan     0.0010    0.0002
##    120        1.2405             nan     0.0010    0.0002
##    140        1.2328             nan     0.0010    0.0001
##    160        1.2251             nan     0.0010    0.0001
##    180        1.2177             nan     0.0010    0.0002
##    200        1.2104             nan     0.0010    0.0001
##    220        1.2034             nan     0.0010    0.0002
##    240        1.1967             nan     0.0010    0.0001
##    260        1.1900             nan     0.0010    0.0002
##    280        1.1834             nan     0.0010    0.0001
##    300        1.1772             nan     0.0010    0.0001
##    320        1.1710             nan     0.0010    0.0001
##    340        1.1651             nan     0.0010    0.0001
##    360        1.1593             nan     0.0010    0.0001
##    380        1.1537             nan     0.0010    0.0001
##    400        1.1482             nan     0.0010    0.0001
##    420        1.1428             nan     0.0010    0.0001
##    440        1.1376             nan     0.0010    0.0001
##    460        1.1325             nan     0.0010    0.0001
##    480        1.1274             nan     0.0010    0.0001
##    500        1.1226             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0003
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0002
##     10        1.2880             nan     0.0010    0.0003
##     20        1.2826             nan     0.0010    0.0002
##     40        1.2717             nan     0.0010    0.0002
##     60        1.2611             nan     0.0010    0.0003
##     80        1.2512             nan     0.0010    0.0002
##    100        1.2415             nan     0.0010    0.0003
##    120        1.2323             nan     0.0010    0.0002
##    140        1.2233             nan     0.0010    0.0002
##    160        1.2143             nan     0.0010    0.0002
##    180        1.2055             nan     0.0010    0.0002
##    200        1.1970             nan     0.0010    0.0001
##    220        1.1888             nan     0.0010    0.0002
##    240        1.1810             nan     0.0010    0.0002
##    260        1.1737             nan     0.0010    0.0001
##    280        1.1663             nan     0.0010    0.0002
##    300        1.1592             nan     0.0010    0.0001
##    320        1.1523             nan     0.0010    0.0001
##    340        1.1455             nan     0.0010    0.0001
##    360        1.1390             nan     0.0010    0.0001
##    380        1.1325             nan     0.0010    0.0001
##    400        1.1264             nan     0.0010    0.0001
##    420        1.1204             nan     0.0010    0.0001
##    440        1.1143             nan     0.0010    0.0001
##    460        1.1085             nan     0.0010    0.0001
##    480        1.1030             nan     0.0010    0.0001
##    500        1.0976             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0003
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2895             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0002
##     20        1.2823             nan     0.0010    0.0002
##     40        1.2714             nan     0.0010    0.0003
##     60        1.2611             nan     0.0010    0.0002
##     80        1.2510             nan     0.0010    0.0002
##    100        1.2409             nan     0.0010    0.0002
##    120        1.2317             nan     0.0010    0.0002
##    140        1.2227             nan     0.0010    0.0002
##    160        1.2142             nan     0.0010    0.0001
##    180        1.2058             nan     0.0010    0.0002
##    200        1.1975             nan     0.0010    0.0002
##    220        1.1892             nan     0.0010    0.0002
##    240        1.1812             nan     0.0010    0.0002
##    260        1.1737             nan     0.0010    0.0001
##    280        1.1664             nan     0.0010    0.0002
##    300        1.1592             nan     0.0010    0.0002
##    320        1.1522             nan     0.0010    0.0002
##    340        1.1453             nan     0.0010    0.0002
##    360        1.1387             nan     0.0010    0.0001
##    380        1.1323             nan     0.0010    0.0001
##    400        1.1262             nan     0.0010    0.0001
##    420        1.1199             nan     0.0010    0.0001
##    440        1.1140             nan     0.0010    0.0001
##    460        1.1082             nan     0.0010    0.0001
##    480        1.1027             nan     0.0010    0.0001
##    500        1.0971             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2916             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2905             nan     0.0010    0.0003
##      6        1.2899             nan     0.0010    0.0003
##      7        1.2894             nan     0.0010    0.0003
##      8        1.2889             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0002
##     20        1.2824             nan     0.0010    0.0003
##     40        1.2715             nan     0.0010    0.0002
##     60        1.2609             nan     0.0010    0.0003
##     80        1.2510             nan     0.0010    0.0002
##    100        1.2414             nan     0.0010    0.0002
##    120        1.2322             nan     0.0010    0.0002
##    140        1.2229             nan     0.0010    0.0002
##    160        1.2144             nan     0.0010    0.0002
##    180        1.2058             nan     0.0010    0.0002
##    200        1.1975             nan     0.0010    0.0002
##    220        1.1892             nan     0.0010    0.0002
##    240        1.1813             nan     0.0010    0.0001
##    260        1.1734             nan     0.0010    0.0001
##    280        1.1662             nan     0.0010    0.0001
##    300        1.1590             nan     0.0010    0.0002
##    320        1.1521             nan     0.0010    0.0002
##    340        1.1454             nan     0.0010    0.0001
##    360        1.1392             nan     0.0010    0.0001
##    380        1.1329             nan     0.0010    0.0001
##    400        1.1268             nan     0.0010    0.0002
##    420        1.1206             nan     0.0010    0.0001
##    440        1.1148             nan     0.0010    0.0001
##    460        1.1089             nan     0.0010    0.0001
##    480        1.1033             nan     0.0010    0.0001
##    500        1.0977             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2598             nan     0.1000    0.0169
##      2        1.2302             nan     0.1000    0.0151
##      3        1.2052             nan     0.1000    0.0125
##      4        1.1820             nan     0.1000    0.0096
##      5        1.1634             nan     0.1000    0.0074
##      6        1.1457             nan     0.1000    0.0069
##      7        1.1317             nan     0.1000    0.0063
##      8        1.1194             nan     0.1000    0.0040
##      9        1.1036             nan     0.1000    0.0068
##     10        1.0913             nan     0.1000    0.0053
##     20        1.0006             nan     0.1000    0.0013
##     40        0.9128             nan     0.1000    0.0012
##     60        0.8684             nan     0.1000   -0.0003
##     80        0.8436             nan     0.1000   -0.0002
##    100        0.8273             nan     0.1000   -0.0013
##    120        0.8128             nan     0.1000   -0.0009
##    140        0.7993             nan     0.1000   -0.0014
##    160        0.7857             nan     0.1000   -0.0004
##    180        0.7738             nan     0.1000   -0.0016
##    200        0.7678             nan     0.1000   -0.0013
##    220        0.7587             nan     0.1000   -0.0013
##    240        0.7517             nan     0.1000   -0.0009
##    260        0.7466             nan     0.1000   -0.0005
##    280        0.7423             nan     0.1000   -0.0009
##    300        0.7345             nan     0.1000   -0.0005
##    320        0.7293             nan     0.1000   -0.0007
##    340        0.7232             nan     0.1000   -0.0010
##    360        0.7188             nan     0.1000   -0.0000
##    380        0.7133             nan     0.1000   -0.0007
##    400        0.7075             nan     0.1000   -0.0004
##    420        0.7025             nan     0.1000   -0.0008
##    440        0.6959             nan     0.1000   -0.0006
##    460        0.6910             nan     0.1000   -0.0004
##    480        0.6869             nan     0.1000   -0.0016
##    500        0.6825             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2568             nan     0.1000    0.0178
##      2        1.2250             nan     0.1000    0.0136
##      3        1.1989             nan     0.1000    0.0106
##      4        1.1789             nan     0.1000    0.0089
##      5        1.1555             nan     0.1000    0.0069
##      6        1.1399             nan     0.1000    0.0063
##      7        1.1248             nan     0.1000    0.0069
##      8        1.1096             nan     0.1000    0.0066
##      9        1.0968             nan     0.1000    0.0066
##     10        1.0838             nan     0.1000    0.0051
##     20        0.9944             nan     0.1000    0.0007
##     40        0.9138             nan     0.1000    0.0001
##     60        0.8731             nan     0.1000   -0.0005
##     80        0.8496             nan     0.1000   -0.0014
##    100        0.8310             nan     0.1000   -0.0007
##    120        0.8129             nan     0.1000   -0.0006
##    140        0.8039             nan     0.1000   -0.0001
##    160        0.7901             nan     0.1000   -0.0019
##    180        0.7793             nan     0.1000   -0.0004
##    200        0.7699             nan     0.1000   -0.0012
##    220        0.7605             nan     0.1000   -0.0007
##    240        0.7530             nan     0.1000   -0.0014
##    260        0.7464             nan     0.1000   -0.0012
##    280        0.7408             nan     0.1000   -0.0008
##    300        0.7355             nan     0.1000   -0.0011
##    320        0.7295             nan     0.1000   -0.0006
##    340        0.7245             nan     0.1000   -0.0007
##    360        0.7189             nan     0.1000   -0.0012
##    380        0.7151             nan     0.1000    0.0000
##    400        0.7106             nan     0.1000   -0.0009
##    420        0.7064             nan     0.1000   -0.0004
##    440        0.7011             nan     0.1000   -0.0005
##    460        0.6977             nan     0.1000   -0.0011
##    480        0.6926             nan     0.1000   -0.0009
##    500        0.6867             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2610             nan     0.1000    0.0174
##      2        1.2328             nan     0.1000    0.0130
##      3        1.2049             nan     0.1000    0.0099
##      4        1.1868             nan     0.1000    0.0083
##      5        1.1625             nan     0.1000    0.0093
##      6        1.1449             nan     0.1000    0.0063
##      7        1.1283             nan     0.1000    0.0061
##      8        1.1159             nan     0.1000    0.0044
##      9        1.1020             nan     0.1000    0.0063
##     10        1.0865             nan     0.1000    0.0059
##     20        0.9947             nan     0.1000    0.0021
##     40        0.9105             nan     0.1000    0.0003
##     60        0.8706             nan     0.1000    0.0004
##     80        0.8457             nan     0.1000    0.0002
##    100        0.8271             nan     0.1000   -0.0006
##    120        0.8121             nan     0.1000   -0.0011
##    140        0.8011             nan     0.1000   -0.0007
##    160        0.7888             nan     0.1000   -0.0013
##    180        0.7779             nan     0.1000   -0.0015
##    200        0.7693             nan     0.1000   -0.0006
##    220        0.7632             nan     0.1000   -0.0020
##    240        0.7573             nan     0.1000   -0.0007
##    260        0.7504             nan     0.1000   -0.0009
##    280        0.7442             nan     0.1000   -0.0007
##    300        0.7394             nan     0.1000   -0.0004
##    320        0.7338             nan     0.1000   -0.0015
##    340        0.7284             nan     0.1000   -0.0014
##    360        0.7230             nan     0.1000   -0.0011
##    380        0.7169             nan     0.1000   -0.0007
##    400        0.7131             nan     0.1000   -0.0004
##    420        0.7067             nan     0.1000   -0.0012
##    440        0.7017             nan     0.1000   -0.0015
##    460        0.6970             nan     0.1000   -0.0013
##    480        0.6937             nan     0.1000   -0.0007
##    500        0.6897             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2472             nan     0.1000    0.0213
##      2        1.2083             nan     0.1000    0.0188
##      3        1.1747             nan     0.1000    0.0141
##      4        1.1437             nan     0.1000    0.0111
##      5        1.1191             nan     0.1000    0.0100
##      6        1.0939             nan     0.1000    0.0085
##      7        1.0746             nan     0.1000    0.0086
##      8        1.0537             nan     0.1000    0.0076
##      9        1.0377             nan     0.1000    0.0055
##     10        1.0275             nan     0.1000    0.0020
##     20        0.9273             nan     0.1000    0.0022
##     40        0.8437             nan     0.1000   -0.0016
##     60        0.7957             nan     0.1000   -0.0015
##     80        0.7588             nan     0.1000   -0.0009
##    100        0.7320             nan     0.1000   -0.0023
##    120        0.7050             nan     0.1000   -0.0020
##    140        0.6790             nan     0.1000    0.0006
##    160        0.6623             nan     0.1000   -0.0014
##    180        0.6431             nan     0.1000   -0.0010
##    200        0.6279             nan     0.1000   -0.0012
##    220        0.6112             nan     0.1000   -0.0011
##    240        0.5892             nan     0.1000   -0.0010
##    260        0.5719             nan     0.1000   -0.0012
##    280        0.5596             nan     0.1000   -0.0018
##    300        0.5449             nan     0.1000   -0.0006
##    320        0.5286             nan     0.1000   -0.0009
##    340        0.5126             nan     0.1000   -0.0006
##    360        0.4997             nan     0.1000   -0.0021
##    380        0.4876             nan     0.1000   -0.0005
##    400        0.4755             nan     0.1000   -0.0003
##    420        0.4639             nan     0.1000   -0.0004
##    440        0.4537             nan     0.1000   -0.0004
##    460        0.4407             nan     0.1000   -0.0010
##    480        0.4313             nan     0.1000   -0.0007
##    500        0.4213             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2465             nan     0.1000    0.0213
##      2        1.2100             nan     0.1000    0.0194
##      3        1.1772             nan     0.1000    0.0146
##      4        1.1467             nan     0.1000    0.0112
##      5        1.1202             nan     0.1000    0.0108
##      6        1.0935             nan     0.1000    0.0107
##      7        1.0722             nan     0.1000    0.0080
##      8        1.0518             nan     0.1000    0.0067
##      9        1.0382             nan     0.1000    0.0053
##     10        1.0220             nan     0.1000    0.0052
##     20        0.9247             nan     0.1000    0.0025
##     40        0.8494             nan     0.1000   -0.0003
##     60        0.8059             nan     0.1000   -0.0005
##     80        0.7647             nan     0.1000   -0.0010
##    100        0.7384             nan     0.1000   -0.0021
##    120        0.7147             nan     0.1000   -0.0028
##    140        0.6910             nan     0.1000   -0.0008
##    160        0.6678             nan     0.1000   -0.0015
##    180        0.6441             nan     0.1000   -0.0011
##    200        0.6249             nan     0.1000   -0.0012
##    220        0.6102             nan     0.1000   -0.0005
##    240        0.5925             nan     0.1000   -0.0005
##    260        0.5723             nan     0.1000   -0.0003
##    280        0.5546             nan     0.1000   -0.0009
##    300        0.5404             nan     0.1000   -0.0015
##    320        0.5238             nan     0.1000   -0.0004
##    340        0.5087             nan     0.1000   -0.0005
##    360        0.4960             nan     0.1000   -0.0020
##    380        0.4827             nan     0.1000   -0.0010
##    400        0.4725             nan     0.1000   -0.0008
##    420        0.4617             nan     0.1000   -0.0001
##    440        0.4523             nan     0.1000   -0.0003
##    460        0.4403             nan     0.1000   -0.0010
##    480        0.4288             nan     0.1000   -0.0009
##    500        0.4202             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2512             nan     0.1000    0.0196
##      2        1.2131             nan     0.1000    0.0164
##      3        1.1763             nan     0.1000    0.0146
##      4        1.1473             nan     0.1000    0.0122
##      5        1.1238             nan     0.1000    0.0087
##      6        1.0992             nan     0.1000    0.0113
##      7        1.0781             nan     0.1000    0.0087
##      8        1.0576             nan     0.1000    0.0095
##      9        1.0414             nan     0.1000    0.0035
##     10        1.0278             nan     0.1000    0.0054
##     20        0.9377             nan     0.1000    0.0001
##     40        0.8471             nan     0.1000   -0.0003
##     60        0.8003             nan     0.1000   -0.0000
##     80        0.7678             nan     0.1000   -0.0018
##    100        0.7389             nan     0.1000   -0.0004
##    120        0.7174             nan     0.1000   -0.0013
##    140        0.6959             nan     0.1000   -0.0015
##    160        0.6780             nan     0.1000   -0.0007
##    180        0.6559             nan     0.1000   -0.0010
##    200        0.6400             nan     0.1000   -0.0009
##    220        0.6214             nan     0.1000   -0.0014
##    240        0.6022             nan     0.1000   -0.0011
##    260        0.5837             nan     0.1000   -0.0011
##    280        0.5657             nan     0.1000   -0.0014
##    300        0.5500             nan     0.1000   -0.0008
##    320        0.5379             nan     0.1000   -0.0012
##    340        0.5233             nan     0.1000   -0.0010
##    360        0.5106             nan     0.1000   -0.0010
##    380        0.4990             nan     0.1000   -0.0004
##    400        0.4897             nan     0.1000   -0.0014
##    420        0.4816             nan     0.1000   -0.0009
##    440        0.4719             nan     0.1000   -0.0013
##    460        0.4581             nan     0.1000   -0.0019
##    480        0.4477             nan     0.1000   -0.0010
##    500        0.4369             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2385             nan     0.1000    0.0239
##      2        1.1929             nan     0.1000    0.0188
##      3        1.1550             nan     0.1000    0.0162
##      4        1.1242             nan     0.1000    0.0141
##      5        1.0916             nan     0.1000    0.0133
##      6        1.0660             nan     0.1000    0.0114
##      7        1.0440             nan     0.1000    0.0061
##      8        1.0254             nan     0.1000    0.0079
##      9        1.0092             nan     0.1000    0.0050
##     10        0.9911             nan     0.1000    0.0060
##     20        0.8902             nan     0.1000   -0.0011
##     40        0.7860             nan     0.1000    0.0004
##     60        0.7257             nan     0.1000   -0.0006
##     80        0.6850             nan     0.1000   -0.0023
##    100        0.6422             nan     0.1000   -0.0005
##    120        0.6045             nan     0.1000   -0.0017
##    140        0.5724             nan     0.1000   -0.0004
##    160        0.5418             nan     0.1000   -0.0004
##    180        0.5159             nan     0.1000   -0.0014
##    200        0.4843             nan     0.1000   -0.0013
##    220        0.4614             nan     0.1000   -0.0005
##    240        0.4418             nan     0.1000   -0.0019
##    260        0.4225             nan     0.1000   -0.0015
##    280        0.4059             nan     0.1000   -0.0006
##    300        0.3881             nan     0.1000   -0.0010
##    320        0.3738             nan     0.1000   -0.0010
##    340        0.3566             nan     0.1000   -0.0006
##    360        0.3393             nan     0.1000   -0.0006
##    380        0.3276             nan     0.1000   -0.0006
##    400        0.3143             nan     0.1000   -0.0004
##    420        0.3022             nan     0.1000   -0.0011
##    440        0.2911             nan     0.1000   -0.0007
##    460        0.2815             nan     0.1000   -0.0007
##    480        0.2710             nan     0.1000   -0.0005
##    500        0.2623             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2332             nan     0.1000    0.0259
##      2        1.1914             nan     0.1000    0.0176
##      3        1.1503             nan     0.1000    0.0178
##      4        1.1168             nan     0.1000    0.0147
##      5        1.0853             nan     0.1000    0.0132
##      6        1.0600             nan     0.1000    0.0082
##      7        1.0397             nan     0.1000    0.0073
##      8        1.0169             nan     0.1000    0.0080
##      9        1.0007             nan     0.1000    0.0055
##     10        0.9844             nan     0.1000    0.0049
##     20        0.8839             nan     0.1000   -0.0009
##     40        0.7879             nan     0.1000   -0.0020
##     60        0.7350             nan     0.1000   -0.0020
##     80        0.6933             nan     0.1000   -0.0015
##    100        0.6511             nan     0.1000   -0.0017
##    120        0.6142             nan     0.1000   -0.0011
##    140        0.5887             nan     0.1000   -0.0011
##    160        0.5594             nan     0.1000   -0.0002
##    180        0.5306             nan     0.1000   -0.0018
##    200        0.5057             nan     0.1000    0.0003
##    220        0.4835             nan     0.1000   -0.0011
##    240        0.4572             nan     0.1000   -0.0007
##    260        0.4363             nan     0.1000   -0.0006
##    280        0.4182             nan     0.1000   -0.0009
##    300        0.4047             nan     0.1000   -0.0019
##    320        0.3864             nan     0.1000   -0.0012
##    340        0.3696             nan     0.1000   -0.0006
##    360        0.3572             nan     0.1000   -0.0019
##    380        0.3425             nan     0.1000   -0.0009
##    400        0.3264             nan     0.1000   -0.0004
##    420        0.3123             nan     0.1000   -0.0006
##    440        0.3001             nan     0.1000   -0.0011
##    460        0.2879             nan     0.1000   -0.0005
##    480        0.2787             nan     0.1000   -0.0008
##    500        0.2685             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2423             nan     0.1000    0.0236
##      2        1.1962             nan     0.1000    0.0205
##      3        1.1592             nan     0.1000    0.0140
##      4        1.1296             nan     0.1000    0.0122
##      5        1.0990             nan     0.1000    0.0108
##      6        1.0760             nan     0.1000    0.0082
##      7        1.0507             nan     0.1000    0.0086
##      8        1.0314             nan     0.1000    0.0084
##      9        1.0124             nan     0.1000    0.0074
##     10        0.9961             nan     0.1000    0.0044
##     20        0.8841             nan     0.1000    0.0002
##     40        0.7893             nan     0.1000   -0.0012
##     60        0.7375             nan     0.1000   -0.0009
##     80        0.6945             nan     0.1000   -0.0044
##    100        0.6575             nan     0.1000   -0.0021
##    120        0.6282             nan     0.1000   -0.0025
##    140        0.5944             nan     0.1000   -0.0014
##    160        0.5689             nan     0.1000   -0.0015
##    180        0.5403             nan     0.1000   -0.0011
##    200        0.5181             nan     0.1000   -0.0015
##    220        0.4954             nan     0.1000   -0.0011
##    240        0.4733             nan     0.1000   -0.0019
##    260        0.4523             nan     0.1000   -0.0012
##    280        0.4343             nan     0.1000   -0.0004
##    300        0.4163             nan     0.1000   -0.0002
##    320        0.3970             nan     0.1000    0.0000
##    340        0.3802             nan     0.1000    0.0001
##    360        0.3640             nan     0.1000   -0.0012
##    380        0.3478             nan     0.1000   -0.0006
##    400        0.3337             nan     0.1000   -0.0001
##    420        0.3218             nan     0.1000   -0.0007
##    440        0.3118             nan     0.1000   -0.0011
##    460        0.2991             nan     0.1000   -0.0007
##    480        0.2903             nan     0.1000   -0.0011
##    500        0.2783             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2212             nan     0.2000    0.0309
##      2        1.1738             nan     0.2000    0.0176
##      3        1.1406             nan     0.2000    0.0155
##      4        1.1084             nan     0.2000    0.0143
##      5        1.0798             nan     0.2000    0.0116
##      6        1.0530             nan     0.2000    0.0112
##      7        1.0337             nan     0.2000    0.0047
##      8        1.0163             nan     0.2000    0.0064
##      9        1.0010             nan     0.2000    0.0056
##     10        0.9867             nan     0.2000    0.0060
##     20        0.9144             nan     0.2000    0.0011
##     40        0.8434             nan     0.2000   -0.0028
##     60        0.8128             nan     0.2000   -0.0012
##     80        0.7894             nan     0.2000   -0.0018
##    100        0.7722             nan     0.2000   -0.0028
##    120        0.7565             nan     0.2000   -0.0038
##    140        0.7411             nan     0.2000   -0.0012
##    160        0.7285             nan     0.2000   -0.0021
##    180        0.7157             nan     0.2000   -0.0023
##    200        0.7068             nan     0.2000   -0.0033
##    220        0.6942             nan     0.2000   -0.0019
##    240        0.6860             nan     0.2000   -0.0040
##    260        0.6809             nan     0.2000   -0.0030
##    280        0.6704             nan     0.2000   -0.0030
##    300        0.6619             nan     0.2000   -0.0014
##    320        0.6568             nan     0.2000   -0.0010
##    340        0.6522             nan     0.2000   -0.0016
##    360        0.6453             nan     0.2000   -0.0023
##    380        0.6358             nan     0.2000   -0.0022
##    400        0.6335             nan     0.2000   -0.0041
##    420        0.6258             nan     0.2000   -0.0024
##    440        0.6192             nan     0.2000   -0.0007
##    460        0.6135             nan     0.2000   -0.0024
##    480        0.6087             nan     0.2000   -0.0021
##    500        0.6055             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2246             nan     0.2000    0.0306
##      2        1.1771             nan     0.2000    0.0205
##      3        1.1457             nan     0.2000    0.0117
##      4        1.1143             nan     0.2000    0.0137
##      5        1.0861             nan     0.2000    0.0107
##      6        1.0624             nan     0.2000    0.0114
##      7        1.0445             nan     0.2000    0.0092
##      8        1.0279             nan     0.2000    0.0044
##      9        1.0125             nan     0.2000    0.0064
##     10        1.0013             nan     0.2000    0.0026
##     20        0.9240             nan     0.2000    0.0005
##     40        0.8551             nan     0.2000    0.0003
##     60        0.8178             nan     0.2000   -0.0019
##     80        0.7890             nan     0.2000   -0.0014
##    100        0.7747             nan     0.2000   -0.0008
##    120        0.7603             nan     0.2000   -0.0008
##    140        0.7500             nan     0.2000   -0.0007
##    160        0.7390             nan     0.2000   -0.0028
##    180        0.7296             nan     0.2000   -0.0033
##    200        0.7190             nan     0.2000   -0.0023
##    220        0.7116             nan     0.2000   -0.0016
##    240        0.7034             nan     0.2000   -0.0017
##    260        0.6952             nan     0.2000   -0.0020
##    280        0.6887             nan     0.2000   -0.0034
##    300        0.6781             nan     0.2000   -0.0017
##    320        0.6682             nan     0.2000   -0.0012
##    340        0.6602             nan     0.2000   -0.0017
##    360        0.6553             nan     0.2000   -0.0019
##    380        0.6476             nan     0.2000   -0.0004
##    400        0.6419             nan     0.2000   -0.0017
##    420        0.6366             nan     0.2000   -0.0027
##    440        0.6341             nan     0.2000   -0.0008
##    460        0.6252             nan     0.2000   -0.0004
##    480        0.6199             nan     0.2000   -0.0013
##    500        0.6174             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2273             nan     0.2000    0.0320
##      2        1.1843             nan     0.2000    0.0217
##      3        1.1448             nan     0.2000    0.0188
##      4        1.1170             nan     0.2000    0.0110
##      5        1.0903             nan     0.2000    0.0119
##      6        1.0628             nan     0.2000    0.0113
##      7        1.0472             nan     0.2000    0.0050
##      8        1.0304             nan     0.2000    0.0045
##      9        1.0099             nan     0.2000    0.0078
##     10        0.9952             nan     0.2000    0.0050
##     20        0.9174             nan     0.2000   -0.0009
##     40        0.8519             nan     0.2000   -0.0026
##     60        0.8172             nan     0.2000   -0.0021
##     80        0.7949             nan     0.2000   -0.0017
##    100        0.7826             nan     0.2000   -0.0009
##    120        0.7674             nan     0.2000   -0.0025
##    140        0.7534             nan     0.2000   -0.0044
##    160        0.7411             nan     0.2000   -0.0028
##    180        0.7305             nan     0.2000   -0.0009
##    200        0.7181             nan     0.2000   -0.0021
##    220        0.7093             nan     0.2000   -0.0033
##    240        0.7010             nan     0.2000   -0.0014
##    260        0.6903             nan     0.2000   -0.0010
##    280        0.6829             nan     0.2000   -0.0014
##    300        0.6742             nan     0.2000   -0.0003
##    320        0.6677             nan     0.2000   -0.0025
##    340        0.6624             nan     0.2000   -0.0033
##    360        0.6559             nan     0.2000   -0.0013
##    380        0.6490             nan     0.2000   -0.0023
##    400        0.6434             nan     0.2000   -0.0021
##    420        0.6357             nan     0.2000   -0.0019
##    440        0.6285             nan     0.2000   -0.0025
##    460        0.6243             nan     0.2000   -0.0015
##    480        0.6170             nan     0.2000   -0.0027
##    500        0.6099             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2082             nan     0.2000    0.0425
##      2        1.1498             nan     0.2000    0.0267
##      3        1.1017             nan     0.2000    0.0228
##      4        1.0640             nan     0.2000    0.0172
##      5        1.0323             nan     0.2000    0.0137
##      6        1.0052             nan     0.2000    0.0125
##      7        0.9865             nan     0.2000    0.0063
##      8        0.9663             nan     0.2000    0.0040
##      9        0.9524             nan     0.2000    0.0021
##     10        0.9369             nan     0.2000    0.0062
##     20        0.8456             nan     0.2000   -0.0021
##     40        0.7633             nan     0.2000   -0.0036
##     60        0.7129             nan     0.2000   -0.0013
##     80        0.6754             nan     0.2000   -0.0014
##    100        0.6506             nan     0.2000   -0.0026
##    120        0.6187             nan     0.2000   -0.0035
##    140        0.5925             nan     0.2000   -0.0017
##    160        0.5543             nan     0.2000   -0.0014
##    180        0.5326             nan     0.2000   -0.0032
##    200        0.5038             nan     0.2000   -0.0015
##    220        0.4852             nan     0.2000   -0.0047
##    240        0.4669             nan     0.2000   -0.0021
##    260        0.4475             nan     0.2000   -0.0054
##    280        0.4173             nan     0.2000   -0.0026
##    300        0.3955             nan     0.2000   -0.0010
##    320        0.3784             nan     0.2000   -0.0025
##    340        0.3588             nan     0.2000   -0.0011
##    360        0.3433             nan     0.2000   -0.0014
##    380        0.3242             nan     0.2000   -0.0012
##    400        0.3107             nan     0.2000   -0.0008
##    420        0.2989             nan     0.2000   -0.0007
##    440        0.2888             nan     0.2000   -0.0007
##    460        0.2798             nan     0.2000   -0.0009
##    480        0.2674             nan     0.2000   -0.0008
##    500        0.2564             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2137             nan     0.2000    0.0359
##      2        1.1410             nan     0.2000    0.0347
##      3        1.0912             nan     0.2000    0.0230
##      4        1.0563             nan     0.2000    0.0135
##      5        1.0307             nan     0.2000    0.0065
##      6        1.0112             nan     0.2000    0.0034
##      7        0.9907             nan     0.2000    0.0070
##      8        0.9711             nan     0.2000    0.0062
##      9        0.9525             nan     0.2000    0.0056
##     10        0.9437             nan     0.2000   -0.0003
##     20        0.8515             nan     0.2000    0.0005
##     40        0.7617             nan     0.2000   -0.0013
##     60        0.7075             nan     0.2000   -0.0001
##     80        0.6624             nan     0.2000   -0.0004
##    100        0.6198             nan     0.2000   -0.0026
##    120        0.5815             nan     0.2000   -0.0040
##    140        0.5484             nan     0.2000   -0.0037
##    160        0.5220             nan     0.2000   -0.0006
##    180        0.4963             nan     0.2000   -0.0027
##    200        0.4751             nan     0.2000   -0.0024
##    220        0.4540             nan     0.2000   -0.0026
##    240        0.4294             nan     0.2000   -0.0038
##    260        0.4127             nan     0.2000   -0.0016
##    280        0.3957             nan     0.2000   -0.0026
##    300        0.3756             nan     0.2000   -0.0012
##    320        0.3598             nan     0.2000   -0.0021
##    340        0.3479             nan     0.2000   -0.0012
##    360        0.3331             nan     0.2000   -0.0019
##    380        0.3162             nan     0.2000   -0.0012
##    400        0.3013             nan     0.2000   -0.0015
##    420        0.2885             nan     0.2000   -0.0012
##    440        0.2771             nan     0.2000   -0.0007
##    460        0.2674             nan     0.2000   -0.0024
##    480        0.2564             nan     0.2000   -0.0006
##    500        0.2458             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2093             nan     0.2000    0.0351
##      2        1.1421             nan     0.2000    0.0275
##      3        1.1011             nan     0.2000    0.0212
##      4        1.0518             nan     0.2000    0.0186
##      5        1.0215             nan     0.2000    0.0088
##      6        0.9971             nan     0.2000    0.0100
##      7        0.9791             nan     0.2000    0.0047
##      8        0.9603             nan     0.2000    0.0080
##      9        0.9459             nan     0.2000    0.0046
##     10        0.9314             nan     0.2000    0.0046
##     20        0.8476             nan     0.2000   -0.0035
##     40        0.7758             nan     0.2000   -0.0024
##     60        0.7240             nan     0.2000   -0.0024
##     80        0.6748             nan     0.2000   -0.0017
##    100        0.6400             nan     0.2000   -0.0002
##    120        0.6100             nan     0.2000   -0.0021
##    140        0.5792             nan     0.2000    0.0004
##    160        0.5504             nan     0.2000   -0.0021
##    180        0.5251             nan     0.2000   -0.0040
##    200        0.5062             nan     0.2000   -0.0010
##    220        0.4855             nan     0.2000   -0.0055
##    240        0.4671             nan     0.2000   -0.0031
##    260        0.4500             nan     0.2000   -0.0028
##    280        0.4316             nan     0.2000   -0.0025
##    300        0.4147             nan     0.2000   -0.0019
##    320        0.3931             nan     0.2000   -0.0016
##    340        0.3761             nan     0.2000   -0.0021
##    360        0.3569             nan     0.2000   -0.0011
##    380        0.3382             nan     0.2000   -0.0028
##    400        0.3238             nan     0.2000   -0.0003
##    420        0.3135             nan     0.2000   -0.0006
##    440        0.3015             nan     0.2000   -0.0013
##    460        0.2887             nan     0.2000   -0.0010
##    480        0.2763             nan     0.2000   -0.0002
##    500        0.2683             nan     0.2000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2027             nan     0.2000    0.0507
##      2        1.1131             nan     0.2000    0.0358
##      3        1.0656             nan     0.2000    0.0175
##      4        1.0204             nan     0.2000    0.0190
##      5        0.9870             nan     0.2000    0.0128
##      6        0.9580             nan     0.2000    0.0055
##      7        0.9405             nan     0.2000    0.0019
##      8        0.9252             nan     0.2000    0.0017
##      9        0.9038             nan     0.2000    0.0060
##     10        0.8907             nan     0.2000    0.0018
##     20        0.8015             nan     0.2000   -0.0025
##     40        0.7123             nan     0.2000   -0.0081
##     60        0.6329             nan     0.2000   -0.0025
##     80        0.5707             nan     0.2000   -0.0082
##    100        0.5114             nan     0.2000   -0.0042
##    120        0.4633             nan     0.2000   -0.0028
##    140        0.4302             nan     0.2000   -0.0015
##    160        0.3904             nan     0.2000   -0.0010
##    180        0.3612             nan     0.2000   -0.0021
##    200        0.3315             nan     0.2000   -0.0033
##    220        0.3057             nan     0.2000   -0.0016
##    240        0.2801             nan     0.2000   -0.0010
##    260        0.2567             nan     0.2000   -0.0010
##    280        0.2418             nan     0.2000   -0.0007
##    300        0.2232             nan     0.2000   -0.0011
##    320        0.2071             nan     0.2000   -0.0005
##    340        0.1932             nan     0.2000   -0.0017
##    360        0.1804             nan     0.2000   -0.0004
##    380        0.1705             nan     0.2000   -0.0014
##    400        0.1600             nan     0.2000   -0.0013
##    420        0.1493             nan     0.2000   -0.0005
##    440        0.1416             nan     0.2000   -0.0002
##    460        0.1325             nan     0.2000   -0.0005
##    480        0.1220             nan     0.2000   -0.0012
##    500        0.1149             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1944             nan     0.2000    0.0452
##      2        1.1193             nan     0.2000    0.0344
##      3        1.0561             nan     0.2000    0.0228
##      4        1.0080             nan     0.2000    0.0177
##      5        0.9744             nan     0.2000    0.0113
##      6        0.9526             nan     0.2000    0.0061
##      7        0.9323             nan     0.2000    0.0063
##      8        0.9125             nan     0.2000    0.0043
##      9        0.8947             nan     0.2000    0.0024
##     10        0.8830             nan     0.2000    0.0010
##     20        0.8008             nan     0.2000    0.0011
##     40        0.7014             nan     0.2000   -0.0020
##     60        0.6268             nan     0.2000   -0.0051
##     80        0.5634             nan     0.2000   -0.0013
##    100        0.5051             nan     0.2000   -0.0044
##    120        0.4599             nan     0.2000   -0.0019
##    140        0.4206             nan     0.2000   -0.0017
##    160        0.3854             nan     0.2000   -0.0010
##    180        0.3526             nan     0.2000   -0.0017
##    200        0.3293             nan     0.2000   -0.0017
##    220        0.3030             nan     0.2000   -0.0031
##    240        0.2785             nan     0.2000   -0.0010
##    260        0.2596             nan     0.2000   -0.0010
##    280        0.2422             nan     0.2000   -0.0013
##    300        0.2261             nan     0.2000   -0.0008
##    320        0.2104             nan     0.2000   -0.0016
##    340        0.1940             nan     0.2000   -0.0023
##    360        0.1808             nan     0.2000   -0.0004
##    380        0.1699             nan     0.2000   -0.0011
##    400        0.1598             nan     0.2000   -0.0007
##    420        0.1490             nan     0.2000   -0.0005
##    440        0.1391             nan     0.2000   -0.0006
##    460        0.1300             nan     0.2000   -0.0006
##    480        0.1214             nan     0.2000   -0.0004
##    500        0.1142             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1942             nan     0.2000    0.0482
##      2        1.1146             nan     0.2000    0.0354
##      3        1.0596             nan     0.2000    0.0236
##      4        1.0188             nan     0.2000    0.0123
##      5        0.9852             nan     0.2000    0.0129
##      6        0.9541             nan     0.2000    0.0081
##      7        0.9364             nan     0.2000    0.0032
##      8        0.9157             nan     0.2000    0.0031
##      9        0.8981             nan     0.2000    0.0030
##     10        0.8873             nan     0.2000    0.0007
##     20        0.7911             nan     0.2000    0.0010
##     40        0.6833             nan     0.2000   -0.0001
##     60        0.6192             nan     0.2000   -0.0073
##     80        0.5585             nan     0.2000   -0.0015
##    100        0.5055             nan     0.2000   -0.0024
##    120        0.4693             nan     0.2000   -0.0045
##    140        0.4330             nan     0.2000   -0.0020
##    160        0.3976             nan     0.2000    0.0003
##    180        0.3648             nan     0.2000   -0.0020
##    200        0.3325             nan     0.2000   -0.0014
##    220        0.3023             nan     0.2000   -0.0013
##    240        0.2766             nan     0.2000   -0.0005
##    260        0.2584             nan     0.2000   -0.0012
##    280        0.2415             nan     0.2000   -0.0020
##    300        0.2217             nan     0.2000   -0.0005
##    320        0.2063             nan     0.2000   -0.0012
##    340        0.1926             nan     0.2000   -0.0018
##    360        0.1802             nan     0.2000   -0.0011
##    380        0.1677             nan     0.2000   -0.0013
##    400        0.1574             nan     0.2000   -0.0008
##    420        0.1486             nan     0.2000   -0.0007
##    440        0.1381             nan     0.2000   -0.0013
##    460        0.1304             nan     0.2000   -0.0013
##    480        0.1235             nan     0.2000   -0.0008
##    500        0.1152             nan     0.2000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1982             nan     0.3000    0.0501
##      2        1.1541             nan     0.3000    0.0209
##      3        1.0987             nan     0.3000    0.0250
##      4        1.0595             nan     0.3000    0.0141
##      5        1.0261             nan     0.3000    0.0145
##      6        1.0050             nan     0.3000    0.0095
##      7        0.9858             nan     0.3000    0.0060
##      8        0.9750             nan     0.3000   -0.0002
##      9        0.9559             nan     0.3000    0.0052
##     10        0.9404             nan     0.3000    0.0015
##     20        0.8856             nan     0.3000   -0.0010
##     40        0.8185             nan     0.3000   -0.0022
##     60        0.7890             nan     0.3000   -0.0027
##     80        0.7676             nan     0.3000   -0.0058
##    100        0.7523             nan     0.3000   -0.0024
##    120        0.7317             nan     0.3000   -0.0011
##    140        0.7148             nan     0.3000   -0.0026
##    160        0.6981             nan     0.3000   -0.0010
##    180        0.6825             nan     0.3000   -0.0007
##    200        0.6719             nan     0.3000   -0.0017
##    220        0.6602             nan     0.3000   -0.0021
##    240        0.6520             nan     0.3000   -0.0030
##    260        0.6437             nan     0.3000   -0.0041
##    280        0.6328             nan     0.3000   -0.0018
##    300        0.6236             nan     0.3000   -0.0011
##    320        0.6165             nan     0.3000   -0.0037
##    340        0.6108             nan     0.3000   -0.0019
##    360        0.6018             nan     0.3000   -0.0011
##    380        0.5971             nan     0.3000   -0.0028
##    400        0.5893             nan     0.3000   -0.0022
##    420        0.5787             nan     0.3000   -0.0028
##    440        0.5740             nan     0.3000   -0.0028
##    460        0.5672             nan     0.3000   -0.0033
##    480        0.5651             nan     0.3000   -0.0015
##    500        0.5575             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2043             nan     0.3000    0.0440
##      2        1.1540             nan     0.3000    0.0204
##      3        1.1123             nan     0.3000    0.0140
##      4        1.0665             nan     0.3000    0.0195
##      5        1.0328             nan     0.3000    0.0158
##      6        1.0120             nan     0.3000    0.0066
##      7        1.0041             nan     0.3000   -0.0015
##      8        0.9824             nan     0.3000    0.0094
##      9        0.9639             nan     0.3000    0.0038
##     10        0.9539             nan     0.3000    0.0020
##     20        0.8746             nan     0.3000   -0.0017
##     40        0.8215             nan     0.3000   -0.0047
##     60        0.7918             nan     0.3000   -0.0061
##     80        0.7644             nan     0.3000   -0.0037
##    100        0.7462             nan     0.3000   -0.0024
##    120        0.7279             nan     0.3000   -0.0014
##    140        0.7152             nan     0.3000   -0.0024
##    160        0.6972             nan     0.3000   -0.0022
##    180        0.6906             nan     0.3000   -0.0033
##    200        0.6803             nan     0.3000   -0.0020
##    220        0.6658             nan     0.3000   -0.0022
##    240        0.6551             nan     0.3000   -0.0040
##    260        0.6470             nan     0.3000   -0.0031
##    280        0.6417             nan     0.3000   -0.0026
##    300        0.6335             nan     0.3000   -0.0038
##    320        0.6239             nan     0.3000   -0.0055
##    340        0.6130             nan     0.3000   -0.0023
##    360        0.6063             nan     0.3000   -0.0028
##    380        0.6018             nan     0.3000   -0.0013
##    400        0.5947             nan     0.3000   -0.0022
##    420        0.5838             nan     0.3000   -0.0026
##    440        0.5773             nan     0.3000   -0.0026
##    460        0.5728             nan     0.3000   -0.0020
##    480        0.5682             nan     0.3000   -0.0015
##    500        0.5602             nan     0.3000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2058             nan     0.3000    0.0485
##      2        1.1502             nan     0.3000    0.0266
##      3        1.1018             nan     0.3000    0.0125
##      4        1.0580             nan     0.3000    0.0149
##      5        1.0267             nan     0.3000    0.0154
##      6        1.0063             nan     0.3000    0.0055
##      7        0.9846             nan     0.3000    0.0059
##      8        0.9693             nan     0.3000    0.0023
##      9        0.9592             nan     0.3000    0.0027
##     10        0.9506             nan     0.3000    0.0020
##     20        0.8814             nan     0.3000   -0.0032
##     40        0.8221             nan     0.3000   -0.0030
##     60        0.7936             nan     0.3000   -0.0017
##     80        0.7658             nan     0.3000   -0.0033
##    100        0.7494             nan     0.3000   -0.0027
##    120        0.7308             nan     0.3000   -0.0009
##    140        0.7201             nan     0.3000   -0.0040
##    160        0.7035             nan     0.3000   -0.0045
##    180        0.6883             nan     0.3000   -0.0014
##    200        0.6763             nan     0.3000   -0.0014
##    220        0.6599             nan     0.3000   -0.0033
##    240        0.6492             nan     0.3000   -0.0017
##    260        0.6408             nan     0.3000   -0.0029
##    280        0.6309             nan     0.3000   -0.0033
##    300        0.6258             nan     0.3000   -0.0043
##    320        0.6139             nan     0.3000   -0.0027
##    340        0.6080             nan     0.3000   -0.0025
##    360        0.6040             nan     0.3000   -0.0038
##    380        0.6000             nan     0.3000   -0.0012
##    400        0.5938             nan     0.3000   -0.0034
##    420        0.5828             nan     0.3000   -0.0022
##    440        0.5746             nan     0.3000   -0.0012
##    460        0.5673             nan     0.3000   -0.0020
##    480        0.5623             nan     0.3000   -0.0014
##    500        0.5572             nan     0.3000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1671             nan     0.3000    0.0586
##      2        1.1024             nan     0.3000    0.0264
##      3        1.0565             nan     0.3000    0.0177
##      4        1.0112             nan     0.3000    0.0202
##      5        0.9794             nan     0.3000    0.0129
##      6        0.9537             nan     0.3000    0.0009
##      7        0.9341             nan     0.3000    0.0065
##      8        0.9163             nan     0.3000    0.0046
##      9        0.9004             nan     0.3000    0.0009
##     10        0.8870             nan     0.3000    0.0021
##     20        0.8100             nan     0.3000   -0.0088
##     40        0.7405             nan     0.3000   -0.0041
##     60        0.6715             nan     0.3000   -0.0048
##     80        0.5972             nan     0.3000   -0.0061
##    100        0.5531             nan     0.3000   -0.0039
##    120        0.5170             nan     0.3000   -0.0009
##    140        0.4757             nan     0.3000   -0.0049
##    160        0.4527             nan     0.3000   -0.0019
##    180        0.4221             nan     0.3000   -0.0020
##    200        0.3881             nan     0.3000   -0.0019
##    220        0.3660             nan     0.3000   -0.0029
##    240        0.3434             nan     0.3000   -0.0037
##    260        0.3190             nan     0.3000   -0.0022
##    280        0.2980             nan     0.3000   -0.0013
##    300        0.2788             nan     0.3000   -0.0019
##    320        0.2610             nan     0.3000   -0.0039
##    340        0.2478             nan     0.3000   -0.0030
##    360        0.2316             nan     0.3000   -0.0012
##    380        0.2151             nan     0.3000   -0.0003
##    400        0.2053             nan     0.3000   -0.0031
##    420        0.1943             nan     0.3000   -0.0021
##    440        0.1846             nan     0.3000   -0.0017
##    460        0.1769             nan     0.3000   -0.0014
##    480        0.1672             nan     0.3000   -0.0012
##    500        0.1578             nan     0.3000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1846             nan     0.3000    0.0536
##      2        1.1057             nan     0.3000    0.0268
##      3        1.0491             nan     0.3000    0.0251
##      4        1.0071             nan     0.3000    0.0126
##      5        0.9858             nan     0.3000   -0.0010
##      6        0.9618             nan     0.3000    0.0070
##      7        0.9422             nan     0.3000    0.0041
##      8        0.9261             nan     0.3000    0.0011
##      9        0.9001             nan     0.3000    0.0063
##     10        0.8911             nan     0.3000   -0.0032
##     20        0.8095             nan     0.3000   -0.0028
##     40        0.7320             nan     0.3000   -0.0058
##     60        0.6663             nan     0.3000   -0.0020
##     80        0.6037             nan     0.3000   -0.0019
##    100        0.5520             nan     0.3000    0.0006
##    120        0.5173             nan     0.3000   -0.0055
##    140        0.4885             nan     0.3000   -0.0035
##    160        0.4562             nan     0.3000   -0.0032
##    180        0.4226             nan     0.3000   -0.0047
##    200        0.3931             nan     0.3000   -0.0051
##    220        0.3695             nan     0.3000   -0.0044
##    240        0.3502             nan     0.3000   -0.0036
##    260        0.3272             nan     0.3000   -0.0010
##    280        0.3104             nan     0.3000   -0.0017
##    300        0.2911             nan     0.3000   -0.0008
##    320        0.2738             nan     0.3000   -0.0019
##    340        0.2552             nan     0.3000   -0.0022
##    360        0.2382             nan     0.3000   -0.0019
##    380        0.2222             nan     0.3000   -0.0003
##    400        0.2096             nan     0.3000   -0.0008
##    420        0.1981             nan     0.3000   -0.0019
##    440        0.1868             nan     0.3000   -0.0012
##    460        0.1781             nan     0.3000   -0.0025
##    480        0.1704             nan     0.3000   -0.0016
##    500        0.1596             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1795             nan     0.3000    0.0500
##      2        1.0978             nan     0.3000    0.0342
##      3        1.0462             nan     0.3000    0.0229
##      4        0.9999             nan     0.3000    0.0198
##      5        0.9649             nan     0.3000    0.0070
##      6        0.9416             nan     0.3000    0.0073
##      7        0.9241             nan     0.3000    0.0053
##      8        0.9064             nan     0.3000    0.0023
##      9        0.9005             nan     0.3000   -0.0024
##     10        0.8910             nan     0.3000   -0.0071
##     20        0.8058             nan     0.3000   -0.0039
##     40        0.7259             nan     0.3000   -0.0079
##     60        0.6717             nan     0.3000   -0.0018
##     80        0.6343             nan     0.3000   -0.0013
##    100        0.5837             nan     0.3000   -0.0070
##    120        0.5432             nan     0.3000   -0.0012
##    140        0.5061             nan     0.3000   -0.0006
##    160        0.4676             nan     0.3000   -0.0032
##    180        0.4338             nan     0.3000   -0.0025
##    200        0.4020             nan     0.3000   -0.0016
##    220        0.3766             nan     0.3000   -0.0029
##    240        0.3593             nan     0.3000   -0.0042
##    260        0.3444             nan     0.3000   -0.0033
##    280        0.3206             nan     0.3000   -0.0048
##    300        0.2953             nan     0.3000   -0.0029
##    320        0.2791             nan     0.3000   -0.0019
##    340        0.2601             nan     0.3000   -0.0043
##    360        0.2475             nan     0.3000   -0.0016
##    380        0.2349             nan     0.3000   -0.0018
##    400        0.2191             nan     0.3000   -0.0013
##    420        0.2039             nan     0.3000   -0.0008
##    440        0.1951             nan     0.3000   -0.0018
##    460        0.1865             nan     0.3000   -0.0008
##    480        0.1739             nan     0.3000   -0.0016
##    500        0.1629             nan     0.3000   -0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1473             nan     0.3000    0.0715
##      2        1.0600             nan     0.3000    0.0363
##      3        0.9908             nan     0.3000    0.0241
##      4        0.9531             nan     0.3000    0.0149
##      5        0.9228             nan     0.3000    0.0027
##      6        0.8990             nan     0.3000    0.0017
##      7        0.8800             nan     0.3000   -0.0020
##      8        0.8621             nan     0.3000    0.0022
##      9        0.8488             nan     0.3000   -0.0002
##     10        0.8352             nan     0.3000    0.0000
##     20        0.7534             nan     0.3000   -0.0060
##     40        0.6653             nan     0.3000   -0.0063
##     60        0.5649             nan     0.3000   -0.0045
##     80        0.4740             nan     0.3000   -0.0026
##    100        0.4171             nan     0.3000   -0.0041
##    120        0.3785             nan     0.3000   -0.0034
##    140        2.8451             nan     0.3000   -0.0023
##    160        2.8160             nan     0.3000   -0.0045
##    180        2.7975             nan     0.3000   -0.0020
##    200        2.7782             nan     0.3000   -0.0022
##    220        2.7596             nan     0.3000   -0.0016
##    240        2.7431             nan     0.3000   -0.0011
##    260        2.7302             nan     0.3000   -0.0008
##    280        2.7113             nan     0.3000   -0.0003
##    300        2.7033             nan     0.3000   -0.0010
##    320        2.6963             nan     0.3000   -0.0007
##    340        2.6856             nan     0.3000   -0.0009
##    360        2.6779             nan     0.3000   -0.0019
##    380        2.6711             nan     0.3000   -0.0017
##    400        2.6586             nan     0.3000   -0.0003
##    420        2.6518             nan     0.3000   -0.0006
##    440        2.6471             nan     0.3000   -0.0012
##    460        2.6370             nan     0.3000   -0.0014
##    480        2.6288             nan     0.3000   -0.0002
##    500        2.6221             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1390             nan     0.3000    0.0647
##      2        1.0475             nan     0.3000    0.0420
##      3        0.9974             nan     0.3000    0.0170
##      4        0.9510             nan     0.3000    0.0134
##      5        0.9172             nan     0.3000    0.0027
##      6        0.8963             nan     0.3000    0.0009
##      7        0.8774             nan     0.3000   -0.0005
##      8        0.8687             nan     0.3000   -0.0059
##      9        0.8604             nan     0.3000   -0.0028
##     10        0.8541             nan     0.3000   -0.0058
##     20        0.7578             nan     0.3000   -0.0054
##     40        0.6394             nan     0.3000   -0.0032
##     60        0.5584             nan     0.3000   -0.0035
##     80        0.4871             nan     0.3000   -0.0021
##    100        0.4240             nan     0.3000   -0.0018
##    120        0.3826             nan     0.3000   -0.0030
##    140        0.3538             nan     0.3000   -0.0015
##    160        0.2956             nan     0.3000   -0.0012
##    180        0.2642             nan     0.3000   -0.0018
##    200        0.2340             nan     0.3000   -0.0011
##    220        0.2058             nan     0.3000   -0.0011
##    240        0.1827             nan     0.3000   -0.0007
##    260        0.1659             nan     0.3000   -0.0011
##    280        0.1505             nan     0.3000   -0.0025
##    300        0.1365             nan     0.3000   -0.0018
##    320        0.1271             nan     0.3000   -0.0004
##    340        0.1148             nan     0.3000   -0.0004
##    360        0.1043             nan     0.3000   -0.0007
##    380        0.0945             nan     0.3000   -0.0007
##    400        0.0849             nan     0.3000   -0.0007
##    420        0.0799             nan     0.3000   -0.0006
##    440        0.0726             nan     0.3000   -0.0006
##    460        0.0668             nan     0.3000   -0.0009
##    480        0.0626             nan     0.3000   -0.0004
##    500        0.0574             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1570             nan     0.3000    0.0603
##      2        1.0774             nan     0.3000    0.0317
##      3        1.0119             nan     0.3000    0.0256
##      4        0.9772             nan     0.3000    0.0030
##      5        0.9451             nan     0.3000    0.0070
##      6        0.9264             nan     0.3000   -0.0010
##      7        0.8890             nan     0.3000    0.0015
##      8        0.8759             nan     0.3000   -0.0047
##      9        0.8611             nan     0.3000   -0.0058
##     10        0.8515             nan     0.3000   -0.0082
##     20        0.7578             nan     0.3000   -0.0053
##     40        0.6427             nan     0.3000   -0.0070
##     60        0.5541             nan     0.3000   -0.0031
##     80        0.4851             nan     0.3000   -0.0043
##    100        0.4334             nan     0.3000   -0.0047
##    120        0.3815             nan     0.3000   -0.0010
##    140        0.3262             nan     0.3000   -0.0011
##    160        0.2918             nan     0.3000   -0.0030
##    180        0.2615             nan     0.3000   -0.0010
##    200        0.2346             nan     0.3000   -0.0037
##    220        0.2155             nan     0.3000   -0.0024
##    240        0.1932             nan     0.3000   -0.0029
##    260        0.1728             nan     0.3000   -0.0004
##    280        0.1548             nan     0.3000   -0.0011
##    300        0.1404             nan     0.3000   -0.0010
##    320        0.1299             nan     0.3000   -0.0023
##    340        0.1146             nan     0.3000   -0.0009
##    360        0.1038             nan     0.3000   -0.0006
##    380        0.0944             nan     0.3000    0.0000
##    400        0.0879             nan     0.3000   -0.0008
##    420        0.0817             nan     0.3000   -0.0010
##    440        0.0765             nan     0.3000   -0.0012
##    460        0.0692             nan     0.3000   -0.0011
##    480        0.0620             nan     0.3000   -0.0004
##    500        0.0569             nan     0.3000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1478             nan     0.5000    0.0494
##      2        1.0780             nan     0.5000    0.0307
##      3        1.0336             nan     0.5000    0.0204
##      4        0.9860             nan     0.5000    0.0218
##      5        0.9594             nan     0.5000    0.0110
##      6        0.9520             nan     0.5000   -0.0046
##      7        0.9347             nan     0.5000    0.0017
##      8        0.9198             nan     0.5000   -0.0004
##      9        0.9113             nan     0.5000   -0.0011
##     10        0.9050             nan     0.5000   -0.0050
##     20        0.8510             nan     0.5000   -0.0044
##     40        0.7897             nan     0.5000   -0.0031
##     60        0.7570             nan     0.5000   -0.0046
##     80        0.7387             nan     0.5000   -0.0030
##    100        0.7089             nan     0.5000   -0.0055
##    120        0.6803             nan     0.5000   -0.0032
##    140        0.6632             nan     0.5000    0.0007
##    160        0.6573             nan     0.5000   -0.0056
##    180        0.6442             nan     0.5000   -0.0030
##    200        0.6313             nan     0.5000   -0.0049
##    220        0.6171             nan     0.5000   -0.0070
##    240        0.6061             nan     0.5000   -0.0012
##    260        0.5944             nan     0.5000   -0.0026
##    280        0.5742             nan     0.5000   -0.0031
##    300        0.5651             nan     0.5000   -0.0082
##    320        0.5558             nan     0.5000   -0.0043
##    340        0.5448             nan     0.5000   -0.0063
##    360        0.5346             nan     0.5000   -0.0039
##    380        0.5268             nan     0.5000   -0.0026
##    400        0.5091             nan     0.5000   -0.0020
##    420        0.5067             nan     0.5000   -0.0034
##    440        0.5005             nan     0.5000   -0.0039
##    460        0.4961             nan     0.5000   -0.0073
##    480        0.4812             nan     0.5000   -0.0004
##    500        0.4795             nan     0.5000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1506             nan     0.5000    0.0489
##      2        1.0872             nan     0.5000    0.0272
##      3        1.0386             nan     0.5000    0.0240
##      4        0.9928             nan     0.5000    0.0179
##      5        0.9766             nan     0.5000    0.0025
##      6        0.9642             nan     0.5000   -0.0030
##      7        0.9453             nan     0.5000    0.0013
##      8        0.9317             nan     0.5000    0.0057
##      9        0.9107             nan     0.5000    0.0021
##     10        0.9076             nan     0.5000   -0.0059
##     20        0.8406             nan     0.5000   -0.0093
##     40        0.8084             nan     0.5000   -0.0071
##     60        0.7698             nan     0.5000   -0.0116
##     80        0.7476             nan     0.5000   -0.0125
##    100        0.7088             nan     0.5000   -0.0028
##    120        0.6984             nan     0.5000   -0.0035
##    140        0.6659             nan     0.5000   -0.0022
##    160        0.6587             nan     0.5000   -0.0044
##    180        0.6525             nan     0.5000   -0.0021
##    200        0.6225             nan     0.5000   -0.0067
##    220        0.6051             nan     0.5000   -0.0026
##    240        0.5931             nan     0.5000   -0.0021
##    260        0.5868             nan     0.5000   -0.0083
##    280        0.5726             nan     0.5000   -0.0027
##    300        0.5716             nan     0.5000   -0.0029
##    320        0.5569             nan     0.5000   -0.0032
##    340        0.5466             nan     0.5000   -0.0045
##    360        0.5389             nan     0.5000   -0.0019
##    380        0.5248             nan     0.5000   -0.0021
##    400        0.5144             nan     0.5000   -0.0043
##    420        0.5086             nan     0.5000   -0.0034
##    440        0.4955             nan     0.5000   -0.0031
##    460        0.4883             nan     0.5000   -0.0043
##    480        0.4841             nan     0.5000   -0.0029
##    500        0.4801             nan     0.5000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1602             nan     0.5000    0.0668
##      2        1.0976             nan     0.5000    0.0309
##      3        1.0479             nan     0.5000    0.0148
##      4        0.9998             nan     0.5000    0.0208
##      5        0.9734             nan     0.5000    0.0090
##      6        0.9420             nan     0.5000    0.0114
##      7        0.9275             nan     0.5000    0.0032
##      8        0.9134             nan     0.5000    0.0039
##      9        0.9056             nan     0.5000   -0.0035
##     10        0.8946             nan     0.5000   -0.0039
##     20        0.8405             nan     0.5000   -0.0047
##     40        0.7891             nan     0.5000   -0.0018
##     60        0.7597             nan     0.5000   -0.0021
##     80        0.7295             nan     0.5000   -0.0020
##    100        0.7184             nan     0.5000   -0.0040
##    120        0.6968             nan     0.5000   -0.0107
##    140        0.6745             nan     0.5000   -0.0019
##    160        0.6649             nan     0.5000   -0.0062
##    180        0.6462             nan     0.5000   -0.0080
##    200        0.6363             nan     0.5000   -0.0063
##    220        0.6200             nan     0.5000   -0.0054
##    240        0.6041             nan     0.5000   -0.0069
##    260        0.5911             nan     0.5000   -0.0093
##    280        0.5731             nan     0.5000   -0.0053
##    300        0.5685             nan     0.5000   -0.0054
##    320        0.5573             nan     0.5000   -0.0066
##    340        0.5550             nan     0.5000   -0.0018
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1274             nan     0.5000    0.0704
##      2        1.0336             nan     0.5000    0.0413
##      3        0.9897             nan     0.5000    0.0161
##      4        0.9577             nan     0.5000    0.0044
##      5        0.9160             nan     0.5000    0.0179
##      6        0.8922             nan     0.5000    0.0036
##      7        0.8867             nan     0.5000   -0.0144
##      8        0.8694             nan     0.5000   -0.0033
##      9        0.8786             nan     0.5000   -0.0236
##     10        0.8565             nan     0.5000    0.0017
##     20        0.7921             nan     0.5000   -0.0104
##     40        0.6825             nan     0.5000   -0.0129
##     60        0.6104             nan     0.5000   -0.0112
##     80        0.5439             nan     0.5000   -0.0089
##    100        0.4773             nan     0.5000   -0.0056
##    120        0.4338             nan     0.5000   -0.0033
##    140        0.3813             nan     0.5000   -0.0028
##    160        0.3388             nan     0.5000   -0.0100
##    180        0.3134             nan     0.5000   -0.0074
##    200        0.2864             nan     0.5000   -0.0082
##    220        0.2650             nan     0.5000   -0.0008
##    240        0.2466             nan     0.5000   -0.0014
##    260        0.2149             nan     0.5000   -0.0037
##    280        0.1910             nan     0.5000   -0.0039
##    300        0.1714             nan     0.5000   -0.0015
##    320        0.1617             nan     0.5000   -0.0025
##    340        0.1475             nan     0.5000   -0.0002
##    360        0.1340             nan     0.5000   -0.0015
##    380        0.1238             nan     0.5000   -0.0004
##    400        0.1128             nan     0.5000   -0.0013
##    420        0.1035             nan     0.5000   -0.0010
##    440        0.0968             nan     0.5000   -0.0004
##    460        0.0911             nan     0.5000   -0.0013
##    480        0.0844             nan     0.5000   -0.0014
##    500        0.0763             nan     0.5000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1131             nan     0.5000    0.0816
##      2        1.0314             nan     0.5000    0.0330
##      3        0.9630             nan     0.5000    0.0292
##      4        0.9231             nan     0.5000    0.0160
##      5        0.9067             nan     0.5000    0.0041
##      6        0.8946             nan     0.5000   -0.0088
##      7        0.8807             nan     0.5000   -0.0019
##      8        0.8707             nan     0.5000   -0.0076
##      9        0.8587             nan     0.5000   -0.0076
##     10        0.8464             nan     0.5000   -0.0036
##     20        0.7887             nan     0.5000   -0.0134
##     40        0.6840             nan     0.5000   -0.0045
##     60        0.6297             nan     0.5000   -0.0027
##     80        0.5491             nan     0.5000   -0.0098
##    100        0.4846             nan     0.5000   -0.0073
##    120        0.4374             nan     0.5000   -0.0089
##    140        0.4190             nan     0.5000   -0.0073
##    160        0.3573             nan     0.5000   -0.0035
##    180        0.3416             nan     0.5000   -0.0045
##    200       23.4553             nan     0.5000   -0.0006
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1150             nan     0.5000    0.0732
##      2        1.0424             nan     0.5000    0.0275
##      3        0.9814             nan     0.5000    0.0258
##      4        0.9350             nan     0.5000    0.0192
##      5        0.9070             nan     0.5000    0.0093
##      6        0.8862             nan     0.5000    0.0041
##      7        0.8646             nan     0.5000    0.0040
##      8        0.8608             nan     0.5000   -0.0143
##      9        0.8560             nan     0.5000   -0.0180
##     10        0.8407             nan     0.5000   -0.0020
##     20        0.7565             nan     0.5000   -0.0054
##     40        0.6966             nan     0.5000   -0.0090
##     60        0.6140             nan     0.5000   -0.0042
##     80        0.5407             nan     0.5000   -0.0044
##    100        0.4711             nan     0.5000   -0.0003
##    120        0.4140             nan     0.5000   -0.0022
##    140        0.3711             nan     0.5000   -0.0023
##    160        0.3344             nan     0.5000   -0.0048
##    180        0.3022             nan     0.5000   -0.0049
##    200        0.2656             nan     0.5000   -0.0037
##    220        0.2358             nan     0.5000   -0.0068
##    240        0.2179             nan     0.5000   -0.0055
##    260        0.1931             nan     0.5000   -0.0031
##    280        0.1705             nan     0.5000   -0.0010
##    300        0.1527             nan     0.5000   -0.0026
##    320        0.1355             nan     0.5000   -0.0013
##    340        0.1269             nan     0.5000   -0.0023
##    360        0.1149             nan     0.5000   -0.0006
##    380        0.1087             nan     0.5000   -0.0024
##    400        0.1023             nan     0.5000   -0.0013
##    420        0.0924             nan     0.5000   -0.0007
##    440        0.0880             nan     0.5000   -0.0016
##    460        0.0806             nan     0.5000   -0.0012
##    480        0.0763             nan     0.5000   -0.0008
##    500        0.0710             nan     0.5000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0756             nan     0.5000    0.0886
##      2        0.9822             nan     0.5000    0.0403
##      3        0.9137             nan     0.5000    0.0267
##      4        0.8925             nan     0.5000   -0.0073
##      5        0.8699             nan     0.5000   -0.0064
##      6        0.8608             nan     0.5000   -0.0087
##      7        0.8307             nan     0.5000    0.0035
##      8        0.8216             nan     0.5000   -0.0068
##      9        0.7961             nan     0.5000    0.0024
##     10        0.7848             nan     0.5000   -0.0057
##     20        0.6840             nan     0.5000   -0.0065
##     40        0.5528             nan     0.5000   -0.0213
##     60        0.4569             nan     0.5000   -0.0048
##     80        0.4536             nan     0.5000   -0.0062
##    100 22630244148.0991             nan     0.5000   -0.0021
##    120 22630244148.0591             nan     0.5000   -0.0069
##    140 22630244148.0099             nan     0.5000   -0.0017
##    160 22631440123.1145             nan     0.5000   -0.0034
##    180 22680938572.3939             nan     0.5000    0.0001
##    200 22680965263.3868             nan     0.5000   -0.0025
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0775             nan     0.5000    0.0949
##      2        1.0063             nan     0.5000    0.0155
##      3        0.9610             nan     0.5000    0.0013
##      4        0.9170             nan     0.5000    0.0074
##      5        0.8895             nan     0.5000   -0.0025
##      6        0.8661             nan     0.5000   -0.0056
##      7        0.8323             nan     0.5000    0.0076
##      8        0.8131             nan     0.5000   -0.0098
##      9        0.8062             nan     0.5000   -0.0114
##     10        0.7904             nan     0.5000   -0.0059
##     20        0.7022             nan     0.5000   -0.0089
##     40        0.5425             nan     0.5000   -0.0140
##     60        0.4699             nan     0.5000   -0.0128
##     80        0.3981             nan     0.5000   -0.0065
##    100        0.3264             nan     0.5000   -0.0004
##    120        0.2719             nan     0.5000   -0.0067
##    140        0.2222             nan     0.5000   -0.0048
##    160        0.1865             nan     0.5000   -0.0019
##    180        0.1570             nan     0.5000   -0.0006
##    200        0.1323             nan     0.5000   -0.0010
##    220        0.1183             nan     0.5000   -0.0013
##    240        0.0983             nan     0.5000   -0.0013
##    260        0.0874             nan     0.5000   -0.0009
##    280        0.0752             nan     0.5000   -0.0012
##    300        0.0657             nan     0.5000   -0.0000
##    320        0.0566             nan     0.5000   -0.0009
##    340        0.0510             nan     0.5000   -0.0009
##    360        0.0455             nan     0.5000   -0.0008
##    380        0.0409             nan     0.5000   -0.0005
##    400        0.0358             nan     0.5000   -0.0004
##    420        0.0321             nan     0.5000   -0.0004
##    440        0.0282             nan     0.5000   -0.0005
##    460        0.0249             nan     0.5000   -0.0004
##    480        0.0227             nan     0.5000   -0.0004
##    500        0.0192             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0827             nan     0.5000    0.0827
##      2        1.0068             nan     0.5000    0.0220
##      3        0.9590             nan     0.5000    0.0036
##      4        0.9296             nan     0.5000    0.0040
##      5        0.9214             nan     0.5000   -0.0188
##      6        0.8886             nan     0.5000    0.0029
##      7        0.8834             nan     0.5000   -0.0197
##      8        0.8677             nan     0.5000   -0.0169
##      9        0.8465             nan     0.5000   -0.0010
##     10        0.8472             nan     0.5000   -0.0176
##     20        0.7527             nan     0.5000   -0.0291
##     40        0.5742             nan     0.5000   -0.0036
##     60        0.4589             nan     0.5000   -0.0076
##     80        0.3763             nan     0.5000   -0.0100
##    100        0.3305             nan     0.5000   -0.0050
##    120        0.2829             nan     0.5000   -0.0174
##    140        0.2301             nan     0.5000   -0.0052
##    160        0.1882             nan     0.5000   -0.0045
##    180        0.1602             nan     0.5000   -0.0040
##    200        0.1390             nan     0.5000   -0.0025
##    220        0.1204             nan     0.5000   -0.0022
##    240        0.1064             nan     0.5000   -0.0014
##    260        0.0923             nan     0.5000   -0.0029
##    280        0.0794             nan     0.5000   -0.0010
##    300        0.0689             nan     0.5000   -0.0024
##    320        0.0600             nan     0.5000   -0.0007
##    340        0.0530             nan     0.5000   -0.0011
##    360        0.0454             nan     0.5000   -0.0007
##    380        0.0379             nan     0.5000   -0.0004
##    400        0.0329             nan     0.5000   -0.0003
##    420        0.0289             nan     0.5000   -0.0006
##    440        0.0259             nan     0.5000   -0.0002
##    460        0.0232             nan     0.5000   -0.0003
##    480        0.0204             nan     0.5000   -0.0004
##    500        0.0184             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1415             nan     1.0000    0.0477
##      2        1.0819             nan     1.0000    0.0192
##      3        1.0137             nan     1.0000    0.0228
##      4        0.9767             nan     1.0000   -0.0033
##      5        0.9807             nan     1.0000   -0.0202
##      6        0.9685             nan     1.0000   -0.0047
##      7        0.9523             nan     1.0000   -0.0152
##      8        0.9413             nan     1.0000    0.0016
##      9        0.9426             nan     1.0000   -0.0128
##     10        0.9359             nan     1.0000   -0.0055
##     20        0.9211             nan     1.0000   -0.0370
##     40        2.1122             nan     1.0000   -0.0171
##     60        2.4327             nan     1.0000   -0.0058
##     80        3.9531             nan     1.0000   -0.0007
##    100        3.9017             nan     1.0000   -0.0271
##    120       34.0067             nan     1.0000  -30.2730
##    140 234630371324689.1875             nan     1.0000   -0.0275
##    160 234630371324689.4688             nan     1.0000   -0.1714
##    180 234630371324689.1875             nan     1.0000    0.0003
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1148             nan     1.0000    0.0774
##      2        1.0441             nan     1.0000    0.0199
##      3        0.9822             nan     1.0000    0.0318
##      4        0.9885             nan     1.0000   -0.0263
##      5        0.9632             nan     1.0000    0.0053
##      6        0.9607             nan     1.0000   -0.0098
##      7        0.9602             nan     1.0000   -0.0223
##      8        0.9775             nan     1.0000   -0.0394
##      9        0.9280             nan     1.0000    0.0148
##     10        0.9351             nan     1.0000   -0.0295
##     20        0.8712             nan     1.0000   -0.0185
##     40        0.8049             nan     1.0000    0.0110
##     60        0.7838             nan     1.0000   -0.0018
##     80        0.8474             nan     1.0000   -0.0444
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1215             nan     1.0000    0.0606
##      2        1.0560             nan     1.0000    0.0116
##      3        1.0045             nan     1.0000    0.0146
##      4        0.9922             nan     1.0000   -0.0017
##      5        0.9730             nan     1.0000    0.0012
##      6        0.9553             nan     1.0000   -0.0012
##      7        0.9387             nan     1.0000   -0.0043
##      8        0.9260             nan     1.0000   -0.0117
##      9        0.8917             nan     1.0000    0.0127
##     10        0.8950             nan     1.0000   -0.0140
##     20        0.8816             nan     1.0000   -0.0260
##     40     5704.2166             nan     1.0000   -0.0143
##     60     5721.8659             nan     1.0000   -0.0129
##     80     5721.8614             nan     1.0000   -0.0042
##    100     5721.8882             nan     1.0000   -0.0615
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000   -0.0022
##    160           inf             nan     1.0000    0.0023
##    180           inf             nan     1.0000    0.0000
##    200 343685242.5997             nan     1.0000   -0.0011
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0585             nan     1.0000    0.0827
##      2        1.0099             nan     1.0000   -0.0055
##      3        0.9683             nan     1.0000   -0.0000
##      4        0.9592             nan     1.0000   -0.0165
##      5        0.9449             nan     1.0000   -0.0102
##      6        0.9414             nan     1.0000   -0.0286
##      7        0.9300             nan     1.0000   -0.0290
##      8        0.9339             nan     1.0000   -0.0385
##      9        0.8871             nan     1.0000    0.0098
##     10        0.9135             nan     1.0000   -0.0599
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0654             nan     1.0000    0.0694
##      2        0.9956             nan     1.0000    0.0128
##      3        0.9669             nan     1.0000   -0.0087
##      4        0.9978             nan     1.0000   -0.0806
##      5        1.0003             nan     1.0000   -0.0247
##      6        1.0069             nan     1.0000   -0.0363
##      7        1.0178             nan     1.0000   -0.0537
##      8        0.9954             nan     1.0000   -0.0089
##      9        0.9820             nan     1.0000   -0.0032
##     10        0.9988             nan     1.0000   -0.0316
##     20        2.2338             nan     1.0000    0.3198
##     40        1.7560             nan     1.0000   -0.0233
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0600             nan     1.0000    0.0985
##      2        0.9304             nan     1.0000    0.0380
##      3        0.9134             nan     1.0000   -0.0063
##      4        0.9274             nan     1.0000   -0.0275
##      5        0.9218             nan     1.0000   -0.0085
##      6        0.9142             nan     1.0000   -0.0192
##      7        1.1203             nan     1.0000   -0.1336
##      8        1.1182             nan     1.0000   -0.0187
##      9        1.1151             nan     1.0000   -0.0190
##     10        1.1011             nan     1.0000   -0.0212
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0199             nan     1.0000    0.1042
##      2        0.9746             nan     1.0000   -0.0148
##      3        0.9244             nan     1.0000   -0.0149
##      4        0.8752             nan     1.0000    0.0047
##      5        0.9299             nan     1.0000   -0.1092
##      6        0.8455             nan     1.0000    0.0259
##      7        0.8487             nan     1.0000   -0.0310
##      8        0.8439             nan     1.0000   -0.0217
##      9        0.8338             nan     1.0000   -0.0169
##     10        0.8282             nan     1.0000   -0.0273
##     20        0.7515             nan     1.0000   -0.0412
##     40        0.9360             nan     1.0000   -0.0119
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0681             nan     1.0000    0.0708
##      2        1.0003             nan     1.0000   -0.0005
##      3        0.9393             nan     1.0000    0.0063
##      4        0.9153             nan     1.0000   -0.0206
##      5        0.8680             nan     1.0000   -0.0134
##      6        0.8388             nan     1.0000    0.0018
##      7        0.8251             nan     1.0000   -0.0111
##      8        0.8178             nan     1.0000   -0.0284
##      9        1.0269             nan     1.0000   -0.2314
##     10 5235298466.0151             nan     1.0000 -5227733004.9460
##     20 5235999052.3480             nan     1.0000   -0.0220
##     40 5235999052.0172             nan     1.0000   -0.0381
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120 117335725592.7703             nan     1.0000   -0.0273
##    140 117480139077.8864             nan     1.0000   -0.0049
##    160 117480139081.7836             nan     1.0000    0.0070
##    180 117480139081.7830             nan     1.0000   -0.0131
##    200 117480139081.7092             nan     1.0000   -0.0095
##    220 117480139081.6291             nan     1.0000    0.0064
##    240 117480139081.5282             nan     1.0000    0.0041
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0298             nan     1.0000    0.1343
##      2        0.9483             nan     1.0000    0.0097
##      3        0.9262             nan     1.0000   -0.0258
##      4        0.8939             nan     1.0000   -0.0017
##      5        0.9006             nan     1.0000   -0.0307
##      6        0.9028             nan     1.0000   -0.0265
##      7        0.9125             nan     1.0000   -0.0519
##      8        0.9228             nan     1.0000   -0.0527
##      9        0.9025             nan     1.0000   -0.0576
##     10        0.9227             nan     1.0000   -0.0776
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0001
##     20        1.2860             nan     0.0010    0.0002
##     40        1.2788             nan     0.0010    0.0001
##     60        1.2720             nan     0.0010    0.0002
##     80        1.2654             nan     0.0010    0.0001
##    100        1.2589             nan     0.0010    0.0002
##    120        1.2529             nan     0.0010    0.0001
##    140        1.2470             nan     0.0010    0.0001
##    160        1.2414             nan     0.0010    0.0001
##    180        1.2360             nan     0.0010    0.0001
##    200        1.2307             nan     0.0010    0.0001
##    220        1.2255             nan     0.0010    0.0001
##    240        1.2205             nan     0.0010    0.0001
##    260        1.2156             nan     0.0010    0.0001
##    280        1.2108             nan     0.0010    0.0001
##    300        1.2062             nan     0.0010    0.0001
##    320        1.2018             nan     0.0010    0.0001
##    340        1.1977             nan     0.0010    0.0001
##    360        1.1933             nan     0.0010    0.0001
##    380        1.1892             nan     0.0010    0.0001
##    400        1.1852             nan     0.0010    0.0001
##    420        1.1811             nan     0.0010    0.0001
##    440        1.1771             nan     0.0010    0.0001
##    460        1.1734             nan     0.0010    0.0001
##    480        1.1698             nan     0.0010    0.0001
##    500        1.1660             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0001
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0001
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2862             nan     0.0010    0.0002
##     40        1.2790             nan     0.0010    0.0002
##     60        1.2721             nan     0.0010    0.0002
##     80        1.2655             nan     0.0010    0.0002
##    100        1.2594             nan     0.0010    0.0001
##    120        1.2533             nan     0.0010    0.0001
##    140        1.2475             nan     0.0010    0.0001
##    160        1.2417             nan     0.0010    0.0001
##    180        1.2362             nan     0.0010    0.0001
##    200        1.2308             nan     0.0010    0.0001
##    220        1.2257             nan     0.0010    0.0001
##    240        1.2205             nan     0.0010    0.0001
##    260        1.2157             nan     0.0010    0.0001
##    280        1.2110             nan     0.0010    0.0001
##    300        1.2065             nan     0.0010    0.0001
##    320        1.2020             nan     0.0010    0.0001
##    340        1.1975             nan     0.0010    0.0001
##    360        1.1933             nan     0.0010    0.0001
##    380        1.1892             nan     0.0010    0.0001
##    400        1.1852             nan     0.0010    0.0001
##    420        1.1812             nan     0.0010    0.0001
##    440        1.1774             nan     0.0010    0.0001
##    460        1.1737             nan     0.0010    0.0001
##    480        1.1701             nan     0.0010    0.0001
##    500        1.1665             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2913             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2906             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0001
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2895             nan     0.0010    0.0001
##     20        1.2859             nan     0.0010    0.0002
##     40        1.2786             nan     0.0010    0.0002
##     60        1.2720             nan     0.0010    0.0001
##     80        1.2655             nan     0.0010    0.0002
##    100        1.2593             nan     0.0010    0.0002
##    120        1.2529             nan     0.0010    0.0001
##    140        1.2467             nan     0.0010    0.0001
##    160        1.2410             nan     0.0010    0.0001
##    180        1.2357             nan     0.0010    0.0001
##    200        1.2305             nan     0.0010    0.0001
##    220        1.2253             nan     0.0010    0.0001
##    240        1.2204             nan     0.0010    0.0001
##    260        1.2157             nan     0.0010    0.0001
##    280        1.2108             nan     0.0010    0.0001
##    300        1.2063             nan     0.0010    0.0001
##    320        1.2017             nan     0.0010    0.0001
##    340        1.1975             nan     0.0010    0.0001
##    360        1.1934             nan     0.0010    0.0001
##    380        1.1894             nan     0.0010    0.0001
##    400        1.1855             nan     0.0010    0.0001
##    420        1.1814             nan     0.0010    0.0001
##    440        1.1775             nan     0.0010    0.0001
##    460        1.1738             nan     0.0010    0.0001
##    480        1.1702             nan     0.0010    0.0000
##    500        1.1664             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0003
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2889             nan     0.0010    0.0002
##     10        1.2884             nan     0.0010    0.0002
##     20        1.2838             nan     0.0010    0.0002
##     40        1.2747             nan     0.0010    0.0002
##     60        1.2662             nan     0.0010    0.0002
##     80        1.2576             nan     0.0010    0.0002
##    100        1.2496             nan     0.0010    0.0002
##    120        1.2415             nan     0.0010    0.0002
##    140        1.2338             nan     0.0010    0.0002
##    160        1.2263             nan     0.0010    0.0002
##    180        1.2191             nan     0.0010    0.0002
##    200        1.2122             nan     0.0010    0.0002
##    220        1.2053             nan     0.0010    0.0002
##    240        1.1988             nan     0.0010    0.0001
##    260        1.1922             nan     0.0010    0.0001
##    280        1.1859             nan     0.0010    0.0001
##    300        1.1797             nan     0.0010    0.0001
##    320        1.1739             nan     0.0010    0.0001
##    340        1.1680             nan     0.0010    0.0001
##    360        1.1624             nan     0.0010    0.0001
##    380        1.1570             nan     0.0010    0.0001
##    400        1.1516             nan     0.0010    0.0001
##    420        1.1463             nan     0.0010    0.0001
##    440        1.1412             nan     0.0010    0.0001
##    460        1.1361             nan     0.0010    0.0001
##    480        1.1312             nan     0.0010    0.0001
##    500        1.1266             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0001
##     20        1.2839             nan     0.0010    0.0002
##     40        1.2747             nan     0.0010    0.0002
##     60        1.2660             nan     0.0010    0.0002
##     80        1.2576             nan     0.0010    0.0002
##    100        1.2495             nan     0.0010    0.0002
##    120        1.2414             nan     0.0010    0.0002
##    140        1.2338             nan     0.0010    0.0001
##    160        1.2264             nan     0.0010    0.0002
##    180        1.2187             nan     0.0010    0.0001
##    200        1.2119             nan     0.0010    0.0002
##    220        1.2049             nan     0.0010    0.0001
##    240        1.1984             nan     0.0010    0.0002
##    260        1.1922             nan     0.0010    0.0001
##    280        1.1860             nan     0.0010    0.0001
##    300        1.1798             nan     0.0010    0.0001
##    320        1.1737             nan     0.0010    0.0001
##    340        1.1682             nan     0.0010    0.0001
##    360        1.1626             nan     0.0010    0.0001
##    380        1.1570             nan     0.0010    0.0001
##    400        1.1516             nan     0.0010    0.0001
##    420        1.1464             nan     0.0010    0.0001
##    440        1.1413             nan     0.0010    0.0001
##    460        1.1364             nan     0.0010    0.0001
##    480        1.1314             nan     0.0010    0.0001
##    500        1.1265             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2840             nan     0.0010    0.0002
##     40        1.2749             nan     0.0010    0.0002
##     60        1.2661             nan     0.0010    0.0002
##     80        1.2575             nan     0.0010    0.0002
##    100        1.2494             nan     0.0010    0.0002
##    120        1.2415             nan     0.0010    0.0002
##    140        1.2338             nan     0.0010    0.0002
##    160        1.2262             nan     0.0010    0.0002
##    180        1.2189             nan     0.0010    0.0002
##    200        1.2118             nan     0.0010    0.0001
##    220        1.2050             nan     0.0010    0.0002
##    240        1.1985             nan     0.0010    0.0001
##    260        1.1921             nan     0.0010    0.0001
##    280        1.1858             nan     0.0010    0.0002
##    300        1.1796             nan     0.0010    0.0001
##    320        1.1735             nan     0.0010    0.0001
##    340        1.1678             nan     0.0010    0.0001
##    360        1.1622             nan     0.0010    0.0001
##    380        1.1567             nan     0.0010    0.0001
##    400        1.1512             nan     0.0010    0.0001
##    420        1.1459             nan     0.0010    0.0001
##    440        1.1408             nan     0.0010    0.0001
##    460        1.1358             nan     0.0010    0.0001
##    480        1.1309             nan     0.0010    0.0001
##    500        1.1262             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2916             nan     0.0010    0.0003
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2905             nan     0.0010    0.0003
##      6        1.2899             nan     0.0010    0.0003
##      7        1.2894             nan     0.0010    0.0002
##      8        1.2888             nan     0.0010    0.0003
##      9        1.2883             nan     0.0010    0.0002
##     10        1.2877             nan     0.0010    0.0003
##     20        1.2825             nan     0.0010    0.0003
##     40        1.2720             nan     0.0010    0.0002
##     60        1.2620             nan     0.0010    0.0002
##     80        1.2522             nan     0.0010    0.0002
##    100        1.2428             nan     0.0010    0.0002
##    120        1.2339             nan     0.0010    0.0002
##    140        1.2250             nan     0.0010    0.0002
##    160        1.2166             nan     0.0010    0.0002
##    180        1.2084             nan     0.0010    0.0002
##    200        1.2005             nan     0.0010    0.0002
##    220        1.1927             nan     0.0010    0.0002
##    240        1.1850             nan     0.0010    0.0001
##    260        1.1777             nan     0.0010    0.0002
##    280        1.1703             nan     0.0010    0.0002
##    300        1.1633             nan     0.0010    0.0001
##    320        1.1564             nan     0.0010    0.0002
##    340        1.1499             nan     0.0010    0.0001
##    360        1.1435             nan     0.0010    0.0002
##    380        1.1373             nan     0.0010    0.0001
##    400        1.1312             nan     0.0010    0.0001
##    420        1.1254             nan     0.0010    0.0001
##    440        1.1198             nan     0.0010    0.0001
##    460        1.1141             nan     0.0010    0.0001
##    480        1.1086             nan     0.0010    0.0001
##    500        1.1032             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2897             nan     0.0010    0.0003
##      8        1.2891             nan     0.0010    0.0003
##      9        1.2885             nan     0.0010    0.0003
##     10        1.2879             nan     0.0010    0.0003
##     20        1.2827             nan     0.0010    0.0002
##     40        1.2726             nan     0.0010    0.0002
##     60        1.2629             nan     0.0010    0.0002
##     80        1.2533             nan     0.0010    0.0002
##    100        1.2436             nan     0.0010    0.0002
##    120        1.2347             nan     0.0010    0.0002
##    140        1.2260             nan     0.0010    0.0002
##    160        1.2176             nan     0.0010    0.0002
##    180        1.2095             nan     0.0010    0.0002
##    200        1.2015             nan     0.0010    0.0002
##    220        1.1939             nan     0.0010    0.0002
##    240        1.1862             nan     0.0010    0.0001
##    260        1.1786             nan     0.0010    0.0002
##    280        1.1712             nan     0.0010    0.0001
##    300        1.1642             nan     0.0010    0.0001
##    320        1.1574             nan     0.0010    0.0001
##    340        1.1508             nan     0.0010    0.0001
##    360        1.1442             nan     0.0010    0.0002
##    380        1.1378             nan     0.0010    0.0002
##    400        1.1318             nan     0.0010    0.0001
##    420        1.1255             nan     0.0010    0.0002
##    440        1.1198             nan     0.0010    0.0001
##    460        1.1142             nan     0.0010    0.0001
##    480        1.1088             nan     0.0010    0.0001
##    500        1.1033             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0003
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0003
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0003
##     10        1.2880             nan     0.0010    0.0003
##     20        1.2826             nan     0.0010    0.0002
##     40        1.2720             nan     0.0010    0.0002
##     60        1.2621             nan     0.0010    0.0002
##     80        1.2526             nan     0.0010    0.0002
##    100        1.2433             nan     0.0010    0.0002
##    120        1.2343             nan     0.0010    0.0002
##    140        1.2254             nan     0.0010    0.0002
##    160        1.2166             nan     0.0010    0.0002
##    180        1.2084             nan     0.0010    0.0002
##    200        1.2004             nan     0.0010    0.0002
##    220        1.1923             nan     0.0010    0.0002
##    240        1.1846             nan     0.0010    0.0001
##    260        1.1774             nan     0.0010    0.0002
##    280        1.1703             nan     0.0010    0.0001
##    300        1.1635             nan     0.0010    0.0001
##    320        1.1567             nan     0.0010    0.0001
##    340        1.1502             nan     0.0010    0.0002
##    360        1.1438             nan     0.0010    0.0001
##    380        1.1374             nan     0.0010    0.0002
##    400        1.1313             nan     0.0010    0.0001
##    420        1.1254             nan     0.0010    0.0001
##    440        1.1198             nan     0.0010    0.0001
##    460        1.1143             nan     0.0010    0.0001
##    480        1.1088             nan     0.0010    0.0001
##    500        1.1034             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2636             nan     0.1000    0.0143
##      2        1.2303             nan     0.1000    0.0143
##      3        1.2036             nan     0.1000    0.0080
##      4        1.1788             nan     0.1000    0.0112
##      5        1.1578             nan     0.1000    0.0076
##      6        1.1398             nan     0.1000    0.0066
##      7        1.1243             nan     0.1000    0.0057
##      8        1.1099             nan     0.1000    0.0047
##      9        1.0993             nan     0.1000    0.0035
##     10        1.0876             nan     0.1000    0.0052
##     20        1.0009             nan     0.1000    0.0031
##     40        0.9215             nan     0.1000    0.0007
##     60        0.8785             nan     0.1000   -0.0006
##     80        0.8502             nan     0.1000   -0.0004
##    100        0.8311             nan     0.1000   -0.0009
##    120        0.8186             nan     0.1000   -0.0004
##    140        0.8045             nan     0.1000   -0.0003
##    160        0.7949             nan     0.1000   -0.0005
##    180        0.7861             nan     0.1000   -0.0011
##    200        0.7785             nan     0.1000   -0.0005
##    220        0.7690             nan     0.1000   -0.0009
##    240        0.7641             nan     0.1000   -0.0004
##    260        0.7590             nan     0.1000   -0.0006
##    280        0.7528             nan     0.1000   -0.0004
##    300        0.7454             nan     0.1000   -0.0008
##    320        0.7410             nan     0.1000   -0.0016
##    340        0.7328             nan     0.1000   -0.0006
##    360        0.7285             nan     0.1000   -0.0016
##    380        0.7243             nan     0.1000   -0.0006
##    400        0.7182             nan     0.1000   -0.0011
##    420        0.7133             nan     0.1000   -0.0010
##    440        0.7096             nan     0.1000   -0.0007
##    460        0.7032             nan     0.1000   -0.0001
##    480        0.6986             nan     0.1000   -0.0007
##    500        0.6935             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2568             nan     0.1000    0.0172
##      2        1.2290             nan     0.1000    0.0142
##      3        1.2106             nan     0.1000    0.0058
##      4        1.1859             nan     0.1000    0.0119
##      5        1.1627             nan     0.1000    0.0091
##      6        1.1470             nan     0.1000    0.0062
##      7        1.1315             nan     0.1000    0.0065
##      8        1.1160             nan     0.1000    0.0057
##      9        1.1041             nan     0.1000    0.0045
##     10        1.0934             nan     0.1000    0.0045
##     20        1.0069             nan     0.1000    0.0032
##     40        0.9210             nan     0.1000   -0.0004
##     60        0.8795             nan     0.1000   -0.0003
##     80        0.8532             nan     0.1000   -0.0012
##    100        0.8323             nan     0.1000   -0.0012
##    120        0.8175             nan     0.1000   -0.0011
##    140        0.8051             nan     0.1000   -0.0009
##    160        0.7939             nan     0.1000   -0.0016
##    180        0.7855             nan     0.1000   -0.0014
##    200        0.7720             nan     0.1000    0.0000
##    220        0.7645             nan     0.1000   -0.0006
##    240        0.7577             nan     0.1000   -0.0006
##    260        0.7528             nan     0.1000   -0.0010
##    280        0.7473             nan     0.1000   -0.0009
##    300        0.7413             nan     0.1000   -0.0006
##    320        0.7349             nan     0.1000   -0.0005
##    340        0.7302             nan     0.1000   -0.0008
##    360        0.7245             nan     0.1000   -0.0015
##    380        0.7173             nan     0.1000   -0.0009
##    400        0.7112             nan     0.1000   -0.0006
##    420        0.7048             nan     0.1000   -0.0006
##    440        0.7016             nan     0.1000   -0.0010
##    460        0.6985             nan     0.1000   -0.0014
##    480        0.6939             nan     0.1000   -0.0006
##    500        0.6908             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2596             nan     0.1000    0.0141
##      2        1.2318             nan     0.1000    0.0153
##      3        1.2099             nan     0.1000    0.0106
##      4        1.1839             nan     0.1000    0.0092
##      5        1.1634             nan     0.1000    0.0082
##      6        1.1478             nan     0.1000    0.0061
##      7        1.1332             nan     0.1000    0.0058
##      8        1.1187             nan     0.1000    0.0060
##      9        1.1041             nan     0.1000    0.0062
##     10        1.0918             nan     0.1000    0.0044
##     20        1.0026             nan     0.1000    0.0027
##     40        0.9202             nan     0.1000    0.0001
##     60        0.8788             nan     0.1000   -0.0007
##     80        0.8502             nan     0.1000   -0.0011
##    100        0.8298             nan     0.1000   -0.0006
##    120        0.8141             nan     0.1000   -0.0003
##    140        0.8042             nan     0.1000   -0.0006
##    160        0.7964             nan     0.1000   -0.0003
##    180        0.7881             nan     0.1000   -0.0004
##    200        0.7821             nan     0.1000   -0.0001
##    220        0.7761             nan     0.1000   -0.0010
##    240        0.7710             nan     0.1000   -0.0007
##    260        0.7645             nan     0.1000   -0.0003
##    280        0.7569             nan     0.1000   -0.0010
##    300        0.7500             nan     0.1000   -0.0005
##    320        0.7423             nan     0.1000   -0.0000
##    340        0.7366             nan     0.1000   -0.0012
##    360        0.7295             nan     0.1000   -0.0008
##    380        0.7251             nan     0.1000   -0.0008
##    400        0.7200             nan     0.1000   -0.0011
##    420        0.7164             nan     0.1000   -0.0003
##    440        0.7111             nan     0.1000   -0.0008
##    460        0.7075             nan     0.1000   -0.0005
##    480        0.7015             nan     0.1000   -0.0011
##    500        0.6972             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2503             nan     0.1000    0.0181
##      2        1.2106             nan     0.1000    0.0194
##      3        1.1796             nan     0.1000    0.0143
##      4        1.1501             nan     0.1000    0.0125
##      5        1.1273             nan     0.1000    0.0090
##      6        1.1076             nan     0.1000    0.0098
##      7        1.0853             nan     0.1000    0.0080
##      8        1.0660             nan     0.1000    0.0078
##      9        1.0483             nan     0.1000    0.0070
##     10        1.0324             nan     0.1000    0.0054
##     20        0.9372             nan     0.1000    0.0003
##     40        0.8475             nan     0.1000   -0.0006
##     60        0.7999             nan     0.1000   -0.0009
##     80        0.7624             nan     0.1000   -0.0011
##    100        0.7357             nan     0.1000   -0.0002
##    120        0.7081             nan     0.1000   -0.0002
##    140        0.6860             nan     0.1000   -0.0009
##    160        0.6660             nan     0.1000   -0.0021
##    180        0.6451             nan     0.1000   -0.0007
##    200        0.6263             nan     0.1000   -0.0005
##    220        0.6070             nan     0.1000   -0.0015
##    240        0.5912             nan     0.1000   -0.0014
##    260        0.5725             nan     0.1000   -0.0006
##    280        0.5585             nan     0.1000   -0.0004
##    300        0.5411             nan     0.1000   -0.0008
##    320        0.5297             nan     0.1000   -0.0016
##    340        0.5177             nan     0.1000   -0.0008
##    360        0.5052             nan     0.1000   -0.0015
##    380        0.4934             nan     0.1000   -0.0010
##    400        0.4827             nan     0.1000   -0.0003
##    420        0.4690             nan     0.1000   -0.0008
##    440        0.4581             nan     0.1000   -0.0006
##    460        0.4469             nan     0.1000   -0.0005
##    480        0.4369             nan     0.1000   -0.0012
##    500        0.4274             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2443             nan     0.1000    0.0226
##      2        1.2091             nan     0.1000    0.0171
##      3        1.1811             nan     0.1000    0.0134
##      4        1.1521             nan     0.1000    0.0132
##      5        1.1271             nan     0.1000    0.0105
##      6        1.1031             nan     0.1000    0.0112
##      7        1.0860             nan     0.1000    0.0052
##      8        1.0704             nan     0.1000    0.0068
##      9        1.0533             nan     0.1000    0.0067
##     10        1.0418             nan     0.1000    0.0041
##     20        0.9386             nan     0.1000    0.0012
##     40        0.8486             nan     0.1000   -0.0016
##     60        0.8007             nan     0.1000   -0.0017
##     80        0.7675             nan     0.1000   -0.0007
##    100        0.7319             nan     0.1000   -0.0022
##    120        0.7111             nan     0.1000   -0.0022
##    140        0.6901             nan     0.1000   -0.0012
##    160        0.6721             nan     0.1000   -0.0022
##    180        0.6533             nan     0.1000   -0.0011
##    200        0.6330             nan     0.1000   -0.0007
##    220        0.6171             nan     0.1000   -0.0016
##    240        0.6003             nan     0.1000   -0.0009
##    260        0.5840             nan     0.1000   -0.0014
##    280        0.5657             nan     0.1000   -0.0013
##    300        0.5499             nan     0.1000   -0.0006
##    320        0.5367             nan     0.1000   -0.0011
##    340        0.5249             nan     0.1000   -0.0011
##    360        0.5110             nan     0.1000   -0.0009
##    380        0.4993             nan     0.1000   -0.0005
##    400        0.4856             nan     0.1000   -0.0006
##    420        0.4720             nan     0.1000   -0.0005
##    440        0.4629             nan     0.1000   -0.0010
##    460        0.4525             nan     0.1000   -0.0006
##    480        0.4441             nan     0.1000   -0.0015
##    500        0.4336             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2477             nan     0.1000    0.0224
##      2        1.2166             nan     0.1000    0.0151
##      3        1.1802             nan     0.1000    0.0137
##      4        1.1509             nan     0.1000    0.0110
##      5        1.1266             nan     0.1000    0.0086
##      6        1.1033             nan     0.1000    0.0110
##      7        1.0815             nan     0.1000    0.0093
##      8        1.0667             nan     0.1000    0.0045
##      9        1.0498             nan     0.1000    0.0062
##     10        1.0346             nan     0.1000    0.0066
##     20        0.9378             nan     0.1000    0.0026
##     40        0.8500             nan     0.1000   -0.0010
##     60        0.8025             nan     0.1000   -0.0022
##     80        0.7691             nan     0.1000   -0.0007
##    100        0.7406             nan     0.1000   -0.0011
##    120        0.7159             nan     0.1000   -0.0012
##    140        0.6914             nan     0.1000   -0.0020
##    160        0.6717             nan     0.1000   -0.0009
##    180        0.6546             nan     0.1000   -0.0014
##    200        0.6376             nan     0.1000   -0.0013
##    220        0.6228             nan     0.1000   -0.0019
##    240        0.6084             nan     0.1000   -0.0011
##    260        0.5905             nan     0.1000   -0.0007
##    280        0.5720             nan     0.1000   -0.0008
##    300        0.5575             nan     0.1000   -0.0010
##    320        0.5448             nan     0.1000   -0.0010
##    340        0.5307             nan     0.1000   -0.0018
##    360        0.5184             nan     0.1000   -0.0016
##    380        0.5071             nan     0.1000   -0.0012
##    400        0.4953             nan     0.1000   -0.0018
##    420        0.4840             nan     0.1000   -0.0007
##    440        0.4727             nan     0.1000   -0.0010
##    460        0.4633             nan     0.1000   -0.0010
##    480        0.4562             nan     0.1000   -0.0009
##    500        0.4473             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2436             nan     0.1000    0.0182
##      2        1.2015             nan     0.1000    0.0178
##      3        1.1620             nan     0.1000    0.0141
##      4        1.1282             nan     0.1000    0.0163
##      5        1.1027             nan     0.1000    0.0089
##      6        1.0787             nan     0.1000    0.0098
##      7        1.0554             nan     0.1000    0.0096
##      8        1.0353             nan     0.1000    0.0064
##      9        1.0140             nan     0.1000    0.0078
##     10        0.9953             nan     0.1000    0.0068
##     20        0.8915             nan     0.1000    0.0009
##     40        0.7945             nan     0.1000   -0.0020
##     60        0.7302             nan     0.1000   -0.0015
##     80        0.6883             nan     0.1000   -0.0021
##    100        0.6490             nan     0.1000   -0.0014
##    120        0.6211             nan     0.1000   -0.0022
##    140        0.5886             nan     0.1000   -0.0019
##    160        0.5555             nan     0.1000   -0.0012
##    180        0.5330             nan     0.1000   -0.0020
##    200        0.5091             nan     0.1000   -0.0005
##    220        0.4897             nan     0.1000   -0.0004
##    240        0.4701             nan     0.1000   -0.0004
##    260        0.4482             nan     0.1000   -0.0005
##    280        0.4282             nan     0.1000   -0.0006
##    300        0.4101             nan     0.1000   -0.0007
##    320        0.3939             nan     0.1000   -0.0005
##    340        0.3766             nan     0.1000   -0.0008
##    360        0.3601             nan     0.1000   -0.0007
##    380        0.3484             nan     0.1000   -0.0009
##    400        0.3357             nan     0.1000   -0.0004
##    420        0.3233             nan     0.1000   -0.0006
##    440        0.3140             nan     0.1000   -0.0007
##    460        0.3015             nan     0.1000   -0.0003
##    480        0.2916             nan     0.1000   -0.0006
##    500        0.2816             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2428             nan     0.1000    0.0240
##      2        1.1950             nan     0.1000    0.0214
##      3        1.1590             nan     0.1000    0.0157
##      4        1.1236             nan     0.1000    0.0156
##      5        1.0951             nan     0.1000    0.0099
##      6        1.0729             nan     0.1000    0.0076
##      7        1.0512             nan     0.1000    0.0071
##      8        1.0313             nan     0.1000    0.0051
##      9        1.0147             nan     0.1000    0.0056
##     10        0.9967             nan     0.1000    0.0068
##     20        0.8927             nan     0.1000    0.0017
##     40        0.8004             nan     0.1000   -0.0001
##     60        0.7435             nan     0.1000   -0.0017
##     80        0.6949             nan     0.1000   -0.0017
##    100        0.6591             nan     0.1000   -0.0008
##    120        0.6193             nan     0.1000   -0.0005
##    140        0.5863             nan     0.1000   -0.0006
##    160        0.5613             nan     0.1000   -0.0014
##    180        0.5347             nan     0.1000   -0.0000
##    200        0.5115             nan     0.1000   -0.0014
##    220        0.4878             nan     0.1000   -0.0014
##    240        0.4670             nan     0.1000   -0.0009
##    260        0.4492             nan     0.1000   -0.0006
##    280        0.4286             nan     0.1000   -0.0010
##    300        0.4096             nan     0.1000   -0.0010
##    320        0.3940             nan     0.1000   -0.0006
##    340        0.3776             nan     0.1000   -0.0019
##    360        0.3616             nan     0.1000   -0.0000
##    380        0.3473             nan     0.1000   -0.0006
##    400        0.3360             nan     0.1000   -0.0014
##    420        0.3253             nan     0.1000   -0.0005
##    440        0.3125             nan     0.1000   -0.0013
##    460        0.3010             nan     0.1000   -0.0010
##    480        0.2904             nan     0.1000   -0.0009
##    500        0.2787             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2414             nan     0.1000    0.0237
##      2        1.2022             nan     0.1000    0.0161
##      3        1.1651             nan     0.1000    0.0173
##      4        1.1299             nan     0.1000    0.0154
##      5        1.1013             nan     0.1000    0.0110
##      6        1.0780             nan     0.1000    0.0094
##      7        1.0584             nan     0.1000    0.0075
##      8        1.0386             nan     0.1000    0.0079
##      9        1.0206             nan     0.1000    0.0045
##     10        1.0029             nan     0.1000    0.0048
##     20        0.9035             nan     0.1000    0.0020
##     40        0.8004             nan     0.1000   -0.0013
##     60        0.7423             nan     0.1000   -0.0016
##     80        0.6962             nan     0.1000   -0.0007
##    100        0.6555             nan     0.1000   -0.0027
##    120        0.6196             nan     0.1000   -0.0003
##    140        0.5847             nan     0.1000   -0.0005
##    160        0.5548             nan     0.1000   -0.0009
##    180        0.5234             nan     0.1000   -0.0007
##    200        0.4999             nan     0.1000   -0.0014
##    220        0.4794             nan     0.1000   -0.0006
##    240        0.4613             nan     0.1000   -0.0018
##    260        0.4433             nan     0.1000   -0.0017
##    280        0.4219             nan     0.1000   -0.0014
##    300        0.4051             nan     0.1000   -0.0014
##    320        0.3852             nan     0.1000   -0.0008
##    340        0.3674             nan     0.1000   -0.0005
##    360        0.3527             nan     0.1000   -0.0006
##    380        0.3368             nan     0.1000   -0.0009
##    400        0.3195             nan     0.1000   -0.0011
##    420        0.3062             nan     0.1000   -0.0009
##    440        0.2952             nan     0.1000   -0.0009
##    460        0.2860             nan     0.1000   -0.0007
##    480        0.2731             nan     0.1000   -0.0002
##    500        0.2626             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2231             nan     0.2000    0.0321
##      2        1.1789             nan     0.2000    0.0192
##      3        1.1476             nan     0.2000    0.0130
##      4        1.1147             nan     0.2000    0.0128
##      5        1.0821             nan     0.2000    0.0108
##      6        1.0639             nan     0.2000    0.0060
##      7        1.0465             nan     0.2000    0.0075
##      8        1.0273             nan     0.2000    0.0070
##      9        1.0144             nan     0.2000    0.0050
##     10        1.0035             nan     0.2000    0.0037
##     20        0.9206             nan     0.2000   -0.0008
##     40        0.8535             nan     0.2000   -0.0031
##     60        0.8182             nan     0.2000   -0.0007
##     80        0.7962             nan     0.2000   -0.0012
##    100        0.7804             nan     0.2000   -0.0056
##    120        0.7642             nan     0.2000   -0.0020
##    140        0.7517             nan     0.2000   -0.0033
##    160        0.7403             nan     0.2000   -0.0018
##    180        0.7309             nan     0.2000   -0.0018
##    200        0.7217             nan     0.2000   -0.0018
##    220        0.7129             nan     0.2000   -0.0007
##    240        0.7017             nan     0.2000   -0.0010
##    260        0.6940             nan     0.2000   -0.0009
##    280        0.6832             nan     0.2000   -0.0011
##    300        0.6696             nan     0.2000   -0.0006
##    320        0.6619             nan     0.2000   -0.0022
##    340        0.6533             nan     0.2000   -0.0017
##    360        0.6487             nan     0.2000   -0.0015
##    380        0.6413             nan     0.2000   -0.0044
##    400        0.6351             nan     0.2000   -0.0013
##    420        0.6309             nan     0.2000   -0.0022
##    440        0.6252             nan     0.2000   -0.0004
##    460        0.6208             nan     0.2000   -0.0025
##    480        0.6137             nan     0.2000   -0.0017
##    500        0.6088             nan     0.2000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2244             nan     0.2000    0.0330
##      2        1.1875             nan     0.2000    0.0136
##      3        1.1502             nan     0.2000    0.0192
##      4        1.1157             nan     0.2000    0.0151
##      5        1.0948             nan     0.2000    0.0078
##      6        1.0706             nan     0.2000    0.0085
##      7        1.0529             nan     0.2000    0.0043
##      8        1.0336             nan     0.2000    0.0103
##      9        1.0224             nan     0.2000    0.0013
##     10        1.0058             nan     0.2000    0.0067
##     20        0.9182             nan     0.2000    0.0025
##     40        0.8595             nan     0.2000   -0.0006
##     60        0.8371             nan     0.2000   -0.0048
##     80        0.8116             nan     0.2000   -0.0006
##    100        0.7875             nan     0.2000   -0.0030
##    120        0.7710             nan     0.2000   -0.0013
##    140        0.7529             nan     0.2000   -0.0028
##    160        0.7425             nan     0.2000   -0.0039
##    180        0.7312             nan     0.2000   -0.0001
##    200        0.7210             nan     0.2000   -0.0025
##    220        0.7118             nan     0.2000   -0.0008
##    240        0.7029             nan     0.2000   -0.0007
##    260        0.6938             nan     0.2000   -0.0025
##    280        0.6863             nan     0.2000   -0.0013
##    300        0.6811             nan     0.2000   -0.0009
##    320        0.6767             nan     0.2000   -0.0014
##    340        0.6697             nan     0.2000   -0.0020
##    360        0.6652             nan     0.2000   -0.0008
##    380        0.6565             nan     0.2000   -0.0038
##    400        0.6522             nan     0.2000   -0.0026
##    420        0.6444             nan     0.2000   -0.0015
##    440        0.6398             nan     0.2000   -0.0019
##    460        0.6360             nan     0.2000   -0.0008
##    480        0.6302             nan     0.2000   -0.0025
##    500        0.6266             nan     0.2000   -0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2202             nan     0.2000    0.0307
##      2        1.1777             nan     0.2000    0.0192
##      3        1.1475             nan     0.2000    0.0135
##      4        1.1157             nan     0.2000    0.0136
##      5        1.0921             nan     0.2000    0.0091
##      6        1.0711             nan     0.2000    0.0091
##      7        1.0519             nan     0.2000    0.0056
##      8        1.0320             nan     0.2000    0.0074
##      9        1.0249             nan     0.2000   -0.0004
##     10        1.0026             nan     0.2000    0.0058
##     20        0.9195             nan     0.2000    0.0031
##     40        0.8552             nan     0.2000   -0.0014
##     60        0.8257             nan     0.2000   -0.0011
##     80        0.7995             nan     0.2000   -0.0018
##    100        0.7804             nan     0.2000   -0.0022
##    120        0.7665             nan     0.2000   -0.0016
##    140        0.7546             nan     0.2000   -0.0011
##    160        0.7442             nan     0.2000   -0.0010
##    180        0.7292             nan     0.2000   -0.0013
##    200        0.7175             nan     0.2000   -0.0018
##    220        0.7094             nan     0.2000   -0.0016
##    240        0.7005             nan     0.2000   -0.0014
##    260        0.6891             nan     0.2000   -0.0020
##    280        0.6797             nan     0.2000   -0.0018
##    300        0.6691             nan     0.2000   -0.0002
##    320        0.6639             nan     0.2000   -0.0026
##    340        0.6586             nan     0.2000   -0.0014
##    360        0.6512             nan     0.2000   -0.0010
##    380        0.6454             nan     0.2000   -0.0027
##    400        0.6424             nan     0.2000   -0.0011
##    420        0.6348             nan     0.2000   -0.0008
##    440        0.6293             nan     0.2000   -0.0023
##    460        0.6211             nan     0.2000   -0.0037
##    480        0.6145             nan     0.2000   -0.0015
##    500        0.6102             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2117             nan     0.2000    0.0323
##      2        1.1524             nan     0.2000    0.0222
##      3        1.1053             nan     0.2000    0.0224
##      4        1.0589             nan     0.2000    0.0178
##      5        1.0290             nan     0.2000    0.0069
##      6        1.0058             nan     0.2000    0.0077
##      7        0.9780             nan     0.2000    0.0092
##      8        0.9642             nan     0.2000   -0.0000
##      9        0.9452             nan     0.2000    0.0041
##     10        0.9323             nan     0.2000    0.0005
##     20        0.8536             nan     0.2000    0.0019
##     40        0.7761             nan     0.2000   -0.0020
##     60        0.7201             nan     0.2000   -0.0040
##     80        0.6813             nan     0.2000   -0.0040
##    100        0.6434             nan     0.2000   -0.0022
##    120        0.6094             nan     0.2000   -0.0020
##    140        0.5819             nan     0.2000   -0.0039
##    160        0.5462             nan     0.2000   -0.0015
##    180        0.5171             nan     0.2000   -0.0037
##    200        0.4943             nan     0.2000   -0.0010
##    220        0.4715             nan     0.2000   -0.0040
##    240        0.4499             nan     0.2000   -0.0004
##    260        0.4292             nan     0.2000   -0.0018
##    280        0.4067             nan     0.2000   -0.0011
##    300        0.3914             nan     0.2000   -0.0011
##    320        0.3786             nan     0.2000   -0.0011
##    340        0.3637             nan     0.2000   -0.0020
##    360        0.3462             nan     0.2000   -0.0011
##    380        0.3310             nan     0.2000   -0.0020
##    400        0.3140             nan     0.2000   -0.0015
##    420        0.3005             nan     0.2000   -0.0015
##    440        0.2925             nan     0.2000   -0.0013
##    460        0.2798             nan     0.2000   -0.0004
##    480        0.2699             nan     0.2000   -0.0010
##    500        0.2596             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2036             nan     0.2000    0.0434
##      2        1.1468             nan     0.2000    0.0227
##      3        1.0990             nan     0.2000    0.0193
##      4        1.0647             nan     0.2000    0.0132
##      5        1.0295             nan     0.2000    0.0132
##      6        1.0025             nan     0.2000    0.0082
##      7        0.9797             nan     0.2000    0.0094
##      8        0.9635             nan     0.2000    0.0081
##      9        0.9482             nan     0.2000    0.0053
##     10        0.9403             nan     0.2000   -0.0013
##     20        0.8458             nan     0.2000   -0.0005
##     40        0.7759             nan     0.2000   -0.0028
##     60        0.7235             nan     0.2000   -0.0083
##     80        0.6753             nan     0.2000   -0.0029
##    100        0.6356             nan     0.2000   -0.0023
##    120        0.6034             nan     0.2000   -0.0009
##    140        0.5726             nan     0.2000   -0.0035
##    160        0.5430             nan     0.2000   -0.0015
##    180        0.5178             nan     0.2000   -0.0015
##    200        0.4943             nan     0.2000   -0.0027
##    220        0.4647             nan     0.2000   -0.0017
##    240        0.4467             nan     0.2000   -0.0015
##    260        0.4300             nan     0.2000   -0.0009
##    280        0.4146             nan     0.2000   -0.0044
##    300        0.3933             nan     0.2000   -0.0008
##    320        0.3751             nan     0.2000   -0.0018
##    340        0.3557             nan     0.2000   -0.0005
##    360        0.3413             nan     0.2000   -0.0008
##    380        0.3268             nan     0.2000   -0.0017
##    400        0.3171             nan     0.2000   -0.0017
##    420        0.3061             nan     0.2000   -0.0004
##    440        0.2932             nan     0.2000   -0.0014
##    460        0.2837             nan     0.2000   -0.0012
##    480        0.2729             nan     0.2000   -0.0015
##    500        0.2618             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2009             nan     0.2000    0.0409
##      2        1.1381             nan     0.2000    0.0247
##      3        1.0947             nan     0.2000    0.0170
##      4        1.0574             nan     0.2000    0.0181
##      5        1.0292             nan     0.2000    0.0130
##      6        1.0021             nan     0.2000    0.0047
##      7        0.9824             nan     0.2000    0.0057
##      8        0.9623             nan     0.2000    0.0060
##      9        0.9432             nan     0.2000    0.0037
##     10        0.9285             nan     0.2000    0.0031
##     20        0.8455             nan     0.2000   -0.0023
##     40        0.7656             nan     0.2000   -0.0028
##     60        0.7189             nan     0.2000   -0.0033
##     80        0.6771             nan     0.2000   -0.0009
##    100        0.6379             nan     0.2000   -0.0012
##    120        0.6110             nan     0.2000   -0.0013
##    140        0.5860             nan     0.2000   -0.0021
##    160        0.5582             nan     0.2000   -0.0055
##    180        0.5335             nan     0.2000   -0.0046
##    200        0.5092             nan     0.2000   -0.0018
##    220        0.4915             nan     0.2000   -0.0017
##    240        0.4682             nan     0.2000   -0.0013
##    260        0.4429             nan     0.2000   -0.0015
##    280        0.4214             nan     0.2000   -0.0017
##    300        0.4024             nan     0.2000   -0.0016
##    320        0.3842             nan     0.2000   -0.0011
##    340        0.3720             nan     0.2000   -0.0005
##    360        0.3600             nan     0.2000   -0.0021
##    380        0.3458             nan     0.2000   -0.0024
##    400        0.3336             nan     0.2000   -0.0012
##    420        0.3206             nan     0.2000   -0.0016
##    440        0.3097             nan     0.2000   -0.0020
##    460        0.3006             nan     0.2000   -0.0009
##    480        0.2890             nan     0.2000   -0.0010
##    500        0.2763             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2033             nan     0.2000    0.0379
##      2        1.1364             nan     0.2000    0.0286
##      3        1.0921             nan     0.2000    0.0157
##      4        1.0468             nan     0.2000    0.0141
##      5        1.0227             nan     0.2000    0.0026
##      6        0.9955             nan     0.2000    0.0047
##      7        0.9726             nan     0.2000    0.0033
##      8        0.9523             nan     0.2000    0.0055
##      9        0.9362             nan     0.2000    0.0022
##     10        0.9163             nan     0.2000    0.0053
##     20        0.8069             nan     0.2000   -0.0018
##     40        0.7127             nan     0.2000   -0.0024
##     60        0.6299             nan     0.2000   -0.0000
##     80        0.5783             nan     0.2000   -0.0016
##    100        0.5229             nan     0.2000   -0.0019
##    120        0.4786             nan     0.2000   -0.0011
##    140        0.4350             nan     0.2000   -0.0044
##    160        0.3948             nan     0.2000   -0.0019
##    180        0.3620             nan     0.2000   -0.0032
##    200        0.3344             nan     0.2000   -0.0011
##    220        0.3112             nan     0.2000   -0.0016
##    240        0.2930             nan     0.2000   -0.0010
##    260        0.2709             nan     0.2000   -0.0009
##    280        0.2543             nan     0.2000   -0.0020
##    300        0.2376             nan     0.2000   -0.0011
##    320        0.2220             nan     0.2000   -0.0016
##    340        0.2073             nan     0.2000   -0.0005
##    360        0.1908             nan     0.2000   -0.0010
##    380        0.1814             nan     0.2000   -0.0004
##    400        0.1701             nan     0.2000   -0.0002
##    420        0.1589             nan     0.2000   -0.0002
##    440        0.1484             nan     0.2000   -0.0006
##    460        0.1399             nan     0.2000   -0.0002
##    480        0.1338             nan     0.2000   -0.0009
##    500        0.1264             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2041             nan     0.2000    0.0287
##      2        1.1281             nan     0.2000    0.0316
##      3        1.0802             nan     0.2000    0.0116
##      4        1.0378             nan     0.2000    0.0153
##      5        1.0011             nan     0.2000    0.0079
##      6        0.9719             nan     0.2000    0.0090
##      7        0.9601             nan     0.2000   -0.0063
##      8        0.9380             nan     0.2000    0.0040
##      9        0.9252             nan     0.2000   -0.0010
##     10        0.9122             nan     0.2000   -0.0000
##     20        0.8162             nan     0.2000   -0.0009
##     40        0.7026             nan     0.2000   -0.0016
##     60        0.6411             nan     0.2000   -0.0027
##     80        0.5877             nan     0.2000   -0.0020
##    100        0.5356             nan     0.2000   -0.0007
##    120        0.4883             nan     0.2000   -0.0025
##    140        0.4536             nan     0.2000   -0.0023
##    160        0.4267             nan     0.2000   -0.0008
##    180        0.3880             nan     0.2000   -0.0008
##    200        0.3556             nan     0.2000   -0.0009
##    220        0.3263             nan     0.2000   -0.0019
##    240        0.3012             nan     0.2000   -0.0019
##    260        0.2810             nan     0.2000   -0.0009
##    280        0.2617             nan     0.2000   -0.0006
##    300        0.2443             nan     0.2000   -0.0023
##    320        0.2258             nan     0.2000   -0.0004
##    340        0.2142             nan     0.2000   -0.0013
##    360        0.1997             nan     0.2000   -0.0015
##    380        0.1851             nan     0.2000   -0.0005
##    400        0.1740             nan     0.2000   -0.0005
##    420        0.1642             nan     0.2000   -0.0009
##    440        0.1532             nan     0.2000   -0.0007
##    460        0.1439             nan     0.2000   -0.0004
##    480        0.1355             nan     0.2000   -0.0008
##    500        0.1295             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2034             nan     0.2000    0.0395
##      2        1.1215             nan     0.2000    0.0379
##      3        1.0763             nan     0.2000    0.0164
##      4        1.0385             nan     0.2000    0.0134
##      5        1.0096             nan     0.2000    0.0125
##      6        0.9886             nan     0.2000    0.0031
##      7        0.9668             nan     0.2000    0.0069
##      8        0.9472             nan     0.2000    0.0066
##      9        0.9262             nan     0.2000    0.0063
##     10        0.9148             nan     0.2000   -0.0010
##     20        0.8111             nan     0.2000   -0.0016
##     40        0.7064             nan     0.2000   -0.0016
##     60        0.6415             nan     0.2000   -0.0078
##     80        0.5790             nan     0.2000   -0.0042
##    100        0.5307             nan     0.2000   -0.0001
##    120        0.4912             nan     0.2000   -0.0026
##    140        0.4541             nan     0.2000   -0.0003
##    160        0.4210             nan     0.2000   -0.0038
##    180        0.3898             nan     0.2000   -0.0021
##    200        0.3573             nan     0.2000   -0.0020
##    220        0.3268             nan     0.2000   -0.0023
##    240        0.3052             nan     0.2000   -0.0028
##    260        0.2830             nan     0.2000   -0.0014
##    280        0.2591             nan     0.2000   -0.0008
##    300        0.2414             nan     0.2000   -0.0005
##    320        0.2216             nan     0.2000   -0.0007
##    340        0.2037             nan     0.2000   -0.0012
##    360        0.1882             nan     0.2000   -0.0022
##    380        0.1774             nan     0.2000   -0.0009
##    400        0.1680             nan     0.2000   -0.0013
##    420        0.1622             nan     0.2000   -0.0024
##    440        0.1513             nan     0.2000   -0.0011
##    460        0.1416             nan     0.2000   -0.0007
##    480        0.1334             nan     0.2000   -0.0001
##    500        0.1262             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2063             nan     0.3000    0.0481
##      2        1.1493             nan     0.3000    0.0244
##      3        1.1092             nan     0.3000    0.0151
##      4        1.0795             nan     0.3000    0.0122
##      5        1.0415             nan     0.3000    0.0146
##      6        1.0224             nan     0.3000    0.0082
##      7        0.9993             nan     0.3000    0.0091
##      8        0.9781             nan     0.3000    0.0047
##      9        0.9674             nan     0.3000    0.0052
##     10        0.9534             nan     0.3000   -0.0001
##     20        0.9021             nan     0.3000   -0.0056
##     40        0.8341             nan     0.3000   -0.0004
##     60        0.8015             nan     0.3000   -0.0032
##     80        0.7663             nan     0.3000   -0.0047
##    100        0.7444             nan     0.3000   -0.0019
##    120        0.7305             nan     0.3000    0.0000
##    140        0.7157             nan     0.3000   -0.0033
##    160        0.7050             nan     0.3000   -0.0036
##    180        0.6953             nan     0.3000   -0.0029
##    200        0.6842             nan     0.3000   -0.0035
##    220        0.6723             nan     0.3000   -0.0055
##    240        0.6573             nan     0.3000   -0.0002
##    260        0.6482             nan     0.3000   -0.0046
##    280        0.6368             nan     0.3000   -0.0039
##    300        0.6287             nan     0.3000   -0.0030
##    320        0.6221             nan     0.3000   -0.0024
##    340        0.6104             nan     0.3000   -0.0017
##    360        0.6039             nan     0.3000   -0.0016
##    380        0.6036             nan     0.3000   -0.0034
##    400        0.5912             nan     0.3000   -0.0035
##    420        0.5845             nan     0.3000   -0.0027
##    440        0.5759             nan     0.3000   -0.0015
##    460        0.5716             nan     0.3000   -0.0011
##    480        0.5637             nan     0.3000   -0.0011
##    500        0.5561             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2008             nan     0.3000    0.0443
##      2        1.1491             nan     0.3000    0.0218
##      3        1.1078             nan     0.3000    0.0145
##      4        1.0715             nan     0.3000    0.0187
##      5        1.0362             nan     0.3000    0.0151
##      6        1.0146             nan     0.3000    0.0036
##      7        0.9946             nan     0.3000    0.0068
##      8        0.9833             nan     0.3000    0.0014
##      9        0.9648             nan     0.3000    0.0034
##     10        0.9547             nan     0.3000    0.0007
##     20        0.8822             nan     0.3000   -0.0011
##     40        0.8323             nan     0.3000   -0.0041
##     60        0.7947             nan     0.3000   -0.0066
##     80        0.7781             nan     0.3000   -0.0022
##    100        0.7588             nan     0.3000   -0.0021
##    120        0.7398             nan     0.3000   -0.0013
##    140        0.7262             nan     0.3000    0.0001
##    160        0.7183             nan     0.3000   -0.0028
##    180        0.7047             nan     0.3000   -0.0037
##    200        0.6948             nan     0.3000   -0.0018
##    220        0.6831             nan     0.3000   -0.0029
##    240        0.6743             nan     0.3000   -0.0049
##    260        0.6594             nan     0.3000   -0.0024
##    280        0.6529             nan     0.3000   -0.0032
##    300        0.6432             nan     0.3000   -0.0003
##    320        0.6381             nan     0.3000   -0.0016
##    340        0.6239             nan     0.3000   -0.0019
##    360        0.6138             nan     0.3000   -0.0011
##    380        0.6086             nan     0.3000   -0.0002
##    400        0.6005             nan     0.3000   -0.0039
##    420        0.5904             nan     0.3000   -0.0008
##    440        0.5833             nan     0.3000   -0.0015
##    460        0.5781             nan     0.3000   -0.0025
##    480        0.5731             nan     0.3000   -0.0033
##    500        0.5662             nan     0.3000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1953             nan     0.3000    0.0459
##      2        1.1417             nan     0.3000    0.0236
##      3        1.1007             nan     0.3000    0.0116
##      4        1.0628             nan     0.3000    0.0173
##      5        1.0406             nan     0.3000    0.0080
##      6        1.0056             nan     0.3000    0.0122
##      7        0.9879             nan     0.3000    0.0086
##      8        0.9753             nan     0.3000    0.0035
##      9        0.9602             nan     0.3000    0.0050
##     10        0.9492             nan     0.3000    0.0015
##     20        0.8863             nan     0.3000   -0.0027
##     40        0.8295             nan     0.3000   -0.0037
##     60        0.7898             nan     0.3000   -0.0005
##     80        0.7656             nan     0.3000    0.0011
##    100        0.7529             nan     0.3000   -0.0018
##    120        0.7347             nan     0.3000   -0.0064
##    140        0.7212             nan     0.3000   -0.0021
##    160        0.7079             nan     0.3000   -0.0041
##    180        0.6926             nan     0.3000   -0.0035
##    200        0.6864             nan     0.3000   -0.0024
##    220        0.6738             nan     0.3000   -0.0028
##    240        0.6597             nan     0.3000   -0.0035
##    260        0.6543             nan     0.3000   -0.0020
##    280        0.6435             nan     0.3000    0.0000
##    300        0.6374             nan     0.3000   -0.0023
##    320        0.6259             nan     0.3000   -0.0016
##    340        0.6209             nan     0.3000   -0.0019
##    360        0.6097             nan     0.3000   -0.0018
##    380        0.5973             nan     0.3000   -0.0034
##    400        0.5918             nan     0.3000   -0.0006
##    420        0.5862             nan     0.3000   -0.0018
##    440        0.5784             nan     0.3000   -0.0032
##    460        0.5707             nan     0.3000   -0.0039
##    480        0.5668             nan     0.3000   -0.0032
##    500        0.5599             nan     0.3000   -0.0034
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1702             nan     0.3000    0.0459
##      2        1.1001             nan     0.3000    0.0356
##      3        1.0564             nan     0.3000    0.0170
##      4        1.0093             nan     0.3000    0.0223
##      5        0.9822             nan     0.3000    0.0064
##      6        0.9582             nan     0.3000    0.0059
##      7        0.9360             nan     0.3000    0.0037
##      8        0.9168             nan     0.3000    0.0033
##      9        0.9076             nan     0.3000    0.0006
##     10        0.8928             nan     0.3000    0.0018
##     20        0.8197             nan     0.3000   -0.0059
##     40        0.7423             nan     0.3000   -0.0029
##     60        0.6721             nan     0.3000   -0.0066
##     80        0.6215             nan     0.3000   -0.0053
##    100        0.5580             nan     0.3000   -0.0053
##    120        0.5282             nan     0.3000   -0.0036
##    140        0.5004             nan     0.3000   -0.0020
##    160        0.4593             nan     0.3000   -0.0001
##    180        0.4325             nan     0.3000   -0.0087
##    200        0.6840             nan     0.3000   -0.0018
##    220        0.6643             nan     0.3000   -0.0024
##    240        0.6332             nan     0.3000   -0.0007
##    260        0.6228             nan     0.3000   -0.0039
##    280        0.6098             nan     0.3000   -0.0005
##    300        0.5870             nan     0.3000   -0.0013
##    320        0.5811             nan     0.3000   -0.0015
##    340        0.5692             nan     0.3000   -0.0055
##    360        0.5554             nan     0.3000   -0.0039
##    380        0.5480             nan     0.3000   -0.0020
##    400        0.5363             nan     0.3000   -0.0015
##    420        0.5192             nan     0.3000   -0.0028
##    440        0.5096             nan     0.3000   -0.0027
##    460        0.5013             nan     0.3000   -0.0008
##    480        0.4951             nan     0.3000   -0.0021
##    500        0.4870             nan     0.3000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1682             nan     0.3000    0.0562
##      2        1.1065             nan     0.3000    0.0233
##      3        1.0521             nan     0.3000    0.0212
##      4        1.0131             nan     0.3000    0.0125
##      5        0.9809             nan     0.3000    0.0101
##      6        0.9593             nan     0.3000    0.0058
##      7        0.9360             nan     0.3000    0.0033
##      8        0.9255             nan     0.3000   -0.0031
##      9        0.9056             nan     0.3000    0.0060
##     10        0.8957             nan     0.3000   -0.0058
##     20        0.7905             nan     0.3000    0.0011
##     40        0.7280             nan     0.3000   -0.0034
##     60        0.6800             nan     0.3000   -0.0051
##     80        0.6295             nan     0.3000   -0.0057
##    100        0.5786             nan     0.3000   -0.0006
##    120        0.5372             nan     0.3000   -0.0031
##    140        0.5094             nan     0.3000   -0.0079
##    160        0.4804             nan     0.3000   -0.0038
##    180        0.4438             nan     0.3000   -0.0022
##    200        0.4105             nan     0.3000   -0.0013
##    220        0.3847             nan     0.3000   -0.0032
##    240        0.3583             nan     0.3000   -0.0028
##    260        0.3344             nan     0.3000   -0.0015
##    280        0.3128             nan     0.3000   -0.0026
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000   -0.0018
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1695             nan     0.3000    0.0575
##      2        1.1086             nan     0.3000    0.0226
##      3        1.0545             nan     0.3000    0.0216
##      4        1.0098             nan     0.3000    0.0201
##      5        0.9813             nan     0.3000    0.0059
##      6        0.9569             nan     0.3000    0.0069
##      7        0.9322             nan     0.3000    0.0044
##      8        0.9160             nan     0.3000    0.0006
##      9        0.9049             nan     0.3000   -0.0017
##     10        0.8885             nan     0.3000   -0.0039
##     20        0.8181             nan     0.3000   -0.0012
##     40        0.7395             nan     0.3000   -0.0038
##     60        0.6851             nan     0.3000   -0.0016
##     80        0.6338             nan     0.3000   -0.0021
##    100        0.5891             nan     0.3000   -0.0043
##    120        0.5512             nan     0.3000   -0.0036
##    140        0.5185             nan     0.3000   -0.0033
##    160        0.4880             nan     0.3000   -0.0037
##    180        0.4612             nan     0.3000   -0.0055
##    200        0.4319             nan     0.3000   -0.0033
##    220        0.4022             nan     0.3000   -0.0049
##    240        0.3745             nan     0.3000   -0.0019
##    260        0.3529             nan     0.3000   -0.0018
##    280        0.3342             nan     0.3000   -0.0015
##    300        0.3195             nan     0.3000   -0.0035
##    320        0.2985             nan     0.3000   -0.0010
##    340        0.2827             nan     0.3000   -0.0021
##    360        0.2694             nan     0.3000   -0.0016
##    380        0.2569             nan     0.3000   -0.0009
##    400        0.2426             nan     0.3000   -0.0022
##    420        0.2284             nan     0.3000   -0.0022
##    440        0.2146             nan     0.3000   -0.0020
##    460        0.2024             nan     0.3000   -0.0003
##    480        0.1915             nan     0.3000   -0.0009
##    500        0.1805             nan     0.3000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1559             nan     0.3000    0.0665
##      2        1.0737             nan     0.3000    0.0385
##      3        1.0187             nan     0.3000    0.0236
##      4        0.9729             nan     0.3000    0.0122
##      5        0.9391             nan     0.3000    0.0088
##      6        0.9203             nan     0.3000   -0.0052
##      7        0.8928             nan     0.3000    0.0018
##      8        0.8735             nan     0.3000   -0.0009
##      9        0.8620             nan     0.3000   -0.0013
##     10        0.8426             nan     0.3000    0.0033
##     20        0.7722             nan     0.3000   -0.0031
##     40           inf             nan     0.3000       nan
##     60           inf             nan     0.3000       nan
##     80 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0030
##    100 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0024
##    120 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0011
##    140 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0023
##    160 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0035
##    180 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0024
##    200 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0037
##    220 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0002
##    240 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0008
##    260 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0018
##    280 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0014
##    300 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0008
##    320 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0024
##    340 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0015
##    360 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0007
##    380 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0007
##    400 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0002
##    420 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0007
##    440 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0013
##    460 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0004
##    480 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0011
##    500 4816822942941066000002440464682008040686240088440240862.0000             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1527             nan     0.3000    0.0669
##      2        1.0563             nan     0.3000    0.0371
##      3        0.9993             nan     0.3000    0.0221
##      4        0.9707             nan     0.3000   -0.0000
##      5        0.9407             nan     0.3000    0.0099
##      6        0.9152             nan     0.3000    0.0076
##      7        0.8895             nan     0.3000    0.0014
##      8        0.8713             nan     0.3000    0.0018
##      9        0.8612             nan     0.3000   -0.0025
##     10        0.8498             nan     0.3000   -0.0055
##     20        0.7553             nan     0.3000   -0.0040
##     40        0.6434             nan     0.3000   -0.0093
##     60        0.5650             nan     0.3000   -0.0100
##     80        0.5068             nan     0.3000   -0.0039
##    100        0.4433             nan     0.3000   -0.0036
##    120        0.3994             nan     0.3000   -0.0025
##    140        0.3446             nan     0.3000   -0.0012
##    160        0.3122             nan     0.3000   -0.0012
##    180        0.2752             nan     0.3000   -0.0029
##    200        0.2447             nan     0.3000   -0.0017
##    220        0.2216             nan     0.3000   -0.0017
##    240        0.1984             nan     0.3000   -0.0008
##    260        0.1772             nan     0.3000   -0.0003
##    280        0.1627             nan     0.3000   -0.0011
##    300        0.1471             nan     0.3000   -0.0009
##    320        0.1322             nan     0.3000   -0.0008
##    340        0.1219             nan     0.3000   -0.0002
##    360        0.1145             nan     0.3000   -0.0014
##    380        0.1054             nan     0.3000   -0.0004
##    400        0.0960             nan     0.3000   -0.0008
##    420        0.0897             nan     0.3000   -0.0009
##    440        0.0836             nan     0.3000   -0.0007
##    460        0.0767             nan     0.3000   -0.0003
##    480        0.0702             nan     0.3000   -0.0003
##    500        0.0670             nan     0.3000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1545             nan     0.3000    0.0628
##      2        1.0765             nan     0.3000    0.0345
##      3        1.0043             nan     0.3000    0.0278
##      4        0.9761             nan     0.3000    0.0041
##      5        0.9471             nan     0.3000    0.0062
##      6        0.9278             nan     0.3000    0.0004
##      7        0.9006             nan     0.3000    0.0091
##      8        0.8775             nan     0.3000    0.0006
##      9        0.8640             nan     0.3000   -0.0010
##     10        0.8591             nan     0.3000   -0.0074
##     20        0.7613             nan     0.3000   -0.0032
##     40        0.6386             nan     0.3000   -0.0027
##     60        0.5517             nan     0.3000   -0.0010
##     80        0.4966             nan     0.3000   -0.0052
##    100        0.4354             nan     0.3000   -0.0033
##    120        0.3775             nan     0.3000   -0.0049
##    140        0.3253             nan     0.3000   -0.0008
##    160        0.2912             nan     0.3000   -0.0025
##    180        0.2616             nan     0.3000   -0.0019
##    200        0.2351             nan     0.3000   -0.0034
##    220        0.2122             nan     0.3000   -0.0025
##    240        0.1922             nan     0.3000   -0.0006
##    260        0.1777             nan     0.3000   -0.0022
##    280        0.1601             nan     0.3000   -0.0025
##    300        0.1479             nan     0.3000   -0.0013
##    320        0.1335             nan     0.3000   -0.0006
##    340        0.1198             nan     0.3000   -0.0001
##    360        0.1100             nan     0.3000   -0.0004
##    380        0.1009             nan     0.3000   -0.0002
##    400        0.0931             nan     0.3000   -0.0003
##    420        0.0836             nan     0.3000   -0.0012
##    440        0.0790             nan     0.3000   -0.0010
##    460        0.0731             nan     0.3000   -0.0005
##    480        0.0673             nan     0.3000   -0.0007
##    500        0.0608             nan     0.3000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1685             nan     0.5000    0.0586
##      2        1.1013             nan     0.5000    0.0292
##      3        1.0514             nan     0.5000    0.0207
##      4        1.0219             nan     0.5000    0.0034
##      5        0.9802             nan     0.5000    0.0148
##      6        0.9691             nan     0.5000   -0.0051
##      7        0.9530             nan     0.5000    0.0039
##      8        0.9374             nan     0.5000    0.0033
##      9        0.9170             nan     0.5000    0.0056
##     10        0.9015             nan     0.5000    0.0065
##     20        0.8587             nan     0.5000   -0.0010
##     40        0.7924             nan     0.5000   -0.0015
##     60        0.7469             nan     0.5000   -0.0017
##     80        0.7202             nan     0.5000   -0.0050
##    100        0.6915             nan     0.5000   -0.0020
##    120        0.6673             nan     0.5000   -0.0034
##    140        0.6556             nan     0.5000   -0.0070
##    160        0.6415             nan     0.5000   -0.0040
##    180        0.6229             nan     0.5000   -0.0015
##    200        0.6142             nan     0.5000   -0.0097
##    220        0.5981             nan     0.5000   -0.0087
##    240        0.5995             nan     0.5000   -0.0105
##    260        0.5664             nan     0.5000   -0.0014
##    280        0.5663             nan     0.5000   -0.0062
##    300        0.5556             nan     0.5000   -0.0041
##    320        0.5468             nan     0.5000   -0.0002
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1535             nan     0.5000    0.0695
##      2        1.0903             nan     0.5000    0.0214
##      3        1.0447             nan     0.5000    0.0195
##      4        0.9972             nan     0.5000    0.0224
##      5        0.9672             nan     0.5000    0.0030
##      6        0.9504             nan     0.5000    0.0031
##      7        0.9350             nan     0.5000    0.0046
##      8        0.9221             nan     0.5000   -0.0082
##      9        0.9129             nan     0.5000   -0.0045
##     10        0.9092             nan     0.5000   -0.0053
##     20        0.8465             nan     0.5000   -0.0033
##     40        0.8011             nan     0.5000   -0.0049
##     60        0.7727             nan     0.5000   -0.0081
##     80        0.7406             nan     0.5000   -0.0136
##    100        0.7169             nan     0.5000   -0.0042
##    120        0.6977             nan     0.5000   -0.0059
##    140        0.6824             nan     0.5000   -0.0012
##    160        0.6705             nan     0.5000   -0.0096
##    180        0.6432             nan     0.5000   -0.0024
##    200        0.6287             nan     0.5000   -0.0039
##    220        0.6167             nan     0.5000   -0.0020
##    240        0.6053             nan     0.5000   -0.0038
##    260        0.5863             nan     0.5000   -0.0061
##    280        0.5792             nan     0.5000   -0.0045
##    300        0.5761             nan     0.5000   -0.0038
##    320        0.5657             nan     0.5000   -0.0042
##    340        0.5544             nan     0.5000   -0.0024
##    360        0.5433             nan     0.5000   -0.0036
##    380        0.5381             nan     0.5000   -0.0062
##    400        0.5247             nan     0.5000   -0.0012
##    420        0.5220             nan     0.5000   -0.0026
##    440        0.5232             nan     0.5000   -0.0071
##    460        0.5109             nan     0.5000   -0.0004
##    480        0.4983             nan     0.5000   -0.0006
##    500        0.4939             nan     0.5000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1453             nan     0.5000    0.0617
##      2        1.1116             nan     0.5000   -0.0024
##      3        1.0490             nan     0.5000    0.0182
##      4        1.0056             nan     0.5000    0.0118
##      5        0.9864             nan     0.5000    0.0029
##      6        0.9604             nan     0.5000    0.0108
##      7        0.9544             nan     0.5000   -0.0044
##      8        0.9487             nan     0.5000   -0.0077
##      9        0.9231             nan     0.5000    0.0122
##     10        0.9124             nan     0.5000   -0.0002
##     20        0.8637             nan     0.5000   -0.0013
##     40        0.8117             nan     0.5000   -0.0040
##     60        0.7697             nan     0.5000   -0.0055
##     80        0.7436             nan     0.5000   -0.0109
##    100        0.7406             nan     0.5000   -0.0049
##    120        0.7231             nan     0.5000   -0.0078
##    140        0.6965             nan     0.5000   -0.0039
##    160        0.6760             nan     0.5000   -0.0003
##    180        0.6604             nan     0.5000   -0.0012
##    200        0.6509             nan     0.5000   -0.0003
##    220        0.6342             nan     0.5000   -0.0061
##    240        0.6219             nan     0.5000   -0.0020
##    260        0.5997             nan     0.5000   -0.0004
##    280        0.5988             nan     0.5000   -0.0016
##    300        0.5833             nan     0.5000   -0.0025
##    320        0.5847             nan     0.5000   -0.0106
##    340        0.5699             nan     0.5000   -0.0033
##    360        0.5600             nan     0.5000   -0.0003
##    380        0.5489             nan     0.5000   -0.0035
##    400        0.5418             nan     0.5000   -0.0047
##    420        0.5359             nan     0.5000   -0.0024
##    440        0.5247             nan     0.5000   -0.0059
##    460        0.5136             nan     0.5000   -0.0025
##    480        0.5042             nan     0.5000   -0.0036
##    500        0.4951             nan     0.5000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1251             nan     0.5000    0.0783
##      2        1.0449             nan     0.5000    0.0302
##      3        0.9814             nan     0.5000    0.0244
##      4        0.9390             nan     0.5000    0.0193
##      5        0.9148             nan     0.5000    0.0018
##      6        0.9037             nan     0.5000   -0.0015
##      7        0.8959             nan     0.5000   -0.0095
##      8        0.8813             nan     0.5000   -0.0053
##      9        0.8630             nan     0.5000   -0.0041
##     10        0.8348             nan     0.5000    0.0096
##     20        0.7297             nan     0.5000   -0.0064
##     40        0.6648             nan     0.5000   -0.0113
##     60        0.6727             nan     0.5000   -0.0127
##     80        0.5453             nan     0.5000   -0.0024
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1198             nan     0.5000    0.0825
##      2        1.0378             nan     0.5000    0.0395
##      3        0.9808             nan     0.5000    0.0226
##      4        0.9438             nan     0.5000    0.0108
##      5        0.9164             nan     0.5000    0.0041
##      6        0.9028             nan     0.5000   -0.0057
##      7        0.8893             nan     0.5000   -0.0048
##      8        0.8834             nan     0.5000   -0.0227
##      9        0.8725             nan     0.5000   -0.0105
##     10        0.8619             nan     0.5000   -0.0090
##     20        0.7783             nan     0.5000   -0.0062
##     40        0.6738             nan     0.5000   -0.0053
##     60        0.5990             nan     0.5000   -0.0072
##     80        0.5377             nan     0.5000   -0.0038
##    100        0.4969             nan     0.5000   -0.0032
##    120        0.4541             nan     0.5000   -0.0119
##    140        0.3931             nan     0.5000   -0.0094
##    160        0.3573             nan     0.5000   -0.0019
##    180        0.3250             nan     0.5000   -0.0036
##    200        0.2877             nan     0.5000    0.0002
##    220        0.2640             nan     0.5000   -0.0022
##    240        0.2448             nan     0.5000   -0.0014
##    260        0.2218             nan     0.5000   -0.0041
##    280        0.2040             nan     0.5000   -0.0014
##    300        0.1861             nan     0.5000   -0.0008
##    320        0.1741             nan     0.5000   -0.0017
##    340        0.1582             nan     0.5000   -0.0014
##    360        0.1437             nan     0.5000   -0.0025
##    380        0.1329             nan     0.5000   -0.0031
##    400        0.1212             nan     0.5000   -0.0023
##    420        0.1103             nan     0.5000   -0.0008
##    440        0.1038             nan     0.5000   -0.0013
##    460        0.0955             nan     0.5000   -0.0022
##    480        0.0889             nan     0.5000   -0.0006
##    500        0.0837             nan     0.5000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1158             nan     0.5000    0.0931
##      2        1.0521             nan     0.5000    0.0265
##      3        0.9876             nan     0.5000    0.0150
##      4        0.9563             nan     0.5000    0.0003
##      5        0.9407             nan     0.5000   -0.0085
##      6        0.9100             nan     0.5000    0.0037
##      7        0.9003             nan     0.5000   -0.0041
##      8        0.8881             nan     0.5000   -0.0019
##      9        0.8840             nan     0.5000   -0.0120
##     10        0.8701             nan     0.5000   -0.0052
##     20        0.7958             nan     0.5000   -0.0167
##     40        0.6896             nan     0.5000   -0.0112
##     60        0.6251             nan     0.5000   -0.0108
##     80        0.5655             nan     0.5000   -0.0068
##    100        0.5229             nan     0.5000    0.0009
##    120        0.4739             nan     0.5000   -0.0047
##    140        0.4396             nan     0.5000   -0.0080
##    160        0.3999             nan     0.5000   -0.0004
##    180        0.3573             nan     0.5000   -0.0014
##    200        0.3242             nan     0.5000   -0.0074
##    220        0.2951             nan     0.5000   -0.0039
##    240        0.2779             nan     0.5000   -0.0001
##    260        0.2420             nan     0.5000   -0.0021
##    280        0.2145             nan     0.5000   -0.0025
##    300        0.1998             nan     0.5000   -0.0046
##    320        0.1767             nan     0.5000   -0.0009
##    340        0.1599             nan     0.5000   -0.0003
##    360        0.1532             nan     0.5000   -0.0033
##    380        0.1375             nan     0.5000   -0.0002
##    400        0.1255             nan     0.5000   -0.0012
##    420        0.1178             nan     0.5000   -0.0013
##    440        0.1079             nan     0.5000   -0.0019
##    460        0.0970             nan     0.5000   -0.0005
##    480        0.0887             nan     0.5000   -0.0013
##    500        0.0831             nan     0.5000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1063             nan     0.5000    0.0818
##      2        1.0261             nan     0.5000    0.0160
##      3        0.9550             nan     0.5000    0.0152
##      4        0.9226             nan     0.5000    0.0022
##      5        0.8906             nan     0.5000    0.0032
##      6        0.8669             nan     0.5000    0.0030
##      7        0.8668             nan     0.5000   -0.0278
##      8        0.8581             nan     0.5000   -0.0188
##      9        0.8440             nan     0.5000   -0.0116
##     10        0.8131             nan     0.5000    0.0016
##     20        0.7325             nan     0.5000   -0.0186
##     40        0.6022             nan     0.5000   -0.0044
##     60        0.5319             nan     0.5000   -0.0086
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0730             nan     0.5000    0.0819
##      2        1.0010             nan     0.5000    0.0111
##      3        0.9121             nan     0.5000    0.0181
##      4        0.9038             nan     0.5000   -0.0168
##      5        0.8745             nan     0.5000    0.0028
##      6        0.8527             nan     0.5000    0.0051
##      7        0.8357             nan     0.5000    0.0009
##      8        0.8261             nan     0.5000   -0.0123
##      9        0.8199             nan     0.5000   -0.0097
##     10        0.7976             nan     0.5000   -0.0044
##     20        0.7292             nan     0.5000   -0.0078
##     40        0.5870             nan     0.5000   -0.0134
##     60 407344394661.0875             nan     0.5000   -0.0019
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0957             nan     0.5000    0.0813
##      2        1.0052             nan     0.5000    0.0161
##      3        0.9406             nan     0.5000    0.0216
##      4        0.9176             nan     0.5000   -0.0001
##      5        0.9071             nan     0.5000   -0.0173
##      6        0.8870             nan     0.5000   -0.0131
##      7        0.8680             nan     0.5000   -0.0086
##      8        0.8591             nan     0.5000   -0.0112
##      9        0.8365             nan     0.5000   -0.0010
##     10        0.8185             nan     0.5000   -0.0058
##     20        0.7311             nan     0.5000   -0.0060
##     40        0.5910             nan     0.5000   -0.0026
##     60        0.4811             nan     0.5000   -0.0044
##     80        0.4069             nan     0.5000   -0.0090
##    100        0.3285             nan     0.5000   -0.0055
##    120        0.2737             nan     0.5000   -0.0065
##    140        0.2249             nan     0.5000   -0.0030
##    160        0.1841             nan     0.5000   -0.0039
##    180        0.1596             nan     0.5000   -0.0042
##    200        0.1354             nan     0.5000   -0.0028
##    220        0.1167             nan     0.5000   -0.0006
##    240        0.0997             nan     0.5000   -0.0009
##    260        0.0854             nan     0.5000   -0.0006
##    280        0.0755             nan     0.5000   -0.0021
##    300        0.0674             nan     0.5000   -0.0009
##    320        0.0573             nan     0.5000   -0.0001
##    340        0.0513             nan     0.5000   -0.0011
##    360        0.0450             nan     0.5000   -0.0006
##    380        0.0389             nan     0.5000   -0.0005
##    400        0.0341             nan     0.5000   -0.0003
##    420        0.0314             nan     0.5000   -0.0006
##    440        0.0278             nan     0.5000   -0.0009
##    460        0.0245             nan     0.5000   -0.0003
##    480        0.0222             nan     0.5000   -0.0005
##    500        0.0195             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1334             nan     1.0000    0.0694
##      2        1.0983             nan     1.0000   -0.0032
##      3        1.0335             nan     1.0000    0.0267
##      4        0.9905             nan     1.0000    0.0194
##      5        0.9739             nan     1.0000    0.0055
##      6        0.9778             nan     1.0000   -0.0223
##      7        0.9728             nan     1.0000   -0.0223
##      8        0.9668             nan     1.0000   -0.0236
##      9        0.9607             nan     1.0000   -0.0088
##     10        0.9697             nan     1.0000   -0.0207
##     20        1.1315             nan     1.0000   -0.0161
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000   -0.0078
##    100           inf             nan     1.0000   -0.0010
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000   -0.0099
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1450             nan     1.0000    0.0443
##      2        1.0776             nan     1.0000    0.0132
##      3        1.0358             nan     1.0000   -0.0026
##      4        1.0009             nan     1.0000   -0.0092
##      5        0.9812             nan     1.0000    0.0016
##      6        0.9841             nan     1.0000   -0.0162
##      7        1.0140             nan     1.0000   -0.0470
##      8        1.0198             nan     1.0000   -0.0162
##      9        1.0405             nan     1.0000   -0.0376
##     10        1.0207             nan     1.0000    0.0065
##     20        1.2843             nan     1.0000    0.0034
##     40        1.2386             nan     1.0000   -0.0366
##     60        1.2134             nan     1.0000    0.0157
##     80 1461871472425.3103             nan     1.0000    0.0006
##    100 1461871472425.2849             nan     1.0000    0.0009
##    120 1461871857369.6980             nan     1.0000   -0.0008
##    140 1461871857369.6956             nan     1.0000   -0.0001
##    160 1461871857369.6995             nan     1.0000    0.0002
##    180 1461871857369.7068             nan     1.0000   -0.0087
##    200 1461871857369.6909             nan     1.0000   -0.0042
##    220 1461871857369.6875             nan     1.0000   -0.0007
##    240 1461871857369.6899             nan     1.0000    0.0001
##    260 1461871857369.6860             nan     1.0000   -0.0000
##    280 1461871857369.6870             nan     1.0000    0.0023
##    300 1461871857369.6865             nan     1.0000   -0.0001
##    320 1461871857369.6882             nan     1.0000   -0.0008
##    340 1461871857369.6843             nan     1.0000    0.0006
##    360 1461871857369.6821             nan     1.0000   -0.0027
##    380 1461871857369.6833             nan     1.0000   -0.0024
##    400 1461871857369.6848             nan     1.0000    0.0013
##    420 1461871857369.6821             nan     1.0000   -0.0003
##    440 1461871857369.6785             nan     1.0000   -0.0029
##    460 1461871857369.6755             nan     1.0000   -0.0003
##    480 1461871857369.6714             nan     1.0000    0.0003
##    500 1461871857369.6658             nan     1.0000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1122             nan     1.0000    0.0965
##      2        1.0409             nan     1.0000    0.0181
##      3        0.9839             nan     1.0000    0.0204
##      4        0.9768             nan     1.0000   -0.0202
##      5        0.9702             nan     1.0000   -0.0192
##      6        0.9793             nan     1.0000   -0.0311
##      7        0.9585             nan     1.0000    0.0041
##      8        0.9474             nan     1.0000   -0.0115
##      9        0.9553             nan     1.0000   -0.0287
##     10        0.9529             nan     1.0000   -0.0205
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0197
##    100 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0179
##    120 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0225
##    140 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0048
##    160 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0159
##    180 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0089
##    200 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0064
##    220 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0030
##    240 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0058
##    260 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0001
##    280 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0129
##    300 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0000
##    320 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0004
##    340 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0090
##    360 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0001
##    380 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0004
##    400 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0019
##    420 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0003
##    440 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0000
##    460 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0002
##    480 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000   -0.0027
##    500 39232177900660099028224660086400662640224482660824886024048442888.0000             nan     1.0000    0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0640             nan     1.0000    0.0995
##      2        0.9910             nan     1.0000    0.0168
##      3        0.9302             nan     1.0000   -0.0018
##      4        0.9233             nan     1.0000   -0.0115
##      5        0.9080             nan     1.0000   -0.0105
##      6        0.8849             nan     1.0000    0.0011
##      7        0.8734             nan     1.0000   -0.0147
##      8        0.8647             nan     1.0000   -0.0135
##      9        0.8968             nan     1.0000   -0.0521
##     10        0.9287             nan     1.0000   -0.0615
##     20        0.8730             nan     1.0000   -0.0140
##     40           inf             nan     1.0000   -0.0308
##     60           inf             nan     1.0000   -0.0082
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0792             nan     1.0000    0.0563
##      2        1.0098             nan     1.0000    0.0120
##      3        0.9799             nan     1.0000   -0.0146
##      4        0.9649             nan     1.0000   -0.0053
##      5        0.9487             nan     1.0000   -0.0135
##      6        0.9541             nan     1.0000   -0.0283
##      7        0.9684             nan     1.0000   -0.0386
##      8        0.9858             nan     1.0000   -0.0628
##      9        0.9399             nan     1.0000   -0.0016
##     10        0.9121             nan     1.0000    0.0019
##     20        0.8301             nan     1.0000   -0.0104
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0742             nan     1.0000    0.1102
##      2        1.0083             nan     1.0000    0.0042
##      3        0.9814             nan     1.0000   -0.0137
##      4        0.9587             nan     1.0000   -0.0263
##      5        0.9616             nan     1.0000   -0.0317
##      6        0.9704             nan     1.0000   -0.0461
##      7        1.1103             nan     1.0000   -0.0187
##      8        1.1248             nan     1.0000   -0.0453
##      9        1.1136             nan     1.0000   -0.0254
##     10        1.1055             nan     1.0000   -0.0370
##     20        1.1120             nan     1.0000   -0.0423
##     40        0.9533             nan     1.0000   -0.0261
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0254             nan     1.0000    0.1011
##      2        0.9255             nan     1.0000    0.0283
##      3        0.9190             nan     1.0000   -0.0486
##      4        0.9279             nan     1.0000   -0.0443
##      5        0.9323             nan     1.0000   -0.0600
##      6        0.9159             nan     1.0000   -0.0221
##      7        0.9258             nan     1.0000   -0.0479
##      8        0.9476             nan     1.0000   -0.0780
##      9        0.9338             nan     1.0000   -0.0446
##     10        0.8995             nan     1.0000    0.0067
##     20        1.9946             nan     1.0000   -0.0029
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0721             nan     1.0000    0.0396
##      2        0.9659             nan     1.0000    0.0381
##      3        0.9408             nan     1.0000   -0.0235
##      4        0.9142             nan     1.0000   -0.0159
##      5        0.9361             nan     1.0000   -0.0532
##      6        0.9285             nan     1.0000   -0.0312
##      7        0.9241             nan     1.0000   -0.0287
##      8        0.9093             nan     1.0000   -0.0339
##      9        0.9195             nan     1.0000   -0.0623
##     10        0.8832             nan     1.0000   -0.0292
##     20        4.0569             nan     1.0000   -0.0439
##     40           inf             nan     1.0000    5.9278
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0244             nan     1.0000    0.1165
##      2        0.9273             nan     1.0000    0.0384
##      3        0.9195             nan     1.0000   -0.0247
##      4        0.9001             nan     1.0000   -0.0195
##      5        0.8857             nan     1.0000   -0.0086
##      6        0.8847             nan     1.0000   -0.0405
##      7        0.8517             nan     1.0000    0.0059
##      8        0.8445             nan     1.0000   -0.0235
##      9        0.8167             nan     1.0000    0.0022
##     10        0.8069             nan     1.0000   -0.0286
##     20        0.8208             nan     1.0000   -0.0995
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0001
##      6        1.2912             nan     0.0010    0.0001
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2899             nan     0.0010    0.0002
##     20        1.2864             nan     0.0010    0.0002
##     40        1.2798             nan     0.0010    0.0001
##     60        1.2734             nan     0.0010    0.0001
##     80        1.2672             nan     0.0010    0.0001
##    100        1.2614             nan     0.0010    0.0001
##    120        1.2556             nan     0.0010    0.0001
##    140        1.2499             nan     0.0010    0.0001
##    160        1.2445             nan     0.0010    0.0001
##    180        1.2391             nan     0.0010    0.0001
##    200        1.2339             nan     0.0010    0.0001
##    220        1.2288             nan     0.0010    0.0001
##    240        1.2238             nan     0.0010    0.0001
##    260        1.2191             nan     0.0010    0.0001
##    280        1.2145             nan     0.0010    0.0001
##    300        1.2101             nan     0.0010    0.0001
##    320        1.2056             nan     0.0010    0.0001
##    340        1.2015             nan     0.0010    0.0001
##    360        1.1975             nan     0.0010    0.0001
##    380        1.1936             nan     0.0010    0.0001
##    400        1.1898             nan     0.0010    0.0001
##    420        1.1860             nan     0.0010    0.0001
##    440        1.1822             nan     0.0010    0.0001
##    460        1.1787             nan     0.0010    0.0000
##    480        1.1752             nan     0.0010    0.0001
##    500        1.1717             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2924             nan     0.0010    0.0001
##      4        1.2920             nan     0.0010    0.0001
##      5        1.2917             nan     0.0010    0.0002
##      6        1.2914             nan     0.0010    0.0002
##      7        1.2910             nan     0.0010    0.0001
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2903             nan     0.0010    0.0002
##     10        1.2899             nan     0.0010    0.0001
##     20        1.2866             nan     0.0010    0.0001
##     40        1.2801             nan     0.0010    0.0001
##     60        1.2738             nan     0.0010    0.0001
##     80        1.2675             nan     0.0010    0.0001
##    100        1.2616             nan     0.0010    0.0001
##    120        1.2557             nan     0.0010    0.0001
##    140        1.2501             nan     0.0010    0.0001
##    160        1.2447             nan     0.0010    0.0001
##    180        1.2395             nan     0.0010    0.0001
##    200        1.2344             nan     0.0010    0.0001
##    220        1.2293             nan     0.0010    0.0001
##    240        1.2245             nan     0.0010    0.0001
##    260        1.2197             nan     0.0010    0.0001
##    280        1.2151             nan     0.0010    0.0001
##    300        1.2105             nan     0.0010    0.0001
##    320        1.2062             nan     0.0010    0.0001
##    340        1.2019             nan     0.0010    0.0001
##    360        1.1979             nan     0.0010    0.0001
##    380        1.1939             nan     0.0010    0.0001
##    400        1.1900             nan     0.0010    0.0001
##    420        1.1862             nan     0.0010    0.0001
##    440        1.1824             nan     0.0010    0.0001
##    460        1.1788             nan     0.0010    0.0001
##    480        1.1753             nan     0.0010    0.0001
##    500        1.1718             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2913             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2863             nan     0.0010    0.0001
##     40        1.2795             nan     0.0010    0.0001
##     60        1.2731             nan     0.0010    0.0001
##     80        1.2670             nan     0.0010    0.0001
##    100        1.2611             nan     0.0010    0.0001
##    120        1.2551             nan     0.0010    0.0001
##    140        1.2494             nan     0.0010    0.0001
##    160        1.2438             nan     0.0010    0.0001
##    180        1.2387             nan     0.0010    0.0001
##    200        1.2335             nan     0.0010    0.0001
##    220        1.2287             nan     0.0010    0.0001
##    240        1.2237             nan     0.0010    0.0001
##    260        1.2190             nan     0.0010    0.0001
##    280        1.2146             nan     0.0010    0.0001
##    300        1.2101             nan     0.0010    0.0001
##    320        1.2058             nan     0.0010    0.0001
##    340        1.2015             nan     0.0010    0.0001
##    360        1.1976             nan     0.0010    0.0001
##    380        1.1936             nan     0.0010    0.0001
##    400        1.1896             nan     0.0010    0.0001
##    420        1.1859             nan     0.0010    0.0001
##    440        1.1821             nan     0.0010    0.0001
##    460        1.1785             nan     0.0010    0.0001
##    480        1.1750             nan     0.0010    0.0001
##    500        1.1714             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2842             nan     0.0010    0.0002
##     40        1.2754             nan     0.0010    0.0002
##     60        1.2668             nan     0.0010    0.0002
##     80        1.2586             nan     0.0010    0.0002
##    100        1.2507             nan     0.0010    0.0002
##    120        1.2429             nan     0.0010    0.0002
##    140        1.2353             nan     0.0010    0.0002
##    160        1.2281             nan     0.0010    0.0002
##    180        1.2211             nan     0.0010    0.0002
##    200        1.2143             nan     0.0010    0.0002
##    220        1.2076             nan     0.0010    0.0001
##    240        1.2010             nan     0.0010    0.0002
##    260        1.1948             nan     0.0010    0.0001
##    280        1.1888             nan     0.0010    0.0001
##    300        1.1827             nan     0.0010    0.0001
##    320        1.1768             nan     0.0010    0.0001
##    340        1.1712             nan     0.0010    0.0001
##    360        1.1659             nan     0.0010    0.0001
##    380        1.1604             nan     0.0010    0.0001
##    400        1.1554             nan     0.0010    0.0001
##    420        1.1502             nan     0.0010    0.0001
##    440        1.1453             nan     0.0010    0.0001
##    460        1.1405             nan     0.0010    0.0001
##    480        1.1358             nan     0.0010    0.0001
##    500        1.1311             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2843             nan     0.0010    0.0002
##     40        1.2755             nan     0.0010    0.0001
##     60        1.2671             nan     0.0010    0.0002
##     80        1.2589             nan     0.0010    0.0002
##    100        1.2510             nan     0.0010    0.0002
##    120        1.2430             nan     0.0010    0.0002
##    140        1.2355             nan     0.0010    0.0002
##    160        1.2284             nan     0.0010    0.0002
##    180        1.2215             nan     0.0010    0.0002
##    200        1.2145             nan     0.0010    0.0002
##    220        1.2078             nan     0.0010    0.0001
##    240        1.2013             nan     0.0010    0.0001
##    260        1.1951             nan     0.0010    0.0001
##    280        1.1890             nan     0.0010    0.0001
##    300        1.1833             nan     0.0010    0.0001
##    320        1.1773             nan     0.0010    0.0001
##    340        1.1717             nan     0.0010    0.0001
##    360        1.1663             nan     0.0010    0.0001
##    380        1.1610             nan     0.0010    0.0001
##    400        1.1557             nan     0.0010    0.0001
##    420        1.1505             nan     0.0010    0.0001
##    440        1.1455             nan     0.0010    0.0001
##    460        1.1407             nan     0.0010    0.0001
##    480        1.1360             nan     0.0010    0.0001
##    500        1.1314             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2921             nan     0.0010    0.0002
##      4        1.2916             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2907             nan     0.0010    0.0002
##      7        1.2902             nan     0.0010    0.0002
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2889             nan     0.0010    0.0002
##     20        1.2844             nan     0.0010    0.0002
##     40        1.2757             nan     0.0010    0.0002
##     60        1.2675             nan     0.0010    0.0002
##     80        1.2591             nan     0.0010    0.0002
##    100        1.2511             nan     0.0010    0.0002
##    120        1.2431             nan     0.0010    0.0002
##    140        1.2356             nan     0.0010    0.0002
##    160        1.2284             nan     0.0010    0.0001
##    180        1.2214             nan     0.0010    0.0002
##    200        1.2148             nan     0.0010    0.0002
##    220        1.2080             nan     0.0010    0.0002
##    240        1.2015             nan     0.0010    0.0001
##    260        1.1951             nan     0.0010    0.0001
##    280        1.1889             nan     0.0010    0.0001
##    300        1.1828             nan     0.0010    0.0001
##    320        1.1770             nan     0.0010    0.0001
##    340        1.1714             nan     0.0010    0.0001
##    360        1.1662             nan     0.0010    0.0001
##    380        1.1608             nan     0.0010    0.0001
##    400        1.1555             nan     0.0010    0.0001
##    420        1.1501             nan     0.0010    0.0001
##    440        1.1452             nan     0.0010    0.0001
##    460        1.1403             nan     0.0010    0.0001
##    480        1.1354             nan     0.0010    0.0001
##    500        1.1306             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0003
##      8        1.2892             nan     0.0010    0.0002
##      9        1.2886             nan     0.0010    0.0003
##     10        1.2880             nan     0.0010    0.0002
##     20        1.2829             nan     0.0010    0.0001
##     40        1.2729             nan     0.0010    0.0002
##     60        1.2629             nan     0.0010    0.0002
##     80        1.2532             nan     0.0010    0.0002
##    100        1.2441             nan     0.0010    0.0002
##    120        1.2353             nan     0.0010    0.0002
##    140        1.2266             nan     0.0010    0.0002
##    160        1.2181             nan     0.0010    0.0002
##    180        1.2099             nan     0.0010    0.0002
##    200        1.2019             nan     0.0010    0.0002
##    220        1.1943             nan     0.0010    0.0001
##    240        1.1869             nan     0.0010    0.0002
##    260        1.1797             nan     0.0010    0.0002
##    280        1.1725             nan     0.0010    0.0001
##    300        1.1658             nan     0.0010    0.0002
##    320        1.1592             nan     0.0010    0.0001
##    340        1.1526             nan     0.0010    0.0001
##    360        1.1462             nan     0.0010    0.0001
##    380        1.1401             nan     0.0010    0.0001
##    400        1.1343             nan     0.0010    0.0001
##    420        1.1286             nan     0.0010    0.0001
##    440        1.1229             nan     0.0010    0.0001
##    460        1.1173             nan     0.0010    0.0001
##    480        1.1120             nan     0.0010    0.0001
##    500        1.1069             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2908             nan     0.0010    0.0002
##      6        1.2903             nan     0.0010    0.0002
##      7        1.2898             nan     0.0010    0.0003
##      8        1.2892             nan     0.0010    0.0003
##      9        1.2887             nan     0.0010    0.0002
##     10        1.2882             nan     0.0010    0.0002
##     20        1.2828             nan     0.0010    0.0002
##     40        1.2727             nan     0.0010    0.0002
##     60        1.2627             nan     0.0010    0.0002
##     80        1.2530             nan     0.0010    0.0002
##    100        1.2438             nan     0.0010    0.0002
##    120        1.2347             nan     0.0010    0.0002
##    140        1.2262             nan     0.0010    0.0002
##    160        1.2180             nan     0.0010    0.0001
##    180        1.2099             nan     0.0010    0.0002
##    200        1.2023             nan     0.0010    0.0001
##    220        1.1946             nan     0.0010    0.0002
##    240        1.1872             nan     0.0010    0.0002
##    260        1.1799             nan     0.0010    0.0001
##    280        1.1727             nan     0.0010    0.0002
##    300        1.1658             nan     0.0010    0.0001
##    320        1.1592             nan     0.0010    0.0001
##    340        1.1529             nan     0.0010    0.0001
##    360        1.1465             nan     0.0010    0.0001
##    380        1.1404             nan     0.0010    0.0001
##    400        1.1342             nan     0.0010    0.0001
##    420        1.1285             nan     0.0010    0.0001
##    440        1.1229             nan     0.0010    0.0001
##    460        1.1174             nan     0.0010    0.0001
##    480        1.1119             nan     0.0010    0.0001
##    500        1.1065             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2908             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0003
##     10        1.2880             nan     0.0010    0.0002
##     20        1.2830             nan     0.0010    0.0002
##     40        1.2728             nan     0.0010    0.0002
##     60        1.2630             nan     0.0010    0.0002
##     80        1.2534             nan     0.0010    0.0002
##    100        1.2440             nan     0.0010    0.0002
##    120        1.2353             nan     0.0010    0.0002
##    140        1.2265             nan     0.0010    0.0001
##    160        1.2183             nan     0.0010    0.0002
##    180        1.2100             nan     0.0010    0.0001
##    200        1.2020             nan     0.0010    0.0002
##    220        1.1942             nan     0.0010    0.0002
##    240        1.1867             nan     0.0010    0.0002
##    260        1.1791             nan     0.0010    0.0001
##    280        1.1720             nan     0.0010    0.0001
##    300        1.1652             nan     0.0010    0.0001
##    320        1.1587             nan     0.0010    0.0001
##    340        1.1521             nan     0.0010    0.0001
##    360        1.1459             nan     0.0010    0.0001
##    380        1.1399             nan     0.0010    0.0001
##    400        1.1338             nan     0.0010    0.0001
##    420        1.1278             nan     0.0010    0.0001
##    440        1.1221             nan     0.0010    0.0001
##    460        1.1167             nan     0.0010    0.0001
##    480        1.1113             nan     0.0010    0.0001
##    500        1.1059             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2587             nan     0.1000    0.0155
##      2        1.2313             nan     0.1000    0.0104
##      3        1.2089             nan     0.1000    0.0109
##      4        1.1884             nan     0.1000    0.0073
##      5        1.1695             nan     0.1000    0.0068
##      6        1.1532             nan     0.1000    0.0063
##      7        1.1373             nan     0.1000    0.0049
##      8        1.1221             nan     0.1000    0.0065
##      9        1.1088             nan     0.1000    0.0052
##     10        1.1023             nan     0.1000    0.0010
##     20        1.0138             nan     0.1000    0.0038
##     40        0.9352             nan     0.1000   -0.0011
##     60        0.8999             nan     0.1000    0.0001
##     80        0.8724             nan     0.1000   -0.0014
##    100        0.8548             nan     0.1000   -0.0000
##    120        0.8360             nan     0.1000   -0.0003
##    140        0.8219             nan     0.1000   -0.0009
##    160        0.8109             nan     0.1000   -0.0004
##    180        0.8019             nan     0.1000   -0.0004
##    200        0.7928             nan     0.1000   -0.0007
##    220        0.7863             nan     0.1000   -0.0007
##    240        0.7812             nan     0.1000   -0.0011
##    260        0.7727             nan     0.1000   -0.0015
##    280        0.7639             nan     0.1000   -0.0005
##    300        0.7582             nan     0.1000   -0.0015
##    320        0.7537             nan     0.1000   -0.0007
##    340        0.7474             nan     0.1000   -0.0008
##    360        0.7411             nan     0.1000   -0.0007
##    380        0.7341             nan     0.1000   -0.0013
##    400        0.7299             nan     0.1000   -0.0009
##    420        0.7242             nan     0.1000   -0.0007
##    440        0.7202             nan     0.1000   -0.0007
##    460        0.7169             nan     0.1000   -0.0009
##    480        0.7120             nan     0.1000   -0.0009
##    500        0.7069             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2641             nan     0.1000    0.0150
##      2        1.2386             nan     0.1000    0.0128
##      3        1.2159             nan     0.1000    0.0112
##      4        1.1930             nan     0.1000    0.0085
##      5        1.1748             nan     0.1000    0.0079
##      6        1.1571             nan     0.1000    0.0062
##      7        1.1421             nan     0.1000    0.0052
##      8        1.1267             nan     0.1000    0.0055
##      9        1.1135             nan     0.1000    0.0046
##     10        1.1006             nan     0.1000    0.0036
##     20        1.0145             nan     0.1000    0.0022
##     40        0.9355             nan     0.1000    0.0013
##     60        0.8985             nan     0.1000   -0.0011
##     80        0.8692             nan     0.1000   -0.0005
##    100        0.8478             nan     0.1000   -0.0015
##    120        0.8316             nan     0.1000   -0.0012
##    140        0.8210             nan     0.1000   -0.0015
##    160        0.8106             nan     0.1000   -0.0010
##    180        0.8014             nan     0.1000   -0.0015
##    200        0.7928             nan     0.1000   -0.0008
##    220        0.7852             nan     0.1000   -0.0009
##    240        0.7790             nan     0.1000   -0.0003
##    260        0.7710             nan     0.1000   -0.0009
##    280        0.7597             nan     0.1000   -0.0009
##    300        0.7531             nan     0.1000   -0.0007
##    320        0.7464             nan     0.1000   -0.0017
##    340        0.7409             nan     0.1000   -0.0007
##    360        0.7364             nan     0.1000   -0.0006
##    380        0.7330             nan     0.1000   -0.0006
##    400        0.7275             nan     0.1000   -0.0004
##    420        0.7225             nan     0.1000   -0.0013
##    440        0.7181             nan     0.1000   -0.0007
##    460        0.7152             nan     0.1000   -0.0011
##    480        0.7103             nan     0.1000   -0.0008
##    500        0.7042             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2599             nan     0.1000    0.0147
##      2        1.2345             nan     0.1000    0.0127
##      3        1.2113             nan     0.1000    0.0119
##      4        1.1895             nan     0.1000    0.0090
##      5        1.1699             nan     0.1000    0.0089
##      6        1.1546             nan     0.1000    0.0063
##      7        1.1385             nan     0.1000    0.0054
##      8        1.1241             nan     0.1000    0.0063
##      9        1.1127             nan     0.1000    0.0043
##     10        1.0980             nan     0.1000    0.0058
##     20        1.0086             nan     0.1000    0.0028
##     40        0.9299             nan     0.1000    0.0004
##     60        0.8936             nan     0.1000   -0.0017
##     80        0.8670             nan     0.1000   -0.0001
##    100        0.8499             nan     0.1000   -0.0016
##    120        0.8403             nan     0.1000   -0.0006
##    140        0.8282             nan     0.1000   -0.0011
##    160        0.8174             nan     0.1000   -0.0014
##    180        0.8065             nan     0.1000   -0.0007
##    200        0.7966             nan     0.1000   -0.0004
##    220        0.7891             nan     0.1000   -0.0000
##    240        0.7837             nan     0.1000   -0.0014
##    260        0.7776             nan     0.1000   -0.0005
##    280        0.7716             nan     0.1000   -0.0003
##    300        0.7645             nan     0.1000   -0.0010
##    320        0.7591             nan     0.1000   -0.0008
##    340        0.7517             nan     0.1000   -0.0007
##    360        0.7457             nan     0.1000   -0.0017
##    380        0.7419             nan     0.1000   -0.0012
##    400        0.7359             nan     0.1000   -0.0009
##    420        0.7297             nan     0.1000   -0.0008
##    440        0.7244             nan     0.1000   -0.0004
##    460        0.7191             nan     0.1000   -0.0010
##    480        0.7131             nan     0.1000   -0.0002
##    500        0.7088             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2483             nan     0.1000    0.0210
##      2        1.2079             nan     0.1000    0.0171
##      3        1.1735             nan     0.1000    0.0137
##      4        1.1435             nan     0.1000    0.0116
##      5        1.1245             nan     0.1000    0.0062
##      6        1.1040             nan     0.1000    0.0069
##      7        1.0859             nan     0.1000    0.0069
##      8        1.0657             nan     0.1000    0.0070
##      9        1.0498             nan     0.1000    0.0073
##     10        1.0344             nan     0.1000    0.0052
##     20        0.9466             nan     0.1000    0.0006
##     40        0.8623             nan     0.1000   -0.0008
##     60        0.8167             nan     0.1000    0.0004
##     80        0.7832             nan     0.1000    0.0002
##    100        0.7563             nan     0.1000   -0.0018
##    120        0.7318             nan     0.1000   -0.0019
##    140        0.7009             nan     0.1000   -0.0014
##    160        0.6808             nan     0.1000   -0.0019
##    180        0.6581             nan     0.1000   -0.0008
##    200        0.6405             nan     0.1000   -0.0006
##    220        0.6195             nan     0.1000   -0.0005
##    240        0.6024             nan     0.1000   -0.0008
##    260        0.5874             nan     0.1000   -0.0008
##    280        0.5745             nan     0.1000   -0.0015
##    300        0.5550             nan     0.1000   -0.0009
##    320        0.5395             nan     0.1000   -0.0007
##    340        0.5262             nan     0.1000   -0.0011
##    360        0.5141             nan     0.1000   -0.0011
##    380        0.5001             nan     0.1000   -0.0006
##    400        0.4873             nan     0.1000   -0.0009
##    420        0.4760             nan     0.1000   -0.0002
##    440        0.4636             nan     0.1000   -0.0011
##    460        0.4522             nan     0.1000   -0.0006
##    480        0.4422             nan     0.1000   -0.0016
##    500        0.4319             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2496             nan     0.1000    0.0221
##      2        1.2066             nan     0.1000    0.0167
##      3        1.1719             nan     0.1000    0.0142
##      4        1.1446             nan     0.1000    0.0117
##      5        1.1207             nan     0.1000    0.0107
##      6        1.0999             nan     0.1000    0.0073
##      7        1.0792             nan     0.1000    0.0078
##      8        1.0629             nan     0.1000    0.0034
##      9        1.0451             nan     0.1000    0.0054
##     10        1.0303             nan     0.1000    0.0037
##     20        0.9415             nan     0.1000    0.0017
##     40        0.8555             nan     0.1000   -0.0011
##     60        0.8011             nan     0.1000   -0.0004
##     80        0.7689             nan     0.1000   -0.0009
##    100        0.7428             nan     0.1000   -0.0003
##    120        0.7191             nan     0.1000   -0.0000
##    140        0.6961             nan     0.1000   -0.0008
##    160        0.6728             nan     0.1000   -0.0011
##    180        0.6536             nan     0.1000   -0.0006
##    200        0.6345             nan     0.1000   -0.0010
##    220        0.6164             nan     0.1000   -0.0004
##    240        0.5978             nan     0.1000   -0.0011
##    260        0.5829             nan     0.1000   -0.0013
##    280        0.5690             nan     0.1000   -0.0009
##    300        0.5557             nan     0.1000   -0.0011
##    320        0.5464             nan     0.1000   -0.0010
##    340        0.5327             nan     0.1000   -0.0006
##    360        0.5213             nan     0.1000   -0.0009
##    380        0.5069             nan     0.1000   -0.0008
##    400        0.4933             nan     0.1000   -0.0011
##    420        0.4839             nan     0.1000   -0.0011
##    440        0.4731             nan     0.1000   -0.0008
##    460        0.4629             nan     0.1000   -0.0009
##    480        0.4526             nan     0.1000   -0.0014
##    500        0.4447             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2530             nan     0.1000    0.0176
##      2        1.2134             nan     0.1000    0.0157
##      3        1.1724             nan     0.1000    0.0136
##      4        1.1441             nan     0.1000    0.0105
##      5        1.1192             nan     0.1000    0.0099
##      6        1.0977             nan     0.1000    0.0095
##      7        1.0824             nan     0.1000    0.0059
##      8        1.0634             nan     0.1000    0.0051
##      9        1.0495             nan     0.1000    0.0050
##     10        1.0366             nan     0.1000    0.0042
##     20        0.9408             nan     0.1000    0.0020
##     40        0.8643             nan     0.1000   -0.0011
##     60        0.8207             nan     0.1000    0.0002
##     80        0.7821             nan     0.1000   -0.0007
##    100        0.7551             nan     0.1000   -0.0013
##    120        0.7274             nan     0.1000    0.0009
##    140        0.7083             nan     0.1000   -0.0021
##    160        0.6853             nan     0.1000   -0.0007
##    180        0.6709             nan     0.1000   -0.0024
##    200        0.6519             nan     0.1000   -0.0003
##    220        0.6342             nan     0.1000   -0.0025
##    240        0.6173             nan     0.1000   -0.0009
##    260        0.5988             nan     0.1000   -0.0015
##    280        0.5875             nan     0.1000   -0.0010
##    300        0.5754             nan     0.1000   -0.0025
##    320        0.5636             nan     0.1000   -0.0002
##    340        0.5485             nan     0.1000   -0.0016
##    360        0.5371             nan     0.1000   -0.0004
##    380        0.5244             nan     0.1000   -0.0013
##    400        0.5110             nan     0.1000   -0.0017
##    420        0.4966             nan     0.1000   -0.0005
##    440        0.4852             nan     0.1000   -0.0010
##    460        0.4739             nan     0.1000   -0.0012
##    480        0.4635             nan     0.1000   -0.0010
##    500        0.4534             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2438             nan     0.1000    0.0204
##      2        1.2009             nan     0.1000    0.0174
##      3        1.1630             nan     0.1000    0.0152
##      4        1.1259             nan     0.1000    0.0171
##      5        1.0981             nan     0.1000    0.0095
##      6        1.0754             nan     0.1000    0.0069
##      7        1.0557             nan     0.1000    0.0080
##      8        1.0413             nan     0.1000    0.0029
##      9        1.0228             nan     0.1000    0.0075
##     10        1.0098             nan     0.1000    0.0042
##     20        0.8996             nan     0.1000    0.0014
##     40        0.8041             nan     0.1000    0.0004
##     60        0.7460             nan     0.1000   -0.0010
##     80        0.7057             nan     0.1000   -0.0014
##    100        0.6645             nan     0.1000   -0.0022
##    120        0.6283             nan     0.1000   -0.0013
##    140        0.5975             nan     0.1000   -0.0006
##    160        0.5697             nan     0.1000   -0.0017
##    180        0.5434             nan     0.1000   -0.0013
##    200        0.5197             nan     0.1000   -0.0014
##    220        0.4940             nan     0.1000   -0.0008
##    240        0.4740             nan     0.1000   -0.0015
##    260        0.4550             nan     0.1000   -0.0012
##    280        0.4365             nan     0.1000   -0.0003
##    300        0.4192             nan     0.1000   -0.0009
##    320        0.4049             nan     0.1000   -0.0001
##    340        0.3910             nan     0.1000   -0.0019
##    360        0.3781             nan     0.1000   -0.0006
##    380        0.3610             nan     0.1000   -0.0006
##    400        0.3436             nan     0.1000   -0.0003
##    420        0.3289             nan     0.1000   -0.0009
##    440        0.3163             nan     0.1000   -0.0003
##    460        0.3039             nan     0.1000   -0.0009
##    480        0.2938             nan     0.1000   -0.0004
##    500        0.2842             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2459             nan     0.1000    0.0182
##      2        1.1952             nan     0.1000    0.0210
##      3        1.1596             nan     0.1000    0.0147
##      4        1.1262             nan     0.1000    0.0159
##      5        1.1021             nan     0.1000    0.0095
##      6        1.0797             nan     0.1000    0.0071
##      7        1.0575             nan     0.1000    0.0074
##      8        1.0404             nan     0.1000    0.0048
##      9        1.0238             nan     0.1000    0.0045
##     10        1.0104             nan     0.1000    0.0045
##     20        0.9042             nan     0.1000    0.0020
##     40        0.8058             nan     0.1000   -0.0013
##     60        0.7465             nan     0.1000   -0.0017
##     80        0.7066             nan     0.1000   -0.0016
##    100        0.6663             nan     0.1000   -0.0021
##    120        0.6302             nan     0.1000   -0.0011
##    140        0.6018             nan     0.1000   -0.0008
##    160        0.5726             nan     0.1000   -0.0013
##    180        0.5432             nan     0.1000   -0.0014
##    200        0.5190             nan     0.1000   -0.0014
##    220        0.4924             nan     0.1000   -0.0008
##    240        0.4709             nan     0.1000   -0.0010
##    260        0.4491             nan     0.1000   -0.0006
##    280        0.4283             nan     0.1000   -0.0006
##    300        0.4082             nan     0.1000   -0.0010
##    320        0.3899             nan     0.1000   -0.0014
##    340        0.3754             nan     0.1000   -0.0005
##    360        0.3590             nan     0.1000   -0.0005
##    380        0.3447             nan     0.1000   -0.0008
##    400        0.3309             nan     0.1000   -0.0005
##    420        0.3170             nan     0.1000   -0.0011
##    440        0.3041             nan     0.1000   -0.0009
##    460        0.2933             nan     0.1000   -0.0012
##    480        0.2807             nan     0.1000   -0.0008
##    500        0.2711             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2427             nan     0.1000    0.0247
##      2        1.2042             nan     0.1000    0.0163
##      3        1.1739             nan     0.1000    0.0114
##      4        1.1415             nan     0.1000    0.0148
##      5        1.1111             nan     0.1000    0.0144
##      6        1.0812             nan     0.1000    0.0133
##      7        1.0620             nan     0.1000    0.0055
##      8        1.0405             nan     0.1000    0.0083
##      9        1.0256             nan     0.1000    0.0056
##     10        1.0099             nan     0.1000    0.0046
##     20        0.9013             nan     0.1000    0.0017
##     40        0.8004             nan     0.1000   -0.0013
##     60        0.7427             nan     0.1000   -0.0010
##     80        0.7036             nan     0.1000   -0.0022
##    100        0.6688             nan     0.1000   -0.0021
##    120        0.6396             nan     0.1000   -0.0034
##    140        0.6038             nan     0.1000   -0.0003
##    160        0.5798             nan     0.1000   -0.0019
##    180        0.5511             nan     0.1000   -0.0009
##    200        0.5283             nan     0.1000   -0.0008
##    220        0.5020             nan     0.1000   -0.0014
##    240        0.4780             nan     0.1000   -0.0010
##    260        0.4545             nan     0.1000   -0.0012
##    280        0.4367             nan     0.1000   -0.0016
##    300        0.4180             nan     0.1000   -0.0007
##    320        0.4020             nan     0.1000   -0.0010
##    340        0.3846             nan     0.1000   -0.0008
##    360        0.3688             nan     0.1000   -0.0014
##    380        0.3559             nan     0.1000   -0.0004
##    400        0.3420             nan     0.1000   -0.0007
##    420        0.3275             nan     0.1000   -0.0009
##    440        0.3179             nan     0.1000   -0.0010
##    460        0.3060             nan     0.1000   -0.0007
##    480        0.2932             nan     0.1000   -0.0017
##    500        0.2822             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2331             nan     0.2000    0.0272
##      2        1.1816             nan     0.2000    0.0219
##      3        1.1533             nan     0.2000    0.0123
##      4        1.1239             nan     0.2000    0.0129
##      5        1.0993             nan     0.2000    0.0122
##      6        1.0730             nan     0.2000    0.0060
##      7        1.0565             nan     0.2000    0.0050
##      8        1.0391             nan     0.2000    0.0066
##      9        1.0248             nan     0.2000    0.0061
##     10        1.0150             nan     0.2000    0.0018
##     20        0.9436             nan     0.2000    0.0012
##     40        0.8751             nan     0.2000   -0.0006
##     60        0.8395             nan     0.2000   -0.0023
##     80        0.8151             nan     0.2000   -0.0026
##    100        0.7949             nan     0.2000   -0.0022
##    120        0.7779             nan     0.2000   -0.0000
##    140        0.7655             nan     0.2000   -0.0027
##    160        0.7511             nan     0.2000   -0.0025
##    180        0.7401             nan     0.2000   -0.0024
##    200        0.7342             nan     0.2000   -0.0040
##    220        0.7226             nan     0.2000   -0.0024
##    240        0.7130             nan     0.2000   -0.0017
##    260        0.7061             nan     0.2000   -0.0023
##    280        0.7024             nan     0.2000   -0.0011
##    300        0.6901             nan     0.2000   -0.0019
##    320        0.6815             nan     0.2000   -0.0035
##    340        0.6758             nan     0.2000   -0.0020
##    360        0.6718             nan     0.2000   -0.0028
##    380        0.6620             nan     0.2000   -0.0026
##    400        0.6571             nan     0.2000   -0.0004
##    420        0.6525             nan     0.2000   -0.0024
##    440        0.6502             nan     0.2000   -0.0024
##    460        0.6418             nan     0.2000   -0.0032
##    480        0.6351             nan     0.2000   -0.0016
##    500        0.6305             nan     0.2000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2281             nan     0.2000    0.0275
##      2        1.1896             nan     0.2000    0.0213
##      3        1.1568             nan     0.2000    0.0136
##      4        1.1269             nan     0.2000    0.0134
##      5        1.1017             nan     0.2000    0.0108
##      6        1.0805             nan     0.2000    0.0060
##      7        1.0560             nan     0.2000    0.0111
##      8        1.0426             nan     0.2000    0.0028
##      9        1.0333             nan     0.2000    0.0017
##     10        1.0185             nan     0.2000    0.0067
##     20        0.9397             nan     0.2000   -0.0007
##     40        0.8709             nan     0.2000   -0.0008
##     60        0.8310             nan     0.2000   -0.0010
##     80        0.8089             nan     0.2000   -0.0023
##    100        0.7890             nan     0.2000   -0.0012
##    120        0.7780             nan     0.2000   -0.0013
##    140        0.7664             nan     0.2000   -0.0020
##    160        0.7529             nan     0.2000   -0.0005
##    180        0.7427             nan     0.2000   -0.0004
##    200        0.7334             nan     0.2000   -0.0016
##    220        0.7237             nan     0.2000   -0.0022
##    240        0.7182             nan     0.2000   -0.0006
##    260        0.7116             nan     0.2000   -0.0013
##    280        0.6998             nan     0.2000   -0.0020
##    300        0.6931             nan     0.2000   -0.0013
##    320        0.6870             nan     0.2000   -0.0015
##    340        0.6829             nan     0.2000   -0.0010
##    360        0.6742             nan     0.2000   -0.0014
##    380        0.6634             nan     0.2000   -0.0006
##    400        0.6621             nan     0.2000   -0.0021
##    420        0.6553             nan     0.2000   -0.0015
##    440        0.6489             nan     0.2000   -0.0004
##    460        0.6406             nan     0.2000   -0.0006
##    480        0.6369             nan     0.2000   -0.0018
##    500        0.6325             nan     0.2000   -0.0039
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2236             nan     0.2000    0.0236
##      2        1.1738             nan     0.2000    0.0251
##      3        1.1404             nan     0.2000    0.0158
##      4        1.1122             nan     0.2000    0.0100
##      5        1.0879             nan     0.2000    0.0074
##      6        1.0633             nan     0.2000    0.0120
##      7        1.0481             nan     0.2000    0.0061
##      8        1.0358             nan     0.2000    0.0051
##      9        1.0218             nan     0.2000    0.0036
##     10        1.0123             nan     0.2000    0.0029
##     20        0.9387             nan     0.2000   -0.0004
##     40        0.8722             nan     0.2000   -0.0024
##     60        0.8395             nan     0.2000   -0.0015
##     80        0.8176             nan     0.2000   -0.0005
##    100        0.8047             nan     0.2000   -0.0006
##    120        0.7931             nan     0.2000   -0.0010
##    140        0.7725             nan     0.2000   -0.0015
##    160        0.7573             nan     0.2000   -0.0008
##    180        0.7471             nan     0.2000   -0.0014
##    200        0.7389             nan     0.2000   -0.0010
##    220        0.7271             nan     0.2000   -0.0023
##    240        0.7174             nan     0.2000   -0.0008
##    260        0.7123             nan     0.2000   -0.0037
##    280        0.7004             nan     0.2000   -0.0017
##    300        0.6933             nan     0.2000   -0.0007
##    320        0.6870             nan     0.2000   -0.0041
##    340        0.6745             nan     0.2000   -0.0014
##    360        0.6686             nan     0.2000   -0.0013
##    380        0.6607             nan     0.2000   -0.0010
##    400        0.6529             nan     0.2000   -0.0014
##    420        0.6448             nan     0.2000   -0.0021
##    440        0.6383             nan     0.2000   -0.0011
##    460        0.6336             nan     0.2000   -0.0002
##    480        0.6288             nan     0.2000   -0.0009
##    500        0.6251             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2122             nan     0.2000    0.0350
##      2        1.1510             nan     0.2000    0.0291
##      3        1.1041             nan     0.2000    0.0151
##      4        1.0682             nan     0.2000    0.0131
##      5        1.0414             nan     0.2000    0.0098
##      6        1.0104             nan     0.2000    0.0107
##      7        0.9865             nan     0.2000    0.0064
##      8        0.9693             nan     0.2000    0.0075
##      9        0.9585             nan     0.2000    0.0001
##     10        0.9430             nan     0.2000    0.0024
##     20        0.8632             nan     0.2000   -0.0006
##     40        0.7796             nan     0.2000   -0.0012
##     60        0.7151             nan     0.2000   -0.0019
##     80        0.6786             nan     0.2000   -0.0004
##    100        0.6387             nan     0.2000   -0.0022
##    120        0.6012             nan     0.2000   -0.0025
##    140        0.5633             nan     0.2000   -0.0009
##    160        0.5328             nan     0.2000   -0.0023
##    180        0.5064             nan     0.2000   -0.0009
##    200        0.4805             nan     0.2000   -0.0009
##    220        0.4573             nan     0.2000   -0.0021
##    240        0.4417             nan     0.2000   -0.0042
##    260        0.4155             nan     0.2000   -0.0017
##    280        0.4010             nan     0.2000   -0.0019
##    300        0.3817             nan     0.2000   -0.0018
##    320        0.3636             nan     0.2000   -0.0002
##    340        0.3455             nan     0.2000   -0.0013
##    360        0.3354             nan     0.2000   -0.0013
##    380        0.3226             nan     0.2000   -0.0010
##    400        0.3096             nan     0.2000   -0.0012
##    420        0.2988             nan     0.2000   -0.0006
##    440        0.2889             nan     0.2000   -0.0018
##    460        0.2786             nan     0.2000   -0.0013
##    480        0.2698             nan     0.2000   -0.0013
##    500        0.2613             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2081             nan     0.2000    0.0378
##      2        1.1570             nan     0.2000    0.0223
##      3        1.1145             nan     0.2000    0.0176
##      4        1.0715             nan     0.2000    0.0184
##      5        1.0371             nan     0.2000    0.0128
##      6        1.0169             nan     0.2000    0.0027
##      7        0.9948             nan     0.2000    0.0073
##      8        0.9752             nan     0.2000    0.0066
##      9        0.9577             nan     0.2000    0.0065
##     10        0.9460             nan     0.2000    0.0036
##     20        0.8665             nan     0.2000   -0.0020
##     40        0.7844             nan     0.2000   -0.0019
##     60        0.7286             nan     0.2000   -0.0022
##     80        0.6890             nan     0.2000   -0.0022
##    100        0.6494             nan     0.2000   -0.0017
##    120        0.6147             nan     0.2000   -0.0014
##    140        0.5766             nan     0.2000   -0.0011
##    160        0.5434             nan     0.2000   -0.0011
##    180        0.5238             nan     0.2000   -0.0017
##    200        0.5005             nan     0.2000   -0.0049
##    220        0.4790             nan     0.2000   -0.0014
##    240        0.4584             nan     0.2000   -0.0012
##    260        0.4401             nan     0.2000   -0.0015
##    280        0.4157             nan     0.2000   -0.0026
##    300        0.3976             nan     0.2000   -0.0022
##    320        0.3810             nan     0.2000   -0.0021
##    340        0.3659             nan     0.2000   -0.0044
##    360        0.3496             nan     0.2000   -0.0014
##    380        0.3359             nan     0.2000   -0.0000
##    400        0.3219             nan     0.2000   -0.0006
##    420        0.3079             nan     0.2000   -0.0009
##    440        0.2958             nan     0.2000   -0.0010
##    460        0.2876             nan     0.2000   -0.0010
##    480        0.2771             nan     0.2000    0.0001
##    500        0.2651             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2109             nan     0.2000    0.0344
##      2        1.1494             nan     0.2000    0.0244
##      3        1.0971             nan     0.2000    0.0222
##      4        1.0634             nan     0.2000    0.0124
##      5        1.0289             nan     0.2000    0.0114
##      6        1.0055             nan     0.2000    0.0076
##      7        0.9826             nan     0.2000    0.0059
##      8        0.9696             nan     0.2000    0.0030
##      9        0.9572             nan     0.2000    0.0011
##     10        0.9384             nan     0.2000    0.0044
##     20        0.8602             nan     0.2000   -0.0018
##     40        0.7876             nan     0.2000   -0.0041
##     60        0.7457             nan     0.2000    0.0004
##     80        0.7068             nan     0.2000   -0.0026
##    100        0.6578             nan     0.2000   -0.0043
##    120        0.6173             nan     0.2000   -0.0031
##    140        0.5866             nan     0.2000   -0.0006
##    160        0.5592             nan     0.2000   -0.0007
##    180        0.5297             nan     0.2000   -0.0049
##    200        0.5041             nan     0.2000   -0.0007
##    220        0.4805             nan     0.2000   -0.0011
##    240        0.4548             nan     0.2000   -0.0007
##    260        0.4376             nan     0.2000   -0.0018
##    280        0.4166             nan     0.2000   -0.0008
##    300        0.4006             nan     0.2000   -0.0010
##    320        0.3850             nan     0.2000   -0.0043
##    340        0.3685             nan     0.2000   -0.0034
##    360        0.3542             nan     0.2000   -0.0005
##    380        0.3420             nan     0.2000   -0.0017
##    400        0.3288             nan     0.2000   -0.0018
##    420        0.3157             nan     0.2000   -0.0016
##    440        0.3052             nan     0.2000   -0.0014
##    460        0.2983             nan     0.2000   -0.0015
##    480        0.2882             nan     0.2000   -0.0008
##    500        0.2762             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1995             nan     0.2000    0.0357
##      2        1.1451             nan     0.2000    0.0160
##      3        1.0935             nan     0.2000    0.0123
##      4        1.0492             nan     0.2000    0.0164
##      5        1.0108             nan     0.2000    0.0130
##      6        0.9864             nan     0.2000    0.0042
##      7        0.9655             nan     0.2000    0.0060
##      8        0.9456             nan     0.2000    0.0041
##      9        0.9318             nan     0.2000    0.0026
##     10        0.9164             nan     0.2000    0.0028
##     20        0.8073             nan     0.2000    0.0004
##     40        0.7009             nan     0.2000   -0.0014
##     60        0.6374             nan     0.2000   -0.0020
##     80        0.5911             nan     0.2000   -0.0028
##    100        0.5330             nan     0.2000   -0.0007
##    120        0.4806             nan     0.2000   -0.0003
##    140        0.4648             nan     0.2000   -0.0024
##    160        0.4107             nan     0.2000   -0.0019
##    180        0.3753             nan     0.2000   -0.0009
##    200        0.3461             nan     0.2000   -0.0028
##    220        0.3205             nan     0.2000   -0.0006
##    240        0.2952             nan     0.2000   -0.0010
##    260        0.2767             nan     0.2000   -0.0024
##    280        0.2573             nan     0.2000   -0.0018
##    300        0.2374             nan     0.2000   -0.0015
##    320        0.2197             nan     0.2000   -0.0003
##    340        0.2050             nan     0.2000   -0.0012
##    360        0.1910             nan     0.2000   -0.0009
##    380        0.1763             nan     0.2000   -0.0012
##    400        0.1630             nan     0.2000   -0.0008
##    420        0.1525             nan     0.2000   -0.0009
##    440        0.1431             nan     0.2000   -0.0016
##    460        0.1330             nan     0.2000   -0.0006
##    480        0.1260             nan     0.2000   -0.0005
##    500        0.1171             nan     0.2000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2040             nan     0.2000    0.0409
##      2        1.1334             nan     0.2000    0.0260
##      3        1.0788             nan     0.2000    0.0187
##      4        1.0352             nan     0.2000    0.0143
##      5        1.0111             nan     0.2000    0.0104
##      6        0.9880             nan     0.2000    0.0043
##      7        0.9586             nan     0.2000    0.0132
##      8        0.9350             nan     0.2000    0.0024
##      9        0.9186             nan     0.2000    0.0014
##     10        0.9012             nan     0.2000    0.0010
##     20        0.8168             nan     0.2000   -0.0037
##     40        0.7194             nan     0.2000   -0.0003
##     60        0.6364             nan     0.2000   -0.0020
##     80        0.5788             nan     0.2000   -0.0022
##    100        0.5265             nan     0.2000   -0.0020
##    120        0.4765             nan     0.2000   -0.0004
##    140        0.4353             nan     0.2000   -0.0022
##    160        0.4002             nan     0.2000   -0.0023
##    180        0.3685             nan     0.2000   -0.0026
##    200        0.3474             nan     0.2000   -0.0031
##    220        0.3216             nan     0.2000   -0.0008
##    240        0.3006             nan     0.2000   -0.0009
##    260        0.2741             nan     0.2000   -0.0024
##    280        0.2598             nan     0.2000   -0.0022
##    300        0.2423             nan     0.2000   -0.0013
##    320        0.2257             nan     0.2000   -0.0016
##    340        0.2104             nan     0.2000   -0.0020
##    360        0.1953             nan     0.2000   -0.0004
##    380        0.1829             nan     0.2000   -0.0006
##    400        0.1705             nan     0.2000   -0.0012
##    420        0.1584             nan     0.2000   -0.0012
##    440        0.1493             nan     0.2000   -0.0010
##    460        0.1395             nan     0.2000   -0.0003
##    480        0.1302             nan     0.2000   -0.0009
##    500        0.1226             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2110             nan     0.2000    0.0327
##      2        1.1390             nan     0.2000    0.0284
##      3        1.0842             nan     0.2000    0.0224
##      4        1.0413             nan     0.2000    0.0140
##      5        1.0021             nan     0.2000    0.0152
##      6        0.9763             nan     0.2000    0.0089
##      7        0.9537             nan     0.2000    0.0043
##      8        0.9347             nan     0.2000    0.0042
##      9        0.9231             nan     0.2000   -0.0015
##     10        0.9080             nan     0.2000    0.0037
##     20        0.8291             nan     0.2000   -0.0006
##     40        0.7279             nan     0.2000   -0.0025
##     60        0.6563             nan     0.2000   -0.0021
##     80        0.6027             nan     0.2000   -0.0037
##    100        0.5373             nan     0.2000   -0.0015
##    120        0.4893             nan     0.2000   -0.0004
##    140        0.4457             nan     0.2000   -0.0020
##    160        0.4049             nan     0.2000    0.0001
##    180        0.3789             nan     0.2000   -0.0021
##    200        0.3485             nan     0.2000   -0.0013
##    220        0.3191             nan     0.2000   -0.0028
##    240        0.3006             nan     0.2000   -0.0027
##    260        0.2799             nan     0.2000   -0.0004
##    280        0.2660             nan     0.2000   -0.0020
##    300        0.2489             nan     0.2000   -0.0009
##    320        0.2299             nan     0.2000   -0.0007
##    340        0.2135             nan     0.2000   -0.0006
##    360        0.1994             nan     0.2000   -0.0013
##    380        0.1863             nan     0.2000   -0.0014
##    400        0.1754             nan     0.2000   -0.0008
##    420        0.1641             nan     0.2000   -0.0010
##    440        0.1556             nan     0.2000   -0.0006
##    460        0.1467             nan     0.2000   -0.0002
##    480        0.1387             nan     0.2000   -0.0005
##    500        0.1306             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2061             nan     0.3000    0.0399
##      2        1.1551             nan     0.3000    0.0181
##      3        1.1096             nan     0.3000    0.0207
##      4        1.0813             nan     0.3000    0.0085
##      5        1.0467             nan     0.3000    0.0147
##      6        1.0270             nan     0.3000    0.0086
##      7        1.0079             nan     0.3000    0.0077
##      8        0.9869             nan     0.3000    0.0058
##      9        0.9729             nan     0.3000    0.0028
##     10        0.9654             nan     0.3000   -0.0009
##     20        0.8921             nan     0.3000    0.0003
##     40        0.8310             nan     0.3000    0.0005
##     60        0.8046             nan     0.3000   -0.0014
##     80        0.7770             nan     0.3000   -0.0032
##    100        0.7653             nan     0.3000   -0.0034
##    120        0.7530             nan     0.3000   -0.0065
##    140        0.7436             nan     0.3000   -0.0007
##    160        0.7211             nan     0.3000   -0.0024
##    180        0.7083             nan     0.3000   -0.0019
##    200        0.6985             nan     0.3000   -0.0055
##    220        0.6889             nan     0.3000   -0.0009
##    240        0.6795             nan     0.3000   -0.0027
##    260        0.6734             nan     0.3000   -0.0030
##    280        0.6614             nan     0.3000   -0.0010
##    300        0.6483             nan     0.3000   -0.0027
##    320        0.6370             nan     0.3000   -0.0007
##    340        0.6290             nan     0.3000   -0.0024
##    360        0.6226             nan     0.3000   -0.0053
##    380        0.6137             nan     0.3000   -0.0025
##    400        0.6117             nan     0.3000   -0.0022
##    420        0.5997             nan     0.3000   -0.0006
##    440        0.5949             nan     0.3000   -0.0029
##    460        0.5869             nan     0.3000   -0.0054
##    480        0.5846             nan     0.3000   -0.0031
##    500        0.5776             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1990             nan     0.3000    0.0411
##      2        1.1444             nan     0.3000    0.0218
##      3        1.1091             nan     0.3000    0.0129
##      4        1.0755             nan     0.3000    0.0152
##      5        1.0447             nan     0.3000    0.0151
##      6        1.0184             nan     0.3000    0.0061
##      7        1.0046             nan     0.3000    0.0037
##      8        0.9960             nan     0.3000   -0.0011
##      9        0.9843             nan     0.3000    0.0035
##     10        0.9674             nan     0.3000    0.0060
##     20        0.8951             nan     0.3000   -0.0016
##     40        0.8366             nan     0.3000   -0.0010
##     60        0.8066             nan     0.3000   -0.0014
##     80        0.7772             nan     0.3000   -0.0013
##    100        0.7579             nan     0.3000   -0.0005
##    120        0.7469             nan     0.3000   -0.0014
##    140        0.7255             nan     0.3000   -0.0017
##    160        0.7148             nan     0.3000   -0.0017
##    180        0.7001             nan     0.3000    0.0004
##    200        0.6897             nan     0.3000   -0.0022
##    220        0.6762             nan     0.3000   -0.0036
##    240        0.6656             nan     0.3000   -0.0023
##    260        0.6566             nan     0.3000   -0.0015
##    280        0.6510             nan     0.3000   -0.0039
##    300        0.6414             nan     0.3000   -0.0018
##    320        0.6284             nan     0.3000   -0.0036
##    340        0.6204             nan     0.3000   -0.0014
##    360        0.6153             nan     0.3000   -0.0032
##    380        0.6057             nan     0.3000   -0.0046
##    400        0.5981             nan     0.3000   -0.0028
##    420        0.5915             nan     0.3000   -0.0041
##    440        0.5879             nan     0.3000   -0.0027
##    460        0.5832             nan     0.3000   -0.0029
##    480        0.5781             nan     0.3000   -0.0029
##    500        0.5723             nan     0.3000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1944             nan     0.3000    0.0372
##      2        1.1495             nan     0.3000    0.0182
##      3        1.1109             nan     0.3000    0.0144
##      4        1.0750             nan     0.3000    0.0172
##      5        1.0425             nan     0.3000    0.0127
##      6        1.0150             nan     0.3000    0.0070
##      7        1.0017             nan     0.3000    0.0008
##      8        0.9912             nan     0.3000    0.0047
##      9        0.9791             nan     0.3000    0.0012
##     10        0.9653             nan     0.3000    0.0055
##     20        0.9046             nan     0.3000    0.0010
##     40        0.8403             nan     0.3000   -0.0016
##     60        0.8035             nan     0.3000   -0.0018
##     80        0.7815             nan     0.3000   -0.0019
##    100        0.7695             nan     0.3000   -0.0037
##    120        0.7491             nan     0.3000   -0.0068
##    140        0.7325             nan     0.3000   -0.0016
##    160        0.7144             nan     0.3000   -0.0034
##    180        0.7011             nan     0.3000   -0.0049
##    200        0.6886             nan     0.3000   -0.0055
##    220        0.6759             nan     0.3000   -0.0027
##    240        0.6676             nan     0.3000   -0.0015
##    260        0.6533             nan     0.3000   -0.0023
##    280        0.6468             nan     0.3000   -0.0006
##    300        0.6430             nan     0.3000   -0.0030
##    320        0.6363             nan     0.3000   -0.0021
##    340        0.6260             nan     0.3000   -0.0037
##    360        0.6155             nan     0.3000   -0.0032
##    380        0.6119             nan     0.3000   -0.0065
##    400        0.6050             nan     0.3000   -0.0020
##    420        0.5977             nan     0.3000   -0.0042
##    440        0.5862             nan     0.3000   -0.0026
##    460        0.5820             nan     0.3000   -0.0020
##    480        0.5742             nan     0.3000   -0.0026
##    500        0.5662             nan     0.3000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1815             nan     0.3000    0.0564
##      2        1.1069             nan     0.3000    0.0338
##      3        1.0604             nan     0.3000    0.0144
##      4        1.0179             nan     0.3000    0.0164
##      5        0.9913             nan     0.3000    0.0085
##      6        0.9638             nan     0.3000    0.0077
##      7        0.9451             nan     0.3000    0.0024
##      8        0.9305             nan     0.3000    0.0007
##      9        0.9163             nan     0.3000    0.0021
##     10        0.9049             nan     0.3000   -0.0043
##     20        0.8208             nan     0.3000   -0.0043
##     40        0.7507             nan     0.3000   -0.0074
##     60        0.6852             nan     0.3000   -0.0099
##     80        0.6242             nan     0.3000   -0.0034
##    100        0.5828             nan     0.3000   -0.0023
##    120        0.5458             nan     0.3000   -0.0043
##    140        0.5161             nan     0.3000   -0.0086
##    160        0.4848             nan     0.3000   -0.0002
##    180        0.4634             nan     0.3000   -0.0055
##    200        0.4379             nan     0.3000   -0.0017
##    220        0.4016             nan     0.3000   -0.0003
##    240        0.3719             nan     0.3000   -0.0015
##    260        0.3536             nan     0.3000   -0.0028
##    280        0.3344             nan     0.3000    0.0001
##    300        0.3201             nan     0.3000   -0.0015
##    320        0.3044             nan     0.3000   -0.0035
##    340        0.2862             nan     0.3000   -0.0005
##    360        0.2664             nan     0.3000   -0.0008
##    380        0.2555             nan     0.3000   -0.0008
##    400        0.2404             nan     0.3000   -0.0001
##    420        0.2273             nan     0.3000   -0.0011
##    440        0.2216             nan     0.3000   -0.0020
##    460        0.2098             nan     0.3000   -0.0006
##    480        0.2030             nan     0.3000   -0.0016
##    500        0.1938             nan     0.3000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1820             nan     0.3000    0.0480
##      2        1.1112             nan     0.3000    0.0309
##      3        1.0554             nan     0.3000    0.0252
##      4        1.0102             nan     0.3000    0.0182
##      5        0.9800             nan     0.3000    0.0053
##      6        0.9589             nan     0.3000    0.0048
##      7        0.9458             nan     0.3000   -0.0054
##      8        0.9251             nan     0.3000    0.0071
##      9        0.9144             nan     0.3000   -0.0026
##     10        0.8986             nan     0.3000    0.0006
##     20        0.8201             nan     0.3000   -0.0028
##     40        0.7272             nan     0.3000   -0.0018
##     60        0.6594             nan     0.3000   -0.0039
##     80        0.6080             nan     0.3000   -0.0034
##    100        0.5675             nan     0.3000   -0.0057
##    120        0.5320             nan     0.3000   -0.0035
##    140        0.4976             nan     0.3000   -0.0015
##    160        0.4666             nan     0.3000   -0.0021
##    180        0.4378             nan     0.3000   -0.0035
##    200        0.4117             nan     0.3000   -0.0021
##    220        0.3873             nan     0.3000   -0.0025
##    240        0.3704             nan     0.3000   -0.0007
##    260           inf             nan     0.3000       nan
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1784             nan     0.3000    0.0606
##      2        1.0945             nan     0.3000    0.0378
##      3        1.0434             nan     0.3000    0.0157
##      4        1.0110             nan     0.3000    0.0125
##      5        0.9845             nan     0.3000    0.0053
##      6        0.9682             nan     0.3000    0.0003
##      7        0.9461             nan     0.3000    0.0065
##      8        0.9268             nan     0.3000    0.0014
##      9        0.9154             nan     0.3000   -0.0027
##     10        0.8970             nan     0.3000    0.0068
##     20        0.8189             nan     0.3000   -0.0007
##     40        0.7285             nan     0.3000   -0.0040
##     60        0.6702             nan     0.3000   -0.0042
##     80        0.6096             nan     0.3000   -0.0021
##    100        0.5676             nan     0.3000   -0.0012
##    120        0.5283             nan     0.3000   -0.0028
##    140        0.5077             nan     0.3000   -0.0043
##    160        0.4651             nan     0.3000   -0.0011
##    180        0.4474             nan     0.3000   -0.0031
##    200        0.4256             nan     0.3000   -0.0033
##    220        0.3991             nan     0.3000   -0.0007
##    240        0.3771             nan     0.3000   -0.0034
##    260        0.3461             nan     0.3000   -0.0001
##    280        0.3328             nan     0.3000   -0.0041
##    300        0.3142             nan     0.3000   -0.0037
##    320        0.2998             nan     0.3000   -0.0017
##    340        0.2815             nan     0.3000   -0.0011
##    360        0.2634             nan     0.3000   -0.0014
##    380        0.2497             nan     0.3000   -0.0009
##    400        0.2347             nan     0.3000   -0.0030
##    420        0.2234             nan     0.3000   -0.0024
##    440        0.2109             nan     0.3000   -0.0010
##    460        0.1995             nan     0.3000   -0.0010
##    480        0.1905             nan     0.3000   -0.0020
##    500        0.1853             nan     0.3000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1548             nan     0.3000    0.0525
##      2        1.0745             nan     0.3000    0.0293
##      3        1.0162             nan     0.3000    0.0179
##      4        0.9688             nan     0.3000    0.0178
##      5        0.9505             nan     0.3000   -0.0018
##      6        0.9282             nan     0.3000   -0.0014
##      7        0.9066             nan     0.3000    0.0011
##      8        0.8988             nan     0.3000   -0.0104
##      9        0.8832             nan     0.3000    0.0024
##     10        0.8719             nan     0.3000   -0.0022
##     20        0.7706             nan     0.3000   -0.0043
##     40        0.6526             nan     0.3000   -0.0011
##     60        0.5875             nan     0.3000   -0.0416
##     80        1.2417             nan     0.3000   -0.0052
##    100        1.2177             nan     0.3000   -0.0008
##    120        1.6391             nan     0.3000   -0.5285
##    140        1.5249             nan     0.3000   -0.0003
##    160     4073.1442             nan     0.3000   -0.0012
##    180     4073.0797             nan     0.3000   -0.0008
##    200     4073.0343             nan     0.3000   -0.0046
##    220     4072.9663             nan     0.3000   -0.0023
##    240     4072.6440             nan     0.3000   -0.0003
##    260     4072.6338             nan     0.3000   -0.0022
##    280     4072.5388             nan     0.3000   -0.0002
##    300     4072.5145             nan     0.3000   -0.0006
##    320     4102.3784             nan     0.3000   -0.0018
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1837             nan     0.3000    0.0568
##      2        1.0997             nan     0.3000    0.0267
##      3        1.0121             nan     0.3000    0.0339
##      4        0.9720             nan     0.3000    0.0096
##      5        0.9403             nan     0.3000    0.0023
##      6        0.9239             nan     0.3000   -0.0032
##      7        0.9093             nan     0.3000    0.0026
##      8        0.8932             nan     0.3000    0.0003
##      9        0.8765             nan     0.3000    0.0010
##     10        0.8594             nan     0.3000    0.0003
##     20        0.7593             nan     0.3000    0.0006
##     40        0.6635             nan     0.3000   -0.0089
##     60        0.5595             nan     0.3000   -0.0060
##     80        0.4968             nan     0.3000   -0.0039
##    100        0.4427             nan     0.3000   -0.0055
##    120        0.3924             nan     0.3000   -0.0010
##    140        0.3427             nan     0.3000   -0.0008
##    160        0.3038             nan     0.3000   -0.0027
##    180        0.2761             nan     0.3000   -0.0017
##    200        0.2475             nan     0.3000   -0.0022
##    220        0.2233             nan     0.3000   -0.0045
##    240        0.2002             nan     0.3000   -0.0013
##    260        0.1811             nan     0.3000   -0.0016
##    280        0.1643             nan     0.3000   -0.0012
##    300        0.1480             nan     0.3000   -0.0016
##    320        0.1344             nan     0.3000   -0.0015
##    340        0.1215             nan     0.3000   -0.0007
##    360        0.1103             nan     0.3000   -0.0015
##    380        0.1004             nan     0.3000   -0.0009
##    400        0.0929             nan     0.3000   -0.0021
##    420        0.0836             nan     0.3000   -0.0005
##    440        0.0775             nan     0.3000   -0.0002
##    460        0.0715             nan     0.3000   -0.0005
##    480        0.0661             nan     0.3000   -0.0007
##    500        0.0611             nan     0.3000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1498             nan     0.3000    0.0607
##      2        1.0577             nan     0.3000    0.0430
##      3        1.0064             nan     0.3000    0.0179
##      4        0.9755             nan     0.3000    0.0041
##      5        0.9587             nan     0.3000   -0.0019
##      6        0.9323             nan     0.3000    0.0024
##      7        0.9089             nan     0.3000   -0.0002
##      8        0.8914             nan     0.3000   -0.0018
##      9        0.8792             nan     0.3000   -0.0021
##     10        0.8644             nan     0.3000   -0.0015
##     20        0.7669             nan     0.3000   -0.0011
##     40        0.6732             nan     0.3000   -0.0046
##     60        0.5966             nan     0.3000   -0.0072
##     80        0.5358             nan     0.3000   -0.0038
##    100        0.4658             nan     0.3000   -0.0026
##    120        0.4063             nan     0.3000   -0.0039
##    140        0.3678             nan     0.3000   -0.0040
##    160        0.3295             nan     0.3000   -0.0011
##    180        0.2909             nan     0.3000   -0.0045
##    200        0.2622             nan     0.3000   -0.0012
##    220        0.2355             nan     0.3000   -0.0016
##    240        0.2100             nan     0.3000   -0.0023
##    260        0.1858             nan     0.3000   -0.0028
##    280        0.1685             nan     0.3000   -0.0019
##    300        0.1555             nan     0.3000   -0.0013
##    320        0.1399             nan     0.3000   -0.0025
##    340        0.1278             nan     0.3000   -0.0009
##    360        0.1167             nan     0.3000   -0.0007
##    380        0.1081             nan     0.3000   -0.0008
##    400        0.1022             nan     0.3000   -0.0009
##    420        0.0915             nan     0.3000   -0.0017
##    440        0.0837             nan     0.3000   -0.0009
##    460        0.0759             nan     0.3000   -0.0005
##    480        0.0692             nan     0.3000   -0.0004
##    500        0.0629             nan     0.3000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1592             nan     0.5000    0.0528
##      2        1.1033             nan     0.5000    0.0210
##      3        1.0483             nan     0.5000    0.0165
##      4        1.0160             nan     0.5000    0.0112
##      5        0.9957             nan     0.5000    0.0101
##      6        0.9670             nan     0.5000    0.0119
##      7        0.9588             nan     0.5000   -0.0005
##      8        0.9428             nan     0.5000    0.0023
##      9        0.9314             nan     0.5000   -0.0029
##     10        0.9236             nan     0.5000   -0.0014
##     20        0.8671             nan     0.5000   -0.0120
##     40        0.8256             nan     0.5000   -0.0133
##     60        0.7772             nan     0.5000   -0.0089
##     80        0.7487             nan     0.5000   -0.0098
##    100        0.7152             nan     0.5000   -0.0107
##    120        0.6875             nan     0.5000   -0.0060
##    140        0.6664             nan     0.5000   -0.0013
##    160        0.6544             nan     0.5000   -0.0066
##    180        0.6436             nan     0.5000   -0.0077
##    200        0.6280             nan     0.5000   -0.0016
##    220        0.6233             nan     0.5000   -0.0093
##    240        0.6077             nan     0.5000   -0.0060
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1661             nan     0.5000    0.0524
##      2        1.0944             nan     0.5000    0.0197
##      3        1.0461             nan     0.5000    0.0145
##      4        1.0019             nan     0.5000    0.0229
##      5        0.9794             nan     0.5000   -0.0021
##      6        0.9536             nan     0.5000    0.0036
##      7        0.9413             nan     0.5000    0.0018
##      8        0.9354             nan     0.5000   -0.0072
##      9        0.9407             nan     0.5000   -0.0154
##     10        0.9223             nan     0.5000    0.0040
##     20        0.8581             nan     0.5000   -0.0022
##     40        0.8059             nan     0.5000   -0.0077
##     60        0.7683             nan     0.5000   -0.0040
##     80        0.7360             nan     0.5000   -0.0041
##    100        0.7246             nan     0.5000   -0.0060
##    120        0.7090             nan     0.5000   -0.0132
##    140        0.6850             nan     0.5000   -0.0109
##    160        0.6643             nan     0.5000   -0.0077
##    180        0.6440             nan     0.5000   -0.0060
##    200        0.6331             nan     0.5000   -0.0067
##    220        0.6225             nan     0.5000   -0.0021
##    240        0.6074             nan     0.5000   -0.0040
##    260        0.5989             nan     0.5000   -0.0000
##    280        0.5859             nan     0.5000   -0.0084
##    300        0.5729             nan     0.5000   -0.0046
##    320        0.5674             nan     0.5000   -0.0010
##    340        0.5627             nan     0.5000   -0.0066
##    360        0.5568             nan     0.5000   -0.0053
##    380        0.5491             nan     0.5000   -0.0111
##    400        0.5453             nan     0.5000   -0.0045
##    420        0.5279             nan     0.5000   -0.0080
##    440        0.5254             nan     0.5000   -0.0061
##    460        0.5109             nan     0.5000   -0.0017
##    480        0.5115             nan     0.5000   -0.0062
##    500        0.4949             nan     0.5000   -0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1781             nan     0.5000    0.0558
##      2        1.1190             nan     0.5000    0.0179
##      3        1.0519             nan     0.5000    0.0296
##      4        1.0166             nan     0.5000    0.0190
##      5        0.9963             nan     0.5000    0.0078
##      6        0.9797             nan     0.5000   -0.0005
##      7        0.9687             nan     0.5000    0.0007
##      8        0.9510             nan     0.5000    0.0016
##      9        0.9417             nan     0.5000   -0.0025
##     10        0.9300             nan     0.5000   -0.0021
##     20        0.8678             nan     0.5000   -0.0028
##     40        0.8190             nan     0.5000   -0.0024
##     60        0.7899             nan     0.5000   -0.0099
##     80        0.7618             nan     0.5000   -0.0002
##    100        0.7360             nan     0.5000   -0.0042
##    120        0.7139             nan     0.5000   -0.0051
##    140        0.6990             nan     0.5000   -0.0025
##    160        0.6841             nan     0.5000   -0.0074
##    180        0.6670             nan     0.5000    0.0006
##    200        0.6636             nan     0.5000   -0.0094
##    220        0.6518             nan     0.5000   -0.0018
##    240        0.6326             nan     0.5000   -0.0021
##    260        0.6104             nan     0.5000   -0.0021
##    280        0.6013             nan     0.5000   -0.0045
##    300        0.5884             nan     0.5000   -0.0049
##    320        0.5733             nan     0.5000   -0.0037
##    340        0.5659             nan     0.5000   -0.0030
##    360        0.5605             nan     0.5000   -0.0017
##    380        0.5531             nan     0.5000   -0.0045
##    400        0.5483             nan     0.5000   -0.0034
##    420        0.5381             nan     0.5000   -0.0010
##    440        0.5369             nan     0.5000   -0.0073
##    460        0.5272             nan     0.5000   -0.0027
##    480        0.5225             nan     0.5000   -0.0023
##    500        0.5154             nan     0.5000   -0.0057
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1290             nan     0.5000    0.0747
##      2        1.0295             nan     0.5000    0.0442
##      3        0.9961             nan     0.5000    0.0010
##      4        0.9491             nan     0.5000    0.0177
##      5        0.9287             nan     0.5000   -0.0010
##      6        0.8981             nan     0.5000   -0.0030
##      7        0.8825             nan     0.5000   -0.0062
##      8        0.8703             nan     0.5000   -0.0027
##      9        0.8637             nan     0.5000   -0.0079
##     10        0.8490             nan     0.5000    0.0002
##     20        0.7858             nan     0.5000   -0.0125
##     40           inf             nan     0.5000      -inf
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1175             nan     0.5000    0.0770
##      2        1.0578             nan     0.5000    0.0030
##      3        1.0051             nan     0.5000    0.0269
##      4        0.9891             nan     0.5000   -0.0028
##      5        0.9635             nan     0.5000   -0.0008
##      6        0.9432             nan     0.5000   -0.0023
##      7        0.9298             nan     0.5000   -0.0048
##      8        0.9265             nan     0.5000   -0.0081
##      9        0.9074             nan     0.5000    0.0008
##     10        0.8932             nan     0.5000   -0.0006
##     20        0.8088             nan     0.5000   -0.0068
##     40        0.7246             nan     0.5000   -0.0044
##     60        0.6403             nan     0.5000   -0.0053
##     80        0.5809             nan     0.5000   -0.0013
##    100        0.5388             nan     0.5000   -0.0061
##    120        0.4813             nan     0.5000   -0.0014
##    140        0.4292             nan     0.5000   -0.0047
##    160        0.4009             nan     0.5000   -0.0084
##    180        0.3482             nan     0.5000   -0.0019
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1295             nan     0.5000    0.0692
##      2        1.0447             nan     0.5000    0.0322
##      3        0.9793             nan     0.5000    0.0183
##      4        0.9506             nan     0.5000    0.0038
##      5        0.9299             nan     0.5000    0.0008
##      6        0.9083             nan     0.5000    0.0009
##      7        0.8932             nan     0.5000   -0.0015
##      8        0.8860             nan     0.5000   -0.0091
##      9        0.8906             nan     0.5000   -0.0282
##     10        0.8795             nan     0.5000   -0.0152
##     20        0.8376             nan     0.5000   -0.0344
##     40        0.7189             nan     0.5000   -0.0036
##     60        0.6456             nan     0.5000   -0.0046
##     80        0.5767             nan     0.5000   -0.0068
##    100        0.5141             nan     0.5000   -0.0066
##    120        0.4715             nan     0.5000   -0.0009
##    140        0.4279             nan     0.5000   -0.0097
##    160        0.3782             nan     0.5000   -0.0021
##    180        0.3417             nan     0.5000   -0.0050
##    200        0.3014             nan     0.5000   -0.0058
##    220        0.2753             nan     0.5000   -0.0054
##    240        0.2528             nan     0.5000   -0.0061
##    260        0.2222             nan     0.5000   -0.0044
##    280        0.1985             nan     0.5000   -0.0013
##    300        0.1795             nan     0.5000   -0.0018
##    320        0.1684             nan     0.5000   -0.0026
##    340        0.1612             nan     0.5000   -0.0021
##    360        0.1477             nan     0.5000   -0.0009
##    380        0.1367             nan     0.5000   -0.0010
##    400        0.1296             nan     0.5000   -0.0043
##    420        0.1189             nan     0.5000   -0.0018
##    440        0.1102             nan     0.5000   -0.0011
##    460        0.0989             nan     0.5000   -0.0002
##    480        0.0924             nan     0.5000   -0.0019
##    500        0.0842             nan     0.5000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1039             nan     0.5000    0.0793
##      2        1.0285             nan     0.5000    0.0151
##      3        0.9526             nan     0.5000    0.0237
##      4        0.9125             nan     0.5000    0.0075
##      5        0.8765             nan     0.5000   -0.0059
##      6        0.8585             nan     0.5000   -0.0053
##      7        0.8543             nan     0.5000   -0.0127
##      8        0.8425             nan     0.5000   -0.0076
##      9        0.8167             nan     0.5000    0.0026
##     10        0.8121             nan     0.5000   -0.0200
##     20        0.6988             nan     0.5000   -0.0098
##     40        0.5468             nan     0.5000   -0.0056
##     60        0.4441             nan     0.5000   -0.0103
##     80        0.3935             nan     0.5000   -0.0037
##    100        0.2983             nan     0.5000   -0.0036
##    120        0.2505             nan     0.5000   -0.0027
##    140        0.2099             nan     0.5000   -0.0029
##    160        0.1705             nan     0.5000   -0.0018
##    180        0.1450             nan     0.5000   -0.0030
##    200        0.1238             nan     0.5000   -0.0010
##    220        0.1064             nan     0.5000   -0.0005
##    240        0.0934             nan     0.5000   -0.0007
##    260        0.0819             nan     0.5000   -0.0015
##    280        0.0738             nan     0.5000   -0.0013
##    300        0.0608             nan     0.5000    0.0001
##    320        0.0555             nan     0.5000   -0.0020
##    340        0.0473             nan     0.5000   -0.0003
##    360        0.0432             nan     0.5000   -0.0005
##    380        0.0386             nan     0.5000   -0.0006
##    400        0.0354             nan     0.5000   -0.0000
##    420        0.0324             nan     0.5000   -0.0007
##    440        0.0290             nan     0.5000   -0.0005
##    460        0.0269             nan     0.5000   -0.0006
##    480        0.0238             nan     0.5000   -0.0003
##    500        0.0221             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0911             nan     0.5000    0.0978
##      2        1.0012             nan     0.5000    0.0337
##      3        0.9341             nan     0.5000    0.0172
##      4        0.9219             nan     0.5000   -0.0080
##      5        0.8908             nan     0.5000    0.0078
##      6        0.8658             nan     0.5000    0.0037
##      7        0.8566             nan     0.5000   -0.0134
##      8        0.8371             nan     0.5000   -0.0024
##      9        0.8255             nan     0.5000   -0.0141
##     10        0.8012             nan     0.5000    0.0004
##     20        0.7339             nan     0.5000   -0.0065
##     40        0.5987             nan     0.5000   -0.0099
##     60        0.5148             nan     0.5000   -0.0082
##     80        0.4039             nan     0.5000   -0.0100
##    100        0.3424             nan     0.5000   -0.0037
##    120        0.2742             nan     0.5000   -0.0011
##    140        0.2318             nan     0.5000   -0.0046
##    160        0.1953             nan     0.5000   -0.0007
##    180        0.1681             nan     0.5000   -0.0048
##    200        0.1358             nan     0.5000   -0.0026
##    220        0.1143             nan     0.5000   -0.0015
##    240        0.0991             nan     0.5000   -0.0023
##    260        0.0857             nan     0.5000   -0.0020
##    280        0.0737             nan     0.5000   -0.0015
##    300        0.0639             nan     0.5000   -0.0004
##    320        0.0595             nan     0.5000   -0.0017
##    340        0.0520             nan     0.5000   -0.0013
##    360        0.0446             nan     0.5000   -0.0010
##    380        0.0395             nan     0.5000   -0.0008
##    400        0.0359             nan     0.5000   -0.0004
##    420        0.0323             nan     0.5000   -0.0005
##    440        0.0300             nan     0.5000   -0.0008
##    460        0.0261             nan     0.5000   -0.0004
##    480        0.0233             nan     0.5000   -0.0002
##    500        0.0212             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0874             nan     0.5000    0.0753
##      2        1.0193             nan     0.5000    0.0160
##      3        0.9677             nan     0.5000    0.0135
##      4        0.9361             nan     0.5000    0.0003
##      5        0.8907             nan     0.5000    0.0114
##      6        0.8631             nan     0.5000   -0.0073
##      7        0.8491             nan     0.5000   -0.0106
##      8        0.8367             nan     0.5000   -0.0082
##      9        0.8125             nan     0.5000    0.0006
##     10        0.7929             nan     0.5000   -0.0077
##     20        0.6837             nan     0.5000   -0.0033
##     40        0.5599             nan     0.5000   -0.0076
##     60        0.4473             nan     0.5000   -0.0044
##     80        0.3511             nan     0.5000    0.0005
##    100        0.2933             nan     0.5000   -0.0039
##    120        0.2444             nan     0.5000   -0.0008
##    140        0.2072             nan     0.5000   -0.0032
##    160        0.1709             nan     0.5000   -0.0017
##    180        0.1400             nan     0.5000   -0.0023
##    200        0.1239             nan     0.5000   -0.0028
##    220        0.1038             nan     0.5000   -0.0007
##    240        0.0899             nan     0.5000   -0.0025
##    260        0.0750             nan     0.5000   -0.0006
##    280        0.0663             nan     0.5000   -0.0022
##    300        0.0582             nan     0.5000   -0.0011
##    320        0.0495             nan     0.5000   -0.0013
##    340        0.0440             nan     0.5000   -0.0003
##    360        0.0385             nan     0.5000   -0.0006
##    380        0.0332             nan     0.5000   -0.0007
##    400        0.0292             nan     0.5000   -0.0007
##    420        0.0252             nan     0.5000   -0.0003
##    440        0.0217             nan     0.5000   -0.0003
##    460        0.0201             nan     0.5000   -0.0003
##    480        0.0175             nan     0.5000   -0.0002
##    500        0.0153             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1277             nan     1.0000    0.0469
##      2        1.0630             nan     1.0000    0.0154
##      3        1.0079             nan     1.0000    0.0105
##      4        0.9848             nan     1.0000   -0.0001
##      5        0.9722             nan     1.0000   -0.0131
##      6        1.0275             nan     1.0000   -0.0703
##      7        1.0128             nan     1.0000    0.0019
##      8        1.0298             nan     1.0000   -0.0278
##      9        1.0141             nan     1.0000   -0.0047
##     10        1.0015             nan     1.0000   -0.0085
##     20        0.9403             nan     1.0000   -0.0121
##     40        1.6101             nan     1.0000   -0.0217
##     60        1.7129             nan     1.0000   -0.0530
##     80 14103816.1715             nan     1.0000    0.0000
##    100 14103816.0541             nan     1.0000    0.0355
##    120 14103817.8795             nan     1.0000   -0.0083
##    140 14103817.8677             nan     1.0000    0.0024
##    160 14103817.8340             nan     1.0000    0.0007
##    180 14103817.8037             nan     1.0000   -0.0031
##    200 14103817.8233             nan     1.0000   -0.0026
##    220 14103819.7096             nan     1.0000    0.0022
##    240 14103819.6722             nan     1.0000    0.0024
##    260 14103819.6417             nan     1.0000   -0.0008
##    280 14103819.6305             nan     1.0000   -0.0005
##    300 14103819.5962             nan     1.0000    0.0019
##    320 14103819.5459             nan     1.0000   -0.0002
##    340 14103819.5182             nan     1.0000    0.0013
##    360 14103819.4915             nan     1.0000    0.0008
##    380 14103819.4756             nan     1.0000    0.0002
##    400 14103819.4497             nan     1.0000    0.0039
##    420 14103819.4481             nan     1.0000   -0.0009
##    440 14103819.4417             nan     1.0000   -0.0017
##    460 14103819.4453             nan     1.0000   -0.0121
##    480 14103819.4155             nan     1.0000    0.0006
##    500 14103819.4059             nan     1.0000   -0.0152
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1408             nan     1.0000    0.0427
##      2        1.0879             nan     1.0000    0.0063
##      3        1.0224             nan     1.0000    0.0211
##      4        1.0392             nan     1.0000   -0.0390
##      5        1.0117             nan     1.0000    0.0085
##      6        0.9997             nan     1.0000   -0.0059
##      7        0.9865             nan     1.0000   -0.0047
##      8        0.9620             nan     1.0000    0.0026
##      9        0.9026             nan     1.0000    0.0247
##     10        0.8840             nan     1.0000   -0.0002
##     20        0.8315             nan     1.0000   -0.0129
##     40        0.8256             nan     1.0000    0.0069
##     60        0.7621             nan     1.0000   -0.0153
##     80        0.7652             nan     1.0000   -0.0068
##    100        0.7514             nan     1.0000    0.0101
##    120        0.7268             nan     1.0000   -0.0047
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1324             nan     1.0000    0.0643
##      2        1.0728             nan     1.0000    0.0179
##      3        1.0251             nan     1.0000   -0.0094
##      4        1.0458             nan     1.0000   -0.0477
##      5        1.0246             nan     1.0000   -0.0071
##      6        0.9984             nan     1.0000    0.0062
##      7        0.9781             nan     1.0000   -0.0074
##      8        0.9578             nan     1.0000   -0.0105
##      9        0.9466             nan     1.0000   -0.0024
##     10        0.9483             nan     1.0000   -0.0243
##     20        1.5387             nan     1.0000   -0.1960
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0801             nan     1.0000    0.0999
##      2        0.9971             nan     1.0000    0.0121
##      3        0.9763             nan     1.0000   -0.0118
##      4        1.0037             nan     1.0000   -0.0644
##      5        0.9584             nan     1.0000    0.0263
##      6        0.9501             nan     1.0000   -0.0328
##      7        0.9569             nan     1.0000   -0.0397
##      8        0.9620             nan     1.0000   -0.0375
##      9        0.9547             nan     1.0000   -0.0245
##     10        1.0646             nan     1.0000   -0.1439
##     20       29.7114             nan     1.0000   -0.1214
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1073             nan     1.0000    0.0800
##      2        1.0199             nan     1.0000    0.0187
##      3        1.0103             nan     1.0000   -0.0241
##      4        1.0190             nan     1.0000   -0.0445
##      5        1.1857             nan     1.0000   -0.1472
##      6           inf             nan     1.0000      -inf
##      7           inf             nan     1.0000       nan
##      8           inf             nan     1.0000       nan
##      9           inf             nan     1.0000       nan
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0896             nan     1.0000    0.0876
##      2        1.0367             nan     1.0000   -0.0245
##      3        1.0463             nan     1.0000   -0.0789
##      4        0.9798             nan     1.0000    0.0273
##      5        0.9506             nan     1.0000   -0.0079
##      6        0.9481             nan     1.0000   -0.0297
##      7        0.9343             nan     1.0000   -0.0003
##      8        0.9743             nan     1.0000   -0.0513
##      9        0.9404             nan     1.0000   -0.0035
##     10        0.9456             nan     1.0000   -0.0386
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0324             nan     1.0000    0.1099
##      2        1.0147             nan     1.0000   -0.0361
##      3        0.9708             nan     1.0000   -0.0225
##      4        0.9440             nan     1.0000   -0.0286
##      5        1.0126             nan     1.0000   -0.1250
##      6        1.0176             nan     1.0000   -0.0675
##      7        1.0878             nan     1.0000   -0.0920
##      8        1.0322             nan     1.0000    0.0017
##      9        0.9791             nan     1.0000   -0.0146
##     10        1.0753             nan     1.0000   -0.1616
##     20           inf             nan     1.0000      -inf
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0538             nan     1.0000    0.0823
##      2        0.9939             nan     1.0000    0.0051
##      3        1.0341             nan     1.0000   -0.0799
##      4        0.9595             nan     1.0000    0.0211
##      5        0.9261             nan     1.0000   -0.0333
##      6        0.9251             nan     1.0000   -0.0403
##      7        0.8984             nan     1.0000   -0.0275
##      8        0.9092             nan     1.0000   -0.0513
##      9        0.8699             nan     1.0000    0.0005
##     10        0.8431             nan     1.0000   -0.0147
##     20        1.0379             nan     1.0000    0.0015
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0474             nan     1.0000    0.1115
##      2        1.0020             nan     1.0000   -0.0137
##      3        0.9494             nan     1.0000   -0.0013
##      4        0.9260             nan     1.0000   -0.0355
##      5        0.9117             nan     1.0000   -0.0268
##      6        0.8790             nan     1.0000    0.0011
##      7        0.9074             nan     1.0000   -0.0669
##      8        0.8681             nan     1.0000    0.0079
##      9        0.8885             nan     1.0000   -0.0479
##     10        0.8704             nan     1.0000   -0.0182
##     20        0.8318             nan     1.0000    0.0294
##     40   221496.8629             nan     1.0000   -1.2187
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2939             nan     0.0010    0.0002
##      3        1.2935             nan     0.0010    0.0002
##      4        1.2932             nan     0.0010    0.0002
##      5        1.2929             nan     0.0010    0.0002
##      6        1.2925             nan     0.0010    0.0002
##      7        1.2922             nan     0.0010    0.0002
##      8        1.2918             nan     0.0010    0.0001
##      9        1.2915             nan     0.0010    0.0002
##     10        1.2911             nan     0.0010    0.0002
##     20        1.2877             nan     0.0010    0.0002
##     40        1.2809             nan     0.0010    0.0002
##     60        1.2744             nan     0.0010    0.0001
##     80        1.2681             nan     0.0010    0.0001
##    100        1.2622             nan     0.0010    0.0001
##    120        1.2562             nan     0.0010    0.0001
##    140        1.2505             nan     0.0010    0.0001
##    160        1.2451             nan     0.0010    0.0001
##    180        1.2396             nan     0.0010    0.0001
##    200        1.2345             nan     0.0010    0.0001
##    220        1.2294             nan     0.0010    0.0001
##    240        1.2244             nan     0.0010    0.0001
##    260        1.2197             nan     0.0010    0.0001
##    280        1.2151             nan     0.0010    0.0001
##    300        1.2106             nan     0.0010    0.0001
##    320        1.2063             nan     0.0010    0.0001
##    340        1.2020             nan     0.0010    0.0001
##    360        1.1978             nan     0.0010    0.0001
##    380        1.1938             nan     0.0010    0.0001
##    400        1.1899             nan     0.0010    0.0001
##    420        1.1861             nan     0.0010    0.0001
##    440        1.1822             nan     0.0010    0.0001
##    460        1.1786             nan     0.0010    0.0001
##    480        1.1748             nan     0.0010    0.0001
##    500        1.1713             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2939             nan     0.0010    0.0002
##      3        1.2935             nan     0.0010    0.0002
##      4        1.2932             nan     0.0010    0.0002
##      5        1.2928             nan     0.0010    0.0001
##      6        1.2925             nan     0.0010    0.0001
##      7        1.2921             nan     0.0010    0.0002
##      8        1.2918             nan     0.0010    0.0002
##      9        1.2914             nan     0.0010    0.0002
##     10        1.2911             nan     0.0010    0.0002
##     20        1.2874             nan     0.0010    0.0002
##     40        1.2807             nan     0.0010    0.0002
##     60        1.2741             nan     0.0010    0.0001
##     80        1.2679             nan     0.0010    0.0001
##    100        1.2618             nan     0.0010    0.0001
##    120        1.2560             nan     0.0010    0.0001
##    140        1.2501             nan     0.0010    0.0001
##    160        1.2447             nan     0.0010    0.0001
##    180        1.2393             nan     0.0010    0.0001
##    200        1.2341             nan     0.0010    0.0001
##    220        1.2290             nan     0.0010    0.0001
##    240        1.2242             nan     0.0010    0.0001
##    260        1.2197             nan     0.0010    0.0001
##    280        1.2150             nan     0.0010    0.0001
##    300        1.2108             nan     0.0010    0.0001
##    320        1.2065             nan     0.0010    0.0001
##    340        1.2022             nan     0.0010    0.0001
##    360        1.1980             nan     0.0010    0.0001
##    380        1.1938             nan     0.0010    0.0001
##    400        1.1897             nan     0.0010    0.0001
##    420        1.1859             nan     0.0010    0.0001
##    440        1.1822             nan     0.0010    0.0001
##    460        1.1784             nan     0.0010    0.0001
##    480        1.1748             nan     0.0010    0.0001
##    500        1.1712             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2939             nan     0.0010    0.0002
##      3        1.2935             nan     0.0010    0.0002
##      4        1.2931             nan     0.0010    0.0001
##      5        1.2927             nan     0.0010    0.0002
##      6        1.2923             nan     0.0010    0.0002
##      7        1.2919             nan     0.0010    0.0002
##      8        1.2916             nan     0.0010    0.0002
##      9        1.2912             nan     0.0010    0.0002
##     10        1.2909             nan     0.0010    0.0001
##     20        1.2874             nan     0.0010    0.0002
##     40        1.2809             nan     0.0010    0.0002
##     60        1.2747             nan     0.0010    0.0001
##     80        1.2683             nan     0.0010    0.0001
##    100        1.2624             nan     0.0010    0.0001
##    120        1.2567             nan     0.0010    0.0001
##    140        1.2510             nan     0.0010    0.0001
##    160        1.2456             nan     0.0010    0.0001
##    180        1.2402             nan     0.0010    0.0001
##    200        1.2351             nan     0.0010    0.0001
##    220        1.2300             nan     0.0010    0.0001
##    240        1.2251             nan     0.0010    0.0001
##    260        1.2202             nan     0.0010    0.0001
##    280        1.2156             nan     0.0010    0.0001
##    300        1.2111             nan     0.0010    0.0001
##    320        1.2068             nan     0.0010    0.0001
##    340        1.2025             nan     0.0010    0.0001
##    360        1.1983             nan     0.0010    0.0001
##    380        1.1943             nan     0.0010    0.0001
##    400        1.1902             nan     0.0010    0.0001
##    420        1.1862             nan     0.0010    0.0001
##    440        1.1824             nan     0.0010    0.0001
##    460        1.1787             nan     0.0010    0.0001
##    480        1.1751             nan     0.0010    0.0001
##    500        1.1714             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2927             nan     0.0010    0.0002
##      5        1.2923             nan     0.0010    0.0002
##      6        1.2918             nan     0.0010    0.0002
##      7        1.2913             nan     0.0010    0.0002
##      8        1.2908             nan     0.0010    0.0002
##      9        1.2903             nan     0.0010    0.0002
##     10        1.2899             nan     0.0010    0.0002
##     20        1.2854             nan     0.0010    0.0002
##     40        1.2766             nan     0.0010    0.0002
##     60        1.2681             nan     0.0010    0.0002
##     80        1.2596             nan     0.0010    0.0002
##    100        1.2515             nan     0.0010    0.0001
##    120        1.2438             nan     0.0010    0.0002
##    140        1.2363             nan     0.0010    0.0002
##    160        1.2288             nan     0.0010    0.0001
##    180        1.2217             nan     0.0010    0.0001
##    200        1.2147             nan     0.0010    0.0002
##    220        1.2079             nan     0.0010    0.0001
##    240        1.2016             nan     0.0010    0.0001
##    260        1.1950             nan     0.0010    0.0001
##    280        1.1886             nan     0.0010    0.0001
##    300        1.1825             nan     0.0010    0.0001
##    320        1.1765             nan     0.0010    0.0001
##    340        1.1709             nan     0.0010    0.0001
##    360        1.1655             nan     0.0010    0.0001
##    380        1.1600             nan     0.0010    0.0001
##    400        1.1547             nan     0.0010    0.0001
##    420        1.1495             nan     0.0010    0.0001
##    440        1.1442             nan     0.0010    0.0001
##    460        1.1392             nan     0.0010    0.0001
##    480        1.1344             nan     0.0010    0.0001
##    500        1.1295             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2922             nan     0.0010    0.0002
##      6        1.2917             nan     0.0010    0.0002
##      7        1.2913             nan     0.0010    0.0002
##      8        1.2908             nan     0.0010    0.0002
##      9        1.2903             nan     0.0010    0.0002
##     10        1.2899             nan     0.0010    0.0002
##     20        1.2854             nan     0.0010    0.0001
##     40        1.2766             nan     0.0010    0.0002
##     60        1.2680             nan     0.0010    0.0002
##     80        1.2598             nan     0.0010    0.0002
##    100        1.2518             nan     0.0010    0.0001
##    120        1.2440             nan     0.0010    0.0002
##    140        1.2366             nan     0.0010    0.0002
##    160        1.2293             nan     0.0010    0.0002
##    180        1.2221             nan     0.0010    0.0002
##    200        1.2153             nan     0.0010    0.0001
##    220        1.2085             nan     0.0010    0.0001
##    240        1.2020             nan     0.0010    0.0002
##    260        1.1959             nan     0.0010    0.0001
##    280        1.1899             nan     0.0010    0.0001
##    300        1.1837             nan     0.0010    0.0001
##    320        1.1778             nan     0.0010    0.0001
##    340        1.1719             nan     0.0010    0.0001
##    360        1.1664             nan     0.0010    0.0001
##    380        1.1608             nan     0.0010    0.0001
##    400        1.1554             nan     0.0010    0.0001
##    420        1.1502             nan     0.0010    0.0001
##    440        1.1452             nan     0.0010    0.0001
##    460        1.1403             nan     0.0010    0.0001
##    480        1.1354             nan     0.0010    0.0001
##    500        1.1305             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2932             nan     0.0010    0.0002
##      4        1.2928             nan     0.0010    0.0002
##      5        1.2923             nan     0.0010    0.0002
##      6        1.2918             nan     0.0010    0.0002
##      7        1.2913             nan     0.0010    0.0002
##      8        1.2908             nan     0.0010    0.0002
##      9        1.2904             nan     0.0010    0.0002
##     10        1.2900             nan     0.0010    0.0002
##     20        1.2854             nan     0.0010    0.0002
##     40        1.2764             nan     0.0010    0.0002
##     60        1.2679             nan     0.0010    0.0002
##     80        1.2595             nan     0.0010    0.0002
##    100        1.2516             nan     0.0010    0.0002
##    120        1.2440             nan     0.0010    0.0002
##    140        1.2364             nan     0.0010    0.0001
##    160        1.2292             nan     0.0010    0.0001
##    180        1.2221             nan     0.0010    0.0002
##    200        1.2151             nan     0.0010    0.0002
##    220        1.2082             nan     0.0010    0.0001
##    240        1.2017             nan     0.0010    0.0001
##    260        1.1953             nan     0.0010    0.0001
##    280        1.1888             nan     0.0010    0.0001
##    300        1.1829             nan     0.0010    0.0001
##    320        1.1769             nan     0.0010    0.0001
##    340        1.1712             nan     0.0010    0.0001
##    360        1.1657             nan     0.0010    0.0001
##    380        1.1602             nan     0.0010    0.0001
##    400        1.1550             nan     0.0010    0.0001
##    420        1.1498             nan     0.0010    0.0001
##    440        1.1448             nan     0.0010    0.0001
##    460        1.1400             nan     0.0010    0.0001
##    480        1.1351             nan     0.0010    0.0001
##    500        1.1304             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2930             nan     0.0010    0.0003
##      4        1.2924             nan     0.0010    0.0003
##      5        1.2919             nan     0.0010    0.0002
##      6        1.2914             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2898             nan     0.0010    0.0002
##     10        1.2893             nan     0.0010    0.0003
##     20        1.2838             nan     0.0010    0.0003
##     40        1.2734             nan     0.0010    0.0002
##     60        1.2636             nan     0.0010    0.0002
##     80        1.2538             nan     0.0010    0.0002
##    100        1.2444             nan     0.0010    0.0002
##    120        1.2352             nan     0.0010    0.0002
##    140        1.2262             nan     0.0010    0.0002
##    160        1.2177             nan     0.0010    0.0002
##    180        1.2098             nan     0.0010    0.0002
##    200        1.2019             nan     0.0010    0.0002
##    220        1.1943             nan     0.0010    0.0002
##    240        1.1869             nan     0.0010    0.0002
##    260        1.1796             nan     0.0010    0.0002
##    280        1.1725             nan     0.0010    0.0001
##    300        1.1654             nan     0.0010    0.0002
##    320        1.1586             nan     0.0010    0.0001
##    340        1.1521             nan     0.0010    0.0001
##    360        1.1458             nan     0.0010    0.0001
##    380        1.1396             nan     0.0010    0.0001
##    400        1.1335             nan     0.0010    0.0001
##    420        1.1273             nan     0.0010    0.0001
##    440        1.1217             nan     0.0010    0.0001
##    460        1.1161             nan     0.0010    0.0001
##    480        1.1108             nan     0.0010    0.0001
##    500        1.1056             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0003
##      3        1.2929             nan     0.0010    0.0002
##      4        1.2924             nan     0.0010    0.0002
##      5        1.2919             nan     0.0010    0.0002
##      6        1.2915             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2898             nan     0.0010    0.0002
##     10        1.2892             nan     0.0010    0.0003
##     20        1.2840             nan     0.0010    0.0003
##     40        1.2738             nan     0.0010    0.0002
##     60        1.2640             nan     0.0010    0.0002
##     80        1.2543             nan     0.0010    0.0003
##    100        1.2449             nan     0.0010    0.0002
##    120        1.2357             nan     0.0010    0.0002
##    140        1.2270             nan     0.0010    0.0002
##    160        1.2186             nan     0.0010    0.0002
##    180        1.2105             nan     0.0010    0.0001
##    200        1.2024             nan     0.0010    0.0002
##    220        1.1946             nan     0.0010    0.0002
##    240        1.1870             nan     0.0010    0.0001
##    260        1.1797             nan     0.0010    0.0002
##    280        1.1726             nan     0.0010    0.0002
##    300        1.1658             nan     0.0010    0.0001
##    320        1.1591             nan     0.0010    0.0001
##    340        1.1526             nan     0.0010    0.0001
##    360        1.1464             nan     0.0010    0.0001
##    380        1.1399             nan     0.0010    0.0001
##    400        1.1337             nan     0.0010    0.0001
##    420        1.1276             nan     0.0010    0.0001
##    440        1.1221             nan     0.0010    0.0001
##    460        1.1163             nan     0.0010    0.0001
##    480        1.1109             nan     0.0010    0.0001
##    500        1.1054             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2929             nan     0.0010    0.0002
##      4        1.2923             nan     0.0010    0.0002
##      5        1.2918             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2902             nan     0.0010    0.0003
##      9        1.2896             nan     0.0010    0.0002
##     10        1.2891             nan     0.0010    0.0002
##     20        1.2838             nan     0.0010    0.0002
##     40        1.2735             nan     0.0010    0.0002
##     60        1.2634             nan     0.0010    0.0002
##     80        1.2541             nan     0.0010    0.0002
##    100        1.2446             nan     0.0010    0.0002
##    120        1.2355             nan     0.0010    0.0002
##    140        1.2266             nan     0.0010    0.0002
##    160        1.2183             nan     0.0010    0.0002
##    180        1.2101             nan     0.0010    0.0002
##    200        1.2021             nan     0.0010    0.0002
##    220        1.1944             nan     0.0010    0.0001
##    240        1.1870             nan     0.0010    0.0002
##    260        1.1797             nan     0.0010    0.0002
##    280        1.1724             nan     0.0010    0.0001
##    300        1.1658             nan     0.0010    0.0001
##    320        1.1589             nan     0.0010    0.0002
##    340        1.1525             nan     0.0010    0.0001
##    360        1.1464             nan     0.0010    0.0001
##    380        1.1403             nan     0.0010    0.0001
##    400        1.1342             nan     0.0010    0.0001
##    420        1.1284             nan     0.0010    0.0001
##    440        1.1225             nan     0.0010    0.0001
##    460        1.1171             nan     0.0010    0.0001
##    480        1.1118             nan     0.0010    0.0001
##    500        1.1064             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2618             nan     0.1000    0.0133
##      2        1.2336             nan     0.1000    0.0130
##      3        1.2084             nan     0.1000    0.0103
##      4        1.1918             nan     0.1000    0.0066
##      5        1.1696             nan     0.1000    0.0093
##      6        1.1537             nan     0.1000    0.0084
##      7        1.1370             nan     0.1000    0.0069
##      8        1.1241             nan     0.1000    0.0048
##      9        1.1081             nan     0.1000    0.0075
##     10        1.1004             nan     0.1000    0.0003
##     20        1.0047             nan     0.1000    0.0025
##     40        0.9161             nan     0.1000    0.0011
##     60        0.8842             nan     0.1000   -0.0002
##     80        0.8608             nan     0.1000   -0.0009
##    100        0.8403             nan     0.1000   -0.0001
##    120        0.8265             nan     0.1000   -0.0000
##    140        0.8152             nan     0.1000   -0.0013
##    160        0.8060             nan     0.1000   -0.0015
##    180        0.7970             nan     0.1000   -0.0009
##    200        0.7844             nan     0.1000   -0.0005
##    220        0.7768             nan     0.1000   -0.0004
##    240        0.7721             nan     0.1000   -0.0006
##    260        0.7652             nan     0.1000   -0.0012
##    280        0.7585             nan     0.1000   -0.0011
##    300        0.7532             nan     0.1000   -0.0009
##    320        0.7492             nan     0.1000   -0.0006
##    340        0.7414             nan     0.1000   -0.0015
##    360        0.7355             nan     0.1000   -0.0005
##    380        0.7306             nan     0.1000   -0.0010
##    400        0.7272             nan     0.1000   -0.0010
##    420        0.7240             nan     0.1000   -0.0025
##    440        0.7193             nan     0.1000   -0.0017
##    460        0.7160             nan     0.1000   -0.0010
##    480        0.7120             nan     0.1000   -0.0009
##    500        0.7066             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2648             nan     0.1000    0.0142
##      2        1.2336             nan     0.1000    0.0133
##      3        1.2089             nan     0.1000    0.0103
##      4        1.1910             nan     0.1000    0.0093
##      5        1.1734             nan     0.1000    0.0078
##      6        1.1571             nan     0.1000    0.0071
##      7        1.1428             nan     0.1000    0.0065
##      8        1.1258             nan     0.1000    0.0067
##      9        1.1081             nan     0.1000    0.0049
##     10        1.0948             nan     0.1000    0.0058
##     20        1.0109             nan     0.1000    0.0020
##     40        0.9281             nan     0.1000    0.0009
##     60        0.8901             nan     0.1000   -0.0005
##     80        0.8662             nan     0.1000   -0.0008
##    100        0.8486             nan     0.1000    0.0000
##    120        0.8377             nan     0.1000   -0.0008
##    140        0.8260             nan     0.1000   -0.0005
##    160        0.8180             nan     0.1000   -0.0009
##    180        0.8097             nan     0.1000   -0.0015
##    200        0.8002             nan     0.1000   -0.0010
##    220        0.7917             nan     0.1000   -0.0010
##    240        0.7860             nan     0.1000   -0.0010
##    260        0.7782             nan     0.1000   -0.0009
##    280        0.7709             nan     0.1000   -0.0017
##    300        0.7638             nan     0.1000   -0.0011
##    320        0.7579             nan     0.1000   -0.0009
##    340        0.7505             nan     0.1000   -0.0009
##    360        0.7465             nan     0.1000   -0.0011
##    380        0.7419             nan     0.1000   -0.0007
##    400        0.7351             nan     0.1000   -0.0011
##    420        0.7299             nan     0.1000   -0.0016
##    440        0.7251             nan     0.1000   -0.0012
##    460        0.7226             nan     0.1000   -0.0006
##    480        0.7175             nan     0.1000   -0.0011
##    500        0.7128             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2603             nan     0.1000    0.0158
##      2        1.2326             nan     0.1000    0.0133
##      3        1.2063             nan     0.1000    0.0101
##      4        1.1833             nan     0.1000    0.0072
##      5        1.1645             nan     0.1000    0.0074
##      6        1.1478             nan     0.1000    0.0069
##      7        1.1349             nan     0.1000    0.0056
##      8        1.1217             nan     0.1000    0.0038
##      9        1.1079             nan     0.1000    0.0034
##     10        1.0953             nan     0.1000    0.0057
##     20        1.0118             nan     0.1000    0.0027
##     40        0.9222             nan     0.1000    0.0010
##     60        0.8856             nan     0.1000   -0.0005
##     80        0.8614             nan     0.1000   -0.0010
##    100        0.8428             nan     0.1000    0.0000
##    120        0.8293             nan     0.1000   -0.0003
##    140        0.8174             nan     0.1000   -0.0013
##    160        0.8058             nan     0.1000   -0.0009
##    180        0.7974             nan     0.1000   -0.0004
##    200        0.7921             nan     0.1000   -0.0018
##    220        0.7865             nan     0.1000   -0.0004
##    240        0.7793             nan     0.1000   -0.0003
##    260        0.7728             nan     0.1000   -0.0006
##    280        0.7647             nan     0.1000   -0.0012
##    300        0.7583             nan     0.1000   -0.0009
##    320        0.7519             nan     0.1000   -0.0014
##    340        0.7462             nan     0.1000   -0.0009
##    360        0.7407             nan     0.1000   -0.0005
##    380        0.7369             nan     0.1000   -0.0011
##    400        0.7311             nan     0.1000   -0.0003
##    420        0.7261             nan     0.1000   -0.0006
##    440        0.7195             nan     0.1000   -0.0008
##    460        0.7145             nan     0.1000   -0.0009
##    480        0.7100             nan     0.1000   -0.0009
##    500        0.7061             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2495             nan     0.1000    0.0196
##      2        1.2167             nan     0.1000    0.0134
##      3        1.1838             nan     0.1000    0.0146
##      4        1.1540             nan     0.1000    0.0109
##      5        1.1241             nan     0.1000    0.0113
##      6        1.1013             nan     0.1000    0.0084
##      7        1.0799             nan     0.1000    0.0090
##      8        1.0617             nan     0.1000    0.0085
##      9        1.0466             nan     0.1000    0.0061
##     10        1.0329             nan     0.1000    0.0037
##     20        0.9428             nan     0.1000    0.0022
##     40        0.8613             nan     0.1000   -0.0002
##     60        0.8148             nan     0.1000   -0.0016
##     80        0.7819             nan     0.1000   -0.0019
##    100        0.7550             nan     0.1000   -0.0022
##    120        0.7303             nan     0.1000   -0.0012
##    140        0.7055             nan     0.1000   -0.0013
##    160        0.6861             nan     0.1000   -0.0008
##    180        0.6639             nan     0.1000   -0.0021
##    200        0.6462             nan     0.1000   -0.0003
##    220        0.6283             nan     0.1000   -0.0012
##    240        0.6106             nan     0.1000   -0.0012
##    260        0.5934             nan     0.1000   -0.0013
##    280        0.5800             nan     0.1000   -0.0013
##    300        0.5672             nan     0.1000   -0.0009
##    320        0.5557             nan     0.1000   -0.0015
##    340        0.5426             nan     0.1000   -0.0017
##    360        0.5262             nan     0.1000   -0.0003
##    380        0.5137             nan     0.1000   -0.0009
##    400        0.5016             nan     0.1000   -0.0009
##    420        0.4932             nan     0.1000   -0.0004
##    440        0.4802             nan     0.1000   -0.0008
##    460        0.4682             nan     0.1000   -0.0012
##    480        0.4575             nan     0.1000   -0.0009
##    500        0.4461             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2474             nan     0.1000    0.0203
##      2        1.2093             nan     0.1000    0.0170
##      3        1.1764             nan     0.1000    0.0131
##      4        1.1516             nan     0.1000    0.0122
##      5        1.1249             nan     0.1000    0.0108
##      6        1.1025             nan     0.1000    0.0108
##      7        1.0858             nan     0.1000    0.0058
##      8        1.0674             nan     0.1000    0.0066
##      9        1.0520             nan     0.1000    0.0061
##     10        1.0380             nan     0.1000    0.0051
##     20        0.9359             nan     0.1000    0.0016
##     40        0.8583             nan     0.1000   -0.0014
##     60        0.8096             nan     0.1000   -0.0006
##     80        0.7820             nan     0.1000   -0.0007
##    100        0.7504             nan     0.1000   -0.0007
##    120        0.7313             nan     0.1000   -0.0021
##    140        0.7025             nan     0.1000   -0.0009
##    160        0.6773             nan     0.1000   -0.0005
##    180        0.6581             nan     0.1000   -0.0027
##    200        0.6421             nan     0.1000   -0.0030
##    220        0.6241             nan     0.1000   -0.0007
##    240        0.6082             nan     0.1000   -0.0014
##    260        0.5899             nan     0.1000   -0.0006
##    280        0.5737             nan     0.1000   -0.0013
##    300        0.5583             nan     0.1000   -0.0004
##    320        0.5453             nan     0.1000   -0.0007
##    340        0.5320             nan     0.1000   -0.0027
##    360        0.5181             nan     0.1000   -0.0011
##    380        0.5106             nan     0.1000   -0.0009
##    400        0.4943             nan     0.1000   -0.0012
##    420        0.4802             nan     0.1000   -0.0009
##    440        0.4681             nan     0.1000   -0.0010
##    460        0.4618             nan     0.1000   -0.0018
##    480        0.4492             nan     0.1000   -0.0007
##    500        0.4381             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2459             nan     0.1000    0.0195
##      2        1.2060             nan     0.1000    0.0164
##      3        1.1755             nan     0.1000    0.0124
##      4        1.1494             nan     0.1000    0.0089
##      5        1.1226             nan     0.1000    0.0113
##      6        1.0997             nan     0.1000    0.0088
##      7        1.0809             nan     0.1000    0.0076
##      8        1.0649             nan     0.1000    0.0067
##      9        1.0455             nan     0.1000    0.0059
##     10        1.0302             nan     0.1000    0.0063
##     20        0.9391             nan     0.1000    0.0007
##     40        0.8611             nan     0.1000   -0.0007
##     60        0.8126             nan     0.1000   -0.0007
##     80        0.7805             nan     0.1000   -0.0021
##    100        0.7539             nan     0.1000   -0.0011
##    120        0.7259             nan     0.1000   -0.0017
##    140        0.7026             nan     0.1000   -0.0023
##    160        0.6819             nan     0.1000   -0.0011
##    180        0.6656             nan     0.1000   -0.0005
##    200        0.6444             nan     0.1000   -0.0009
##    220        0.6274             nan     0.1000   -0.0015
##    240        0.6154             nan     0.1000   -0.0013
##    260        0.6002             nan     0.1000   -0.0008
##    280        0.5835             nan     0.1000   -0.0011
##    300        0.5690             nan     0.1000   -0.0021
##    320        0.5567             nan     0.1000   -0.0018
##    340        0.5444             nan     0.1000   -0.0004
##    360        0.5340             nan     0.1000   -0.0007
##    380        0.5232             nan     0.1000   -0.0012
##    400        0.5100             nan     0.1000   -0.0013
##    420        0.4983             nan     0.1000   -0.0015
##    440        0.4876             nan     0.1000   -0.0009
##    460        0.4782             nan     0.1000   -0.0009
##    480        0.4700             nan     0.1000   -0.0006
##    500        0.4586             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2399             nan     0.1000    0.0247
##      2        1.1938             nan     0.1000    0.0201
##      3        1.1611             nan     0.1000    0.0150
##      4        1.1250             nan     0.1000    0.0176
##      5        1.0959             nan     0.1000    0.0105
##      6        1.0740             nan     0.1000    0.0083
##      7        1.0549             nan     0.1000    0.0052
##      8        1.0381             nan     0.1000    0.0059
##      9        1.0195             nan     0.1000    0.0059
##     10        1.0029             nan     0.1000    0.0065
##     20        0.8945             nan     0.1000   -0.0010
##     40        0.8074             nan     0.1000    0.0000
##     60        0.7545             nan     0.1000   -0.0019
##     80        0.7112             nan     0.1000   -0.0015
##    100        0.6757             nan     0.1000   -0.0030
##    120        0.6447             nan     0.1000   -0.0030
##    140        0.6114             nan     0.1000   -0.0023
##    160        0.5836             nan     0.1000   -0.0005
##    180        0.5538             nan     0.1000   -0.0003
##    200        0.5316             nan     0.1000   -0.0021
##    220        0.5082             nan     0.1000   -0.0022
##    240        0.4851             nan     0.1000   -0.0001
##    260        0.4642             nan     0.1000   -0.0008
##    280        0.4461             nan     0.1000   -0.0003
##    300        0.4266             nan     0.1000   -0.0015
##    320        0.4138             nan     0.1000   -0.0020
##    340        0.3964             nan     0.1000   -0.0010
##    360        0.3806             nan     0.1000   -0.0008
##    380        0.3666             nan     0.1000   -0.0004
##    400        0.3512             nan     0.1000   -0.0007
##    420        0.3381             nan     0.1000   -0.0007
##    440        0.3257             nan     0.1000   -0.0012
##    460        0.3126             nan     0.1000   -0.0008
##    480        0.3020             nan     0.1000   -0.0009
##    500        0.2926             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2415             nan     0.1000    0.0235
##      2        1.1948             nan     0.1000    0.0207
##      3        1.1574             nan     0.1000    0.0136
##      4        1.1231             nan     0.1000    0.0138
##      5        1.0948             nan     0.1000    0.0111
##      6        1.0713             nan     0.1000    0.0069
##      7        1.0519             nan     0.1000    0.0064
##      8        1.0327             nan     0.1000    0.0073
##      9        1.0137             nan     0.1000    0.0080
##     10        0.9958             nan     0.1000    0.0051
##     20        0.8932             nan     0.1000    0.0013
##     40        0.8038             nan     0.1000   -0.0020
##     60        0.7550             nan     0.1000   -0.0031
##     80        0.7168             nan     0.1000   -0.0018
##    100        0.6780             nan     0.1000   -0.0008
##    120        0.6449             nan     0.1000   -0.0012
##    140        0.6120             nan     0.1000   -0.0020
##    160        0.5867             nan     0.1000   -0.0015
##    180        0.5626             nan     0.1000   -0.0010
##    200        0.5360             nan     0.1000   -0.0008
##    220        0.5161             nan     0.1000   -0.0010
##    240        0.4957             nan     0.1000   -0.0021
##    260        0.4719             nan     0.1000   -0.0017
##    280        0.4564             nan     0.1000   -0.0010
##    300        0.4377             nan     0.1000   -0.0008
##    320        0.4193             nan     0.1000   -0.0010
##    340        0.4047             nan     0.1000   -0.0012
##    360        0.3907             nan     0.1000   -0.0009
##    380        0.3769             nan     0.1000   -0.0008
##    400        0.3645             nan     0.1000   -0.0007
##    420        0.3499             nan     0.1000   -0.0003
##    440        0.3389             nan     0.1000   -0.0012
##    460        0.3256             nan     0.1000   -0.0012
##    480        0.3153             nan     0.1000   -0.0009
##    500        0.3026             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2422             nan     0.1000    0.0234
##      2        1.1920             nan     0.1000    0.0209
##      3        1.1598             nan     0.1000    0.0134
##      4        1.1257             nan     0.1000    0.0129
##      5        1.1006             nan     0.1000    0.0100
##      6        1.0762             nan     0.1000    0.0103
##      7        1.0576             nan     0.1000    0.0067
##      8        1.0321             nan     0.1000    0.0077
##      9        1.0123             nan     0.1000    0.0080
##     10        0.9948             nan     0.1000    0.0054
##     20        0.8963             nan     0.1000    0.0005
##     40        0.8016             nan     0.1000   -0.0006
##     60        0.7492             nan     0.1000   -0.0018
##     80        0.7094             nan     0.1000   -0.0018
##    100        0.6679             nan     0.1000   -0.0004
##    120        0.6347             nan     0.1000   -0.0020
##    140        0.6067             nan     0.1000   -0.0019
##    160        0.5797             nan     0.1000   -0.0013
##    180        0.5538             nan     0.1000   -0.0008
##    200        0.5298             nan     0.1000   -0.0009
##    220        0.5027             nan     0.1000   -0.0006
##    240        0.4853             nan     0.1000   -0.0006
##    260        0.4644             nan     0.1000   -0.0017
##    280        0.4478             nan     0.1000   -0.0003
##    300        0.4304             nan     0.1000   -0.0008
##    320        0.4137             nan     0.1000   -0.0004
##    340        0.3950             nan     0.1000   -0.0011
##    360        0.3765             nan     0.1000   -0.0006
##    380        0.3610             nan     0.1000   -0.0005
##    400        0.3479             nan     0.1000   -0.0008
##    420        0.3347             nan     0.1000   -0.0004
##    440        0.3258             nan     0.1000   -0.0007
##    460        0.3138             nan     0.1000   -0.0005
##    480        0.3014             nan     0.1000   -0.0010
##    500        0.2912             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2270             nan     0.2000    0.0305
##      2        1.1838             nan     0.2000    0.0161
##      3        1.1447             nan     0.2000    0.0133
##      4        1.1095             nan     0.2000    0.0130
##      5        1.0875             nan     0.2000    0.0111
##      6        1.0680             nan     0.2000    0.0074
##      7        1.0514             nan     0.2000    0.0059
##      8        1.0335             nan     0.2000    0.0062
##      9        1.0252             nan     0.2000    0.0009
##     10        1.0109             nan     0.2000    0.0059
##     20        0.9208             nan     0.2000    0.0001
##     40        0.8652             nan     0.2000   -0.0020
##     60        0.8333             nan     0.2000   -0.0011
##     80        0.8132             nan     0.2000   -0.0017
##    100        0.7913             nan     0.2000   -0.0014
##    120        0.7788             nan     0.2000   -0.0025
##    140        0.7639             nan     0.2000   -0.0015
##    160        0.7471             nan     0.2000   -0.0012
##    180        0.7375             nan     0.2000   -0.0016
##    200        0.7247             nan     0.2000   -0.0044
##    220        0.7157             nan     0.2000   -0.0010
##    240        0.7080             nan     0.2000   -0.0021
##    260        0.7013             nan     0.2000   -0.0038
##    280        0.6971             nan     0.2000   -0.0014
##    300        0.6888             nan     0.2000   -0.0026
##    320        0.6829             nan     0.2000   -0.0014
##    340        0.6758             nan     0.2000   -0.0024
##    360        0.6663             nan     0.2000   -0.0010
##    380        0.6595             nan     0.2000   -0.0013
##    400        0.6525             nan     0.2000   -0.0020
##    420        0.6452             nan     0.2000   -0.0012
##    440        0.6398             nan     0.2000   -0.0025
##    460        0.6333             nan     0.2000   -0.0016
##    480        0.6284             nan     0.2000   -0.0016
##    500        0.6199             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2311             nan     0.2000    0.0261
##      2        1.1727             nan     0.2000    0.0201
##      3        1.1384             nan     0.2000    0.0109
##      4        1.1085             nan     0.2000    0.0141
##      5        1.0841             nan     0.2000    0.0110
##      6        1.0598             nan     0.2000    0.0072
##      7        1.0395             nan     0.2000    0.0081
##      8        1.0252             nan     0.2000    0.0054
##      9        1.0099             nan     0.2000    0.0052
##     10        0.9950             nan     0.2000    0.0024
##     20        0.9204             nan     0.2000   -0.0014
##     40        0.8635             nan     0.2000   -0.0014
##     60        0.8247             nan     0.2000   -0.0035
##     80        0.7998             nan     0.2000   -0.0015
##    100        0.7886             nan     0.2000   -0.0020
##    120        0.7756             nan     0.2000   -0.0023
##    140        0.7595             nan     0.2000   -0.0021
##    160        0.7491             nan     0.2000   -0.0008
##    180        0.7414             nan     0.2000   -0.0037
##    200        0.7284             nan     0.2000   -0.0016
##    220        0.7185             nan     0.2000   -0.0014
##    240        0.7093             nan     0.2000   -0.0007
##    260        0.6977             nan     0.2000   -0.0029
##    280        0.6913             nan     0.2000   -0.0013
##    300        0.6839             nan     0.2000   -0.0011
##    320        0.6766             nan     0.2000   -0.0016
##    340        0.6672             nan     0.2000   -0.0014
##    360        0.6626             nan     0.2000   -0.0012
##    380        0.6543             nan     0.2000   -0.0021
##    400        0.6465             nan     0.2000   -0.0014
##    420        0.6451             nan     0.2000   -0.0013
##    440        0.6396             nan     0.2000   -0.0028
##    460        0.6320             nan     0.2000   -0.0015
##    480        0.6270             nan     0.2000   -0.0012
##    500        0.6227             nan     0.2000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2221             nan     0.2000    0.0243
##      2        1.1855             nan     0.2000    0.0161
##      3        1.1530             nan     0.2000    0.0150
##      4        1.1177             nan     0.2000    0.0163
##      5        1.0936             nan     0.2000    0.0112
##      6        1.0710             nan     0.2000    0.0078
##      7        1.0491             nan     0.2000    0.0086
##      8        1.0317             nan     0.2000    0.0073
##      9        1.0169             nan     0.2000    0.0048
##     10        1.0022             nan     0.2000    0.0051
##     20        0.9330             nan     0.2000   -0.0011
##     40        0.8672             nan     0.2000   -0.0010
##     60        0.8455             nan     0.2000   -0.0018
##     80        0.8223             nan     0.2000   -0.0011
##    100        0.8010             nan     0.2000   -0.0027
##    120        0.7878             nan     0.2000   -0.0017
##    140        0.7753             nan     0.2000   -0.0024
##    160        0.7635             nan     0.2000   -0.0004
##    180        0.7491             nan     0.2000   -0.0026
##    200        0.7372             nan     0.2000   -0.0012
##    220        0.7262             nan     0.2000   -0.0014
##    240        0.7203             nan     0.2000   -0.0021
##    260        0.7084             nan     0.2000   -0.0028
##    280        0.7006             nan     0.2000   -0.0021
##    300        0.6912             nan     0.2000   -0.0008
##    320        0.6838             nan     0.2000   -0.0005
##    340        0.6764             nan     0.2000   -0.0013
##    360        0.6657             nan     0.2000   -0.0010
##    380        0.6609             nan     0.2000   -0.0009
##    400        0.6554             nan     0.2000   -0.0023
##    420        0.6458             nan     0.2000   -0.0003
##    440        0.6420             nan     0.2000   -0.0028
##    460        0.6327             nan     0.2000   -0.0014
##    480        0.6292             nan     0.2000   -0.0010
##    500        0.6275             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2207             nan     0.2000    0.0353
##      2        1.1654             nan     0.2000    0.0283
##      3        1.1276             nan     0.2000    0.0145
##      4        1.0857             nan     0.2000    0.0104
##      5        1.0489             nan     0.2000    0.0134
##      6        1.0198             nan     0.2000    0.0127
##      7        1.0003             nan     0.2000    0.0058
##      8        0.9795             nan     0.2000    0.0067
##      9        0.9628             nan     0.2000    0.0054
##     10        0.9493             nan     0.2000    0.0035
##     20        0.8646             nan     0.2000   -0.0056
##     40        0.7808             nan     0.2000   -0.0016
##     60        0.7246             nan     0.2000   -0.0081
##     80        0.6830             nan     0.2000   -0.0014
##    100        0.6438             nan     0.2000   -0.0037
##    120        0.6052             nan     0.2000   -0.0046
##    140        0.5788             nan     0.2000   -0.0047
##    160        0.5484             nan     0.2000   -0.0015
##    180        0.5164             nan     0.2000   -0.0047
##    200        0.4921             nan     0.2000   -0.0021
##    220        0.4720             nan     0.2000   -0.0010
##    240        0.4517             nan     0.2000   -0.0016
##    260        0.4285             nan     0.2000   -0.0018
##    280        0.4111             nan     0.2000   -0.0015
##    300        0.3892             nan     0.2000   -0.0004
##    320        0.3722             nan     0.2000   -0.0002
##    340        0.3519             nan     0.2000   -0.0027
##    360        0.3366             nan     0.2000   -0.0009
##    380        0.3241             nan     0.2000   -0.0014
##    400        0.3143             nan     0.2000   -0.0018
##    420        0.3033             nan     0.2000   -0.0031
##    440        0.2927             nan     0.2000   -0.0005
##    460        0.2825             nan     0.2000   -0.0018
##    480        0.2718             nan     0.2000   -0.0010
##    500        0.2638             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2164             nan     0.2000    0.0420
##      2        1.1483             nan     0.2000    0.0318
##      3        1.1008             nan     0.2000    0.0186
##      4        1.0597             nan     0.2000    0.0144
##      5        1.0247             nan     0.2000    0.0119
##      6        0.9948             nan     0.2000    0.0055
##      7        0.9769             nan     0.2000    0.0042
##      8        0.9584             nan     0.2000    0.0059
##      9        0.9412             nan     0.2000    0.0032
##     10        0.9323             nan     0.2000   -0.0006
##     20        0.8573             nan     0.2000    0.0001
##     40        0.7813             nan     0.2000   -0.0019
##     60        0.7332             nan     0.2000   -0.0020
##     80        0.6895             nan     0.2000   -0.0028
##    100        0.6538             nan     0.2000   -0.0023
##    120        0.6130             nan     0.2000   -0.0001
##    140        0.5833             nan     0.2000   -0.0000
##    160        0.5497             nan     0.2000   -0.0014
##    180        0.5285             nan     0.2000   -0.0016
##    200        0.5053             nan     0.2000   -0.0016
##    220        0.4865             nan     0.2000   -0.0013
##    240        0.4617             nan     0.2000   -0.0018
##    260        0.4410             nan     0.2000   -0.0004
##    280        0.4246             nan     0.2000   -0.0024
##    300        0.4061             nan     0.2000   -0.0029
##    320        0.3874             nan     0.2000    0.0000
##    340        0.3722             nan     0.2000   -0.0016
##    360        0.3536             nan     0.2000   -0.0003
##    380        0.3429             nan     0.2000   -0.0014
##    400        0.3325             nan     0.2000   -0.0023
##    420        0.3219             nan     0.2000   -0.0013
##    440        0.3090             nan     0.2000   -0.0014
##    460        0.2996             nan     0.2000   -0.0013
##    480        0.2860             nan     0.2000   -0.0032
##    500        0.2773             nan     0.2000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2096             nan     0.2000    0.0370
##      2        1.1483             nan     0.2000    0.0292
##      3        1.1054             nan     0.2000    0.0158
##      4        1.0696             nan     0.2000    0.0120
##      5        1.0348             nan     0.2000    0.0118
##      6        1.0046             nan     0.2000    0.0111
##      7        0.9856             nan     0.2000    0.0069
##      8        0.9704             nan     0.2000    0.0043
##      9        0.9572             nan     0.2000    0.0029
##     10        0.9479             nan     0.2000    0.0022
##     20        0.8582             nan     0.2000   -0.0014
##     40        0.7792             nan     0.2000   -0.0019
##     60        0.7350             nan     0.2000   -0.0036
##     80        0.6840             nan     0.2000   -0.0021
##    100        0.6472             nan     0.2000   -0.0015
##    120        0.6115             nan     0.2000    0.0000
##    140        0.5769             nan     0.2000   -0.0005
##    160        0.5472             nan     0.2000   -0.0009
##    180        0.5232             nan     0.2000   -0.0009
##    200        0.4967             nan     0.2000   -0.0005
##    220        0.4770             nan     0.2000   -0.0012
##    240        0.4577             nan     0.2000   -0.0018
##    260        0.4380             nan     0.2000   -0.0015
##    280        0.4204             nan     0.2000   -0.0025
##    300        0.4028             nan     0.2000   -0.0012
##    320        0.3909             nan     0.2000   -0.0023
##    340        0.3762             nan     0.2000   -0.0005
##    360        0.3597             nan     0.2000   -0.0008
##    380        0.3471             nan     0.2000   -0.0009
##    400        0.3338             nan     0.2000   -0.0009
##    420        0.3237             nan     0.2000   -0.0005
##    440        0.3084             nan     0.2000   -0.0010
##    460        0.2960             nan     0.2000   -0.0012
##    480        0.2825             nan     0.2000   -0.0005
##    500        0.2698             nan     0.2000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2086             nan     0.2000    0.0356
##      2        1.1268             nan     0.2000    0.0359
##      3        1.0815             nan     0.2000    0.0188
##      4        1.0334             nan     0.2000    0.0160
##      5        1.0012             nan     0.2000    0.0099
##      6        0.9785             nan     0.2000    0.0043
##      7        0.9688             nan     0.2000   -0.0033
##      8        0.9488             nan     0.2000    0.0018
##      9        0.9325             nan     0.2000    0.0018
##     10        0.9165             nan     0.2000    0.0029
##     20        0.8225             nan     0.2000    0.0016
##     40        0.7248             nan     0.2000   -0.0021
##     60        0.6609             nan     0.2000   -0.0033
##     80        0.6001             nan     0.2000    0.0001
##    100        0.5492             nan     0.2000   -0.0009
##    120        0.5014             nan     0.2000   -0.0009
##    140        0.4595             nan     0.2000   -0.0032
##    160        0.4240             nan     0.2000   -0.0019
##    180        0.3929             nan     0.2000   -0.0017
##    200        0.3633             nan     0.2000   -0.0013
##    220        0.3398             nan     0.2000   -0.0038
##    240        0.3150             nan     0.2000   -0.0020
##    260        0.2935             nan     0.2000   -0.0013
##    280        0.2707             nan     0.2000   -0.0013
##    300        0.2517             nan     0.2000   -0.0017
##    320        0.2364             nan     0.2000   -0.0015
##    340        0.2204             nan     0.2000   -0.0009
##    360        0.2069             nan     0.2000   -0.0011
##    380        0.1956             nan     0.2000   -0.0007
##    400        0.1851             nan     0.2000   -0.0008
##    420        0.1722             nan     0.2000   -0.0010
##    440        0.1609             nan     0.2000   -0.0006
##    460        0.1522             nan     0.2000   -0.0004
##    480        0.1460             nan     0.2000   -0.0006
##    500        0.1379             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1910             nan     0.2000    0.0473
##      2        1.1085             nan     0.2000    0.0326
##      3        1.0547             nan     0.2000    0.0234
##      4        1.0162             nan     0.2000    0.0127
##      5        0.9854             nan     0.2000    0.0087
##      6        0.9647             nan     0.2000    0.0027
##      7        0.9471             nan     0.2000    0.0026
##      8        0.9276             nan     0.2000    0.0034
##      9        0.9110             nan     0.2000    0.0038
##     10        0.8958             nan     0.2000    0.0004
##     20        0.8030             nan     0.2000    0.0018
##     40        0.6944             nan     0.2000   -0.0019
##     60        0.6376             nan     0.2000   -0.0023
##     80        0.5841             nan     0.2000   -0.0035
##    100        0.5370             nan     0.2000   -0.0025
##    120        0.4859             nan     0.2000   -0.0030
##    140        0.4398             nan     0.2000   -0.0013
##    160        0.4035             nan     0.2000   -0.0026
##    180        0.3726             nan     0.2000   -0.0011
##    200        0.3482             nan     0.2000   -0.0008
##    220        0.3266             nan     0.2000   -0.0018
##    240        0.3016             nan     0.2000   -0.0012
##    260        0.2808             nan     0.2000   -0.0009
##    280        0.2682             nan     0.2000   -0.0020
##    300        0.2529             nan     0.2000   -0.0009
##    320        0.2401             nan     0.2000   -0.0012
##    340        0.2214             nan     0.2000   -0.0010
##    360        0.2045             nan     0.2000   -0.0005
##    380        0.1895             nan     0.2000   -0.0008
##    400        0.1785             nan     0.2000   -0.0018
##    420        0.1656             nan     0.2000   -0.0018
##    440        0.1558             nan     0.2000   -0.0005
##    460        0.1464             nan     0.2000   -0.0005
##    480        0.1380             nan     0.2000   -0.0004
##    500        0.1287             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1986             nan     0.2000    0.0476
##      2        1.1355             nan     0.2000    0.0240
##      3        1.0672             nan     0.2000    0.0233
##      4        1.0262             nan     0.2000    0.0123
##      5        0.9921             nan     0.2000    0.0105
##      6        0.9633             nan     0.2000    0.0102
##      7        0.9397             nan     0.2000    0.0041
##      8        0.9229             nan     0.2000    0.0037
##      9        0.9109             nan     0.2000   -0.0001
##     10        0.9001             nan     0.2000   -0.0001
##     20        0.7986             nan     0.2000   -0.0012
##     40        0.7077             nan     0.2000   -0.0009
##     60        0.6300             nan     0.2000   -0.0040
##     80        0.5843             nan     0.2000   -0.0029
##    100        0.5398             nan     0.2000   -0.0019
##    120        0.4969             nan     0.2000   -0.0025
##    140        0.4592             nan     0.2000   -0.0010
##    160        0.4266             nan     0.2000   -0.0025
##    180        0.3956             nan     0.2000   -0.0020
##    200        0.3735             nan     0.2000   -0.0031
##    220        0.3480             nan     0.2000   -0.0022
##    240        0.3192             nan     0.2000   -0.0014
##    260        0.2941             nan     0.2000   -0.0016
##    280        0.2720             nan     0.2000   -0.0010
##    300        0.2509             nan     0.2000   -0.0014
##    320        0.2366             nan     0.2000   -0.0008
##    340        0.2222             nan     0.2000   -0.0009
##    360        0.2087             nan     0.2000   -0.0015
##    380        0.1955             nan     0.2000   -0.0006
##    400        0.1843             nan     0.2000   -0.0015
##    420        0.1716             nan     0.2000   -0.0006
##    440        0.1605             nan     0.2000   -0.0004
##    460        0.1501             nan     0.2000   -0.0018
##    480        0.1408             nan     0.2000   -0.0002
##    500        0.1324             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1994             nan     0.3000    0.0392
##      2        1.1479             nan     0.3000    0.0223
##      3        1.1048             nan     0.3000    0.0173
##      4        1.0715             nan     0.3000    0.0130
##      5        1.0467             nan     0.3000    0.0075
##      6        1.0232             nan     0.3000    0.0074
##      7        1.0112             nan     0.3000    0.0015
##      8        0.9974             nan     0.3000    0.0050
##      9        0.9843             nan     0.3000    0.0015
##     10        0.9658             nan     0.3000    0.0038
##     20        0.8902             nan     0.3000   -0.0012
##     40        0.8522             nan     0.3000   -0.0023
##     60        0.8188             nan     0.3000   -0.0045
##     80        0.8006             nan     0.3000   -0.0018
##    100        0.7790             nan     0.3000   -0.0028
##    120        0.7658             nan     0.3000   -0.0011
##    140        0.7550             nan     0.3000   -0.0019
##    160        0.7448             nan     0.3000   -0.0006
##    180        0.7299             nan     0.3000   -0.0022
##    200        0.7207             nan     0.3000   -0.0043
##    220        0.7032             nan     0.3000   -0.0020
##    240        0.6918             nan     0.3000   -0.0042
##    260        0.6811             nan     0.3000   -0.0013
##    280        0.6728             nan     0.3000   -0.0019
##    300        0.6631             nan     0.3000   -0.0022
##    320        0.6509             nan     0.3000   -0.0020
##    340        0.6472             nan     0.3000   -0.0027
##    360        0.6404             nan     0.3000   -0.0023
##    380        0.6341             nan     0.3000   -0.0066
##    400        0.6242             nan     0.3000   -0.0035
##    420        0.6121             nan     0.3000   -0.0015
##    440        0.6085             nan     0.3000   -0.0018
##    460        0.6023             nan     0.3000   -0.0012
##    480        0.5920             nan     0.3000   -0.0046
##    500        0.5863             nan     0.3000   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2012             nan     0.3000    0.0383
##      2        1.1505             nan     0.3000    0.0121
##      3        1.1123             nan     0.3000    0.0143
##      4        1.0701             nan     0.3000    0.0124
##      5        1.0506             nan     0.3000    0.0039
##      6        1.0235             nan     0.3000    0.0103
##      7        0.9999             nan     0.3000    0.0063
##      8        0.9805             nan     0.3000    0.0038
##      9        0.9698             nan     0.3000    0.0033
##     10        0.9558             nan     0.3000    0.0001
##     20        0.9044             nan     0.3000   -0.0008
##     40        0.8451             nan     0.3000   -0.0059
##     60        0.8084             nan     0.3000   -0.0044
##     80        0.7912             nan     0.3000   -0.0030
##    100        0.7757             nan     0.3000   -0.0054
##    120        0.7569             nan     0.3000   -0.0034
##    140        0.7396             nan     0.3000   -0.0020
##    160        0.7205             nan     0.3000   -0.0009
##    180        0.7067             nan     0.3000   -0.0033
##    200        0.6948             nan     0.3000   -0.0046
##    220        0.6893             nan     0.3000   -0.0033
##    240        0.6705             nan     0.3000   -0.0007
##    260        0.6582             nan     0.3000   -0.0028
##    280        0.6494             nan     0.3000   -0.0026
##    300        0.6413             nan     0.3000   -0.0014
##    320        0.6359             nan     0.3000   -0.0053
##    340        0.6274             nan     0.3000   -0.0042
##    360        0.6233             nan     0.3000   -0.0050
##    380        0.6208             nan     0.3000   -0.0029
##    400        0.6120             nan     0.3000   -0.0023
##    420        0.6063             nan     0.3000   -0.0038
##    440        0.5987             nan     0.3000   -0.0016
##    460        0.5912             nan     0.3000   -0.0005
##    480        0.5875             nan     0.3000   -0.0009
##    500        0.5807             nan     0.3000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2018             nan     0.3000    0.0352
##      2        1.1455             nan     0.3000    0.0222
##      3        1.1070             nan     0.3000    0.0130
##      4        1.0701             nan     0.3000    0.0151
##      5        1.0418             nan     0.3000    0.0093
##      6        1.0278             nan     0.3000    0.0039
##      7        1.0073             nan     0.3000    0.0096
##      8        0.9958             nan     0.3000   -0.0000
##      9        0.9829             nan     0.3000    0.0023
##     10        0.9691             nan     0.3000    0.0005
##     20        0.8963             nan     0.3000   -0.0039
##     40        0.8419             nan     0.3000    0.0005
##     60        0.8112             nan     0.3000   -0.0024
##     80        0.7944             nan     0.3000   -0.0038
##    100        0.7802             nan     0.3000   -0.0055
##    120        0.7664             nan     0.3000   -0.0039
##    140        0.7574             nan     0.3000   -0.0028
##    160        0.7472             nan     0.3000   -0.0018
##    180        0.7368             nan     0.3000   -0.0048
##    200        0.7194             nan     0.3000   -0.0025
##    220        0.7061             nan     0.3000   -0.0041
##    240        0.6938             nan     0.3000   -0.0019
##    260        0.6832             nan     0.3000   -0.0010
##    280        0.6726             nan     0.3000   -0.0036
##    300        0.6613             nan     0.3000   -0.0022
##    320        0.6509             nan     0.3000   -0.0026
##    340        0.6421             nan     0.3000   -0.0025
##    360        0.6412             nan     0.3000   -0.0023
##    380        0.6343             nan     0.3000   -0.0021
##    400        0.6254             nan     0.3000   -0.0018
##    420        0.6197             nan     0.3000   -0.0011
##    440        0.6121             nan     0.3000   -0.0040
##    460        0.6040             nan     0.3000   -0.0014
##    480        0.5921             nan     0.3000   -0.0024
##    500        0.5811             nan     0.3000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1782             nan     0.3000    0.0503
##      2        1.0911             nan     0.3000    0.0276
##      3        1.0552             nan     0.3000    0.0091
##      4        1.0128             nan     0.3000    0.0168
##      5        0.9795             nan     0.3000    0.0097
##      6        0.9647             nan     0.3000   -0.0035
##      7        0.9387             nan     0.3000    0.0064
##      8        0.9217             nan     0.3000    0.0061
##      9        0.9060             nan     0.3000    0.0040
##     10        0.8941             nan     0.3000   -0.0005
##     20        0.8227             nan     0.3000   -0.0031
##     40        0.7410             nan     0.3000   -0.0036
##     60        0.6751             nan     0.3000   -0.0024
##     80        0.6243             nan     0.3000    0.0003
##    100        0.5786             nan     0.3000   -0.0063
##    120        0.5300             nan     0.3000   -0.0026
##    140        0.4948             nan     0.3000   -0.0040
##    160        0.4618             nan     0.3000   -0.0002
##    180        0.4340             nan     0.3000   -0.0022
##    200        0.4124             nan     0.3000   -0.0016
##    220        0.3912             nan     0.3000   -0.0027
##    240        0.3665             nan     0.3000   -0.0000
##    260        0.3433             nan     0.3000   -0.0031
##    280        0.3272             nan     0.3000   -0.0013
##    300        0.3072             nan     0.3000   -0.0014
##    320        0.2939             nan     0.3000   -0.0023
##    340        0.2754             nan     0.3000   -0.0022
##    360        0.2625             nan     0.3000   -0.0003
##    380        0.2521             nan     0.3000   -0.0012
##    400        0.2362             nan     0.3000   -0.0017
##    420        0.2274             nan     0.3000   -0.0030
##    440        0.2150             nan     0.3000   -0.0012
##    460        0.2037             nan     0.3000   -0.0026
##    480        0.1931             nan     0.3000   -0.0009
##    500        0.1817             nan     0.3000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1688             nan     0.3000    0.0563
##      2        1.1092             nan     0.3000    0.0194
##      3        1.0578             nan     0.3000    0.0174
##      4        1.0072             nan     0.3000    0.0195
##      5        0.9725             nan     0.3000    0.0109
##      6        0.9547             nan     0.3000    0.0064
##      7        0.9412             nan     0.3000    0.0006
##      8        0.9271             nan     0.3000   -0.0003
##      9        0.9130             nan     0.3000    0.0019
##     10        0.8967             nan     0.3000    0.0007
##     20        0.8327             nan     0.3000   -0.0033
##     40        0.7458             nan     0.3000   -0.0055
##     60        0.6953             nan     0.3000   -0.0057
##     80        0.6456             nan     0.3000   -0.0030
##    100        0.6060             nan     0.3000   -0.0003
##    120        0.5747             nan     0.3000   -0.0061
##    140        0.5261             nan     0.3000   -0.0031
##    160        0.4886             nan     0.3000   -0.0042
##    180        0.4621             nan     0.3000   -0.0042
##    200        0.4279             nan     0.3000   -0.0022
##    220        0.4031             nan     0.3000   -0.0041
##    240        0.3767             nan     0.3000   -0.0047
##    260        0.3594             nan     0.3000   -0.0029
##    280        0.3332             nan     0.3000   -0.0015
##    300        0.3157             nan     0.3000   -0.0006
##    320        0.2954             nan     0.3000   -0.0001
##    340        0.2795             nan     0.3000   -0.0018
##    360        0.2637             nan     0.3000   -0.0010
##    380        0.2511             nan     0.3000   -0.0025
##    400        0.2390             nan     0.3000   -0.0005
##    420        0.2304             nan     0.3000   -0.0012
##    440        0.2224             nan     0.3000   -0.0037
##    460        0.2088             nan     0.3000   -0.0005
##    480        0.1990             nan     0.3000   -0.0028
##    500        0.1884             nan     0.3000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1844             nan     0.3000    0.0577
##      2        1.0993             nan     0.3000    0.0414
##      3        1.0395             nan     0.3000    0.0202
##      4        1.0023             nan     0.3000    0.0158
##      5        0.9677             nan     0.3000    0.0043
##      6        0.9545             nan     0.3000   -0.0029
##      7        0.9331             nan     0.3000    0.0048
##      8        0.9058             nan     0.3000    0.0065
##      9        0.9005             nan     0.3000   -0.0066
##     10        0.8880             nan     0.3000   -0.0033
##     20        0.8142             nan     0.3000   -0.0093
##     40        0.7366             nan     0.3000   -0.0008
##     60        0.6809             nan     0.3000   -0.0024
##     80        0.6240             nan     0.3000   -0.0060
##    100        0.5703             nan     0.3000   -0.0037
##    120        0.5306             nan     0.3000   -0.0046
##    140        0.5045             nan     0.3000   -0.0066
##    160        0.4647             nan     0.3000   -0.0016
##    180        0.4296             nan     0.3000   -0.0029
##    200        0.4022             nan     0.3000   -0.0028
##    220        0.3797             nan     0.3000   -0.0025
##    240        0.3592             nan     0.3000   -0.0017
##    260        0.3359             nan     0.3000   -0.0033
##    280        0.3222             nan     0.3000   -0.0050
##    300        0.3044             nan     0.3000   -0.0021
##    320        0.2854             nan     0.3000   -0.0018
##    340        0.2678             nan     0.3000   -0.0010
##    360        0.2480             nan     0.3000   -0.0012
##    380        0.2326             nan     0.3000   -0.0018
##    400        0.2248             nan     0.3000   -0.0005
##    420        0.2145             nan     0.3000   -0.0026
##    440        0.2036             nan     0.3000    0.0000
##    460        0.1918             nan     0.3000   -0.0006
##    480        0.1812             nan     0.3000   -0.0013
##    500        0.1749             nan     0.3000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1476             nan     0.3000    0.0681
##      2        1.0616             nan     0.3000    0.0247
##      3        1.0126             nan     0.3000    0.0141
##      4        0.9776             nan     0.3000    0.0066
##      5        0.9401             nan     0.3000    0.0136
##      6        0.9138             nan     0.3000    0.0012
##      7        0.8966             nan     0.3000   -0.0008
##      8        0.8866             nan     0.3000   -0.0082
##      9        0.8703             nan     0.3000   -0.0003
##     10        0.8575             nan     0.3000   -0.0029
##     20        0.7437             nan     0.3000   -0.0027
##     40        0.6372             nan     0.3000   -0.0048
##     60        0.5648             nan     0.3000   -0.0061
##     80        0.5144             nan     0.3000   -0.0030
##    100        0.4585             nan     0.3000   -0.0037
##    120        0.4003             nan     0.3000   -0.0033
##    140        0.3517             nan     0.3000   -0.0019
##    160        0.3055             nan     0.3000   -0.0033
##    180        0.2731             nan     0.3000   -0.0015
##    200        0.2436             nan     0.3000   -0.0015
##    220        0.2230             nan     0.3000   -0.0024
##    240        0.2038             nan     0.3000   -0.0005
##    260        0.1848             nan     0.3000   -0.0036
##    280        0.1670             nan     0.3000   -0.0030
##    300        0.1511             nan     0.3000   -0.0011
##    320        0.1353             nan     0.3000   -0.0012
##    340        0.1271             nan     0.3000   -0.0009
##    360        0.1171             nan     0.3000   -0.0013
##    380        0.1082             nan     0.3000   -0.0015
##    400        0.0987             nan     0.3000   -0.0007
##    420        0.0916             nan     0.3000   -0.0006
##    440        0.0851             nan     0.3000   -0.0004
##    460        0.0794             nan     0.3000   -0.0011
##    480        0.0731             nan     0.3000   -0.0004
##    500        0.0692             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1660             nan     0.3000    0.0457
##      2        1.0853             nan     0.3000    0.0233
##      3        1.0134             nan     0.3000    0.0161
##      4        0.9784             nan     0.3000    0.0080
##      5        0.9512             nan     0.3000   -0.0012
##      6        0.9264             nan     0.3000    0.0014
##      7        0.8971             nan     0.3000    0.0013
##      8        0.8839             nan     0.3000   -0.0048
##      9        0.8706             nan     0.3000    0.0001
##     10        0.8593             nan     0.3000   -0.0001
##     20        0.7767             nan     0.3000   -0.0034
##     40        0.6587             nan     0.3000   -0.0057
##     60        0.5627             nan     0.3000   -0.0061
##     80        0.5069             nan     0.3000   -0.0033
##    100        0.4359             nan     0.3000   -0.0027
##    120        0.3885             nan     0.3000   -0.0050
##    140        0.3435             nan     0.3000   -0.0043
##    160        0.3013             nan     0.3000   -0.0023
##    180        0.2680             nan     0.3000   -0.0028
##    200        0.2429             nan     0.3000   -0.0023
##    220        0.2203             nan     0.3000   -0.0015
##    240        0.1983             nan     0.3000   -0.0027
##    260        0.1819             nan     0.3000   -0.0009
##    280        0.1654             nan     0.3000   -0.0032
##    300        0.1484             nan     0.3000   -0.0015
##    320        0.1354             nan     0.3000   -0.0007
##    340        0.1215             nan     0.3000   -0.0008
##    360        0.1120             nan     0.3000   -0.0006
##    380        0.1026             nan     0.3000   -0.0005
##    400        0.0922             nan     0.3000   -0.0005
##    420        0.0859             nan     0.3000   -0.0007
##    440        0.0793             nan     0.3000   -0.0004
##    460        0.0737             nan     0.3000   -0.0008
##    480        0.0683             nan     0.3000   -0.0006
##    500        0.0628             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1488             nan     0.3000    0.0598
##      2        1.0641             nan     0.3000    0.0376
##      3        1.0132             nan     0.3000    0.0202
##      4        0.9652             nan     0.3000    0.0198
##      5        0.9383             nan     0.3000    0.0065
##      6        0.9178             nan     0.3000    0.0034
##      7        0.9014             nan     0.3000   -0.0014
##      8        0.8793             nan     0.3000    0.0004
##      9        0.8713             nan     0.3000   -0.0070
##     10        0.8543             nan     0.3000    0.0010
##     20        0.7619             nan     0.3000   -0.0045
##     40        0.6628             nan     0.3000   -0.0081
##     60        0.5812             nan     0.3000   -0.0011
##     80        0.5204             nan     0.3000   -0.0041
##    100        0.4500             nan     0.3000   -0.0009
##    120        0.4007             nan     0.3000   -0.0017
##    140        0.3575             nan     0.3000   -0.0027
##    160        0.3186             nan     0.3000   -0.0030
##    180        0.2886             nan     0.3000   -0.0039
##    200        0.2575             nan     0.3000   -0.0030
##    220        0.2267             nan     0.3000   -0.0007
##    240        0.2085             nan     0.3000   -0.0015
##    260        0.1879             nan     0.3000   -0.0003
##    280        0.1671             nan     0.3000   -0.0014
##    300        0.1555             nan     0.3000   -0.0012
##    320        0.1437             nan     0.3000   -0.0010
##    340        0.1317             nan     0.3000   -0.0017
##    360        0.1183             nan     0.3000   -0.0007
##    380        0.1084             nan     0.3000   -0.0010
##    400        0.0985             nan     0.3000   -0.0004
##    420        0.0900             nan     0.3000   -0.0007
##    440        0.0824             nan     0.3000   -0.0009
##    460        0.0756             nan     0.3000   -0.0007
##    480        0.0693             nan     0.3000   -0.0006
##    500        0.0641             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1633             nan     0.5000    0.0582
##      2        1.1076             nan     0.5000    0.0169
##      3        1.0623             nan     0.5000    0.0135
##      4        1.0077             nan     0.5000    0.0190
##      5        0.9813             nan     0.5000    0.0120
##      6        0.9656             nan     0.5000   -0.0026
##      7        0.9421             nan     0.5000    0.0051
##      8        0.9357             nan     0.5000    0.0015
##      9        0.9308             nan     0.5000   -0.0017
##     10        0.9199             nan     0.5000   -0.0014
##     20        0.8690             nan     0.5000   -0.0016
##     40        0.8266             nan     0.5000   -0.0026
##     60        0.8021             nan     0.5000   -0.0062
##     80        0.7814             nan     0.5000   -0.0040
##    100        0.7547             nan     0.5000   -0.0069
##    120        0.7355             nan     0.5000   -0.0086
##    140        0.7159             nan     0.5000   -0.0061
##    160        0.6935             nan     0.5000   -0.0041
##    180        0.6786             nan     0.5000   -0.0030
##    200        0.6647             nan     0.5000   -0.0076
##    220        0.6372             nan     0.5000   -0.0046
##    240        0.6342             nan     0.5000   -0.0044
##    260        0.6188             nan     0.5000   -0.0046
##    280        0.6151             nan     0.5000   -0.0072
##    300        0.5959             nan     0.5000   -0.0019
##    320        0.5806             nan     0.5000   -0.0018
##    340        0.5760             nan     0.5000   -0.0021
##    360        0.5587             nan     0.5000   -0.0035
##    380        0.5554             nan     0.5000   -0.0023
##    400        0.5358             nan     0.5000   -0.0033
##    420        0.5272             nan     0.5000   -0.0020
##    440        0.5225             nan     0.5000   -0.0010
##    460        0.5099             nan     0.5000   -0.0037
##    480        0.5039             nan     0.5000   -0.0000
##    500        0.4953             nan     0.5000   -0.0034
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1640             nan     0.5000    0.0669
##      2        1.1164             nan     0.5000    0.0124
##      3        1.0618             nan     0.5000    0.0220
##      4        1.0149             nan     0.5000    0.0179
##      5        0.9853             nan     0.5000    0.0101
##      6        0.9690             nan     0.5000   -0.0016
##      7        0.9586             nan     0.5000   -0.0007
##      8        0.9351             nan     0.5000    0.0086
##      9        0.9269             nan     0.5000   -0.0048
##     10        0.9194             nan     0.5000   -0.0044
##     20        0.8635             nan     0.5000    0.0006
##     40        0.8228             nan     0.5000   -0.0039
##     60        0.7885             nan     0.5000   -0.0045
##     80        0.7623             nan     0.5000   -0.0067
##    100        0.7490             nan     0.5000   -0.0082
##    120        0.7191             nan     0.5000   -0.0018
##    140        0.7043             nan     0.5000   -0.0043
##    160        0.6879             nan     0.5000   -0.0040
##    180        0.6734             nan     0.5000   -0.0100
##    200        0.6500             nan     0.5000   -0.0125
##    220        0.6341             nan     0.5000   -0.0027
##    240        0.6236             nan     0.5000   -0.0061
##    260        0.6078             nan     0.5000   -0.0021
##    280        0.5913             nan     0.5000   -0.0061
##    300        0.5790             nan     0.5000   -0.0033
##    320        0.5692             nan     0.5000   -0.0014
##    340        0.5647             nan     0.5000   -0.0035
##    360        0.5532             nan     0.5000   -0.0042
##    380        0.5443             nan     0.5000   -0.0018
##    400        0.5402             nan     0.5000   -0.0057
##    420        0.5280             nan     0.5000   -0.0018
##    440        0.5179             nan     0.5000   -0.0010
##    460        0.5165             nan     0.5000   -0.0024
##    480        0.5036             nan     0.5000   -0.0018
##    500        0.4991             nan     0.5000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1572             nan     0.5000    0.0629
##      2        1.0964             nan     0.5000    0.0200
##      3        1.0337             nan     0.5000    0.0200
##      4        0.9988             nan     0.5000    0.0173
##      5        0.9744             nan     0.5000    0.0111
##      6        0.9627             nan     0.5000    0.0008
##      7        0.9438             nan     0.5000    0.0055
##      8        0.9253             nan     0.5000    0.0002
##      9        0.9113             nan     0.5000   -0.0021
##     10        0.9073             nan     0.5000   -0.0007
##     20        0.8628             nan     0.5000   -0.0102
##     40        0.8077             nan     0.5000   -0.0023
##     60        0.7804             nan     0.5000   -0.0021
##     80        0.7518             nan     0.5000   -0.0072
##    100        0.7373             nan     0.5000   -0.0039
##    120        0.7327             nan     0.5000   -0.0058
##    140        0.7167             nan     0.5000   -0.0010
##    160        0.6895             nan     0.5000    0.0031
##    180        0.6834             nan     0.5000   -0.0144
##    200        0.6647             nan     0.5000   -0.0059
##    220        0.6499             nan     0.5000   -0.0051
##    240        0.6334             nan     0.5000   -0.0041
##    260        0.6135             nan     0.5000   -0.0036
##    280        0.6070             nan     0.5000   -0.0071
##    300        0.5936             nan     0.5000   -0.0027
##    320        0.5942             nan     0.5000   -0.0044
##    340        0.5897             nan     0.5000   -0.0060
##    360        0.5827             nan     0.5000   -0.0084
##    380        0.5731             nan     0.5000   -0.0069
##    400        0.5541             nan     0.5000   -0.0014
##    420        0.5461             nan     0.5000   -0.0082
##    440        0.5392             nan     0.5000   -0.0024
##    460        0.5339             nan     0.5000   -0.0055
##    480        0.5252             nan     0.5000   -0.0049
##    500        0.5114             nan     0.5000   -0.0040
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1370             nan     0.5000    0.0754
##      2        1.0297             nan     0.5000    0.0477
##      3        0.9744             nan     0.5000    0.0171
##      4        0.9488             nan     0.5000    0.0048
##      5        0.9338             nan     0.5000   -0.0070
##      6        0.9136             nan     0.5000   -0.0033
##      7        0.9049             nan     0.5000   -0.0044
##      8        0.8931             nan     0.5000   -0.0114
##      9        0.8864             nan     0.5000   -0.0159
##     10        0.8688             nan     0.5000   -0.0046
##     20        0.8010             nan     0.5000   -0.0122
##     40        0.7039             nan     0.5000   -0.0110
##     60        0.5955             nan     0.5000   -0.0034
##     80        0.5529             nan     0.5000   -0.0073
##    100        0.4882             nan     0.5000   -0.0042
##    120        0.4324             nan     0.5000   -0.0064
##    140        0.3891             nan     0.5000   -0.0012
##    160        0.3545             nan     0.5000   -0.0041
##    180        0.3074             nan     0.5000   -0.0025
##    200        0.2747             nan     0.5000   -0.0009
##    220        0.2509             nan     0.5000   -0.0026
##    240        0.2210             nan     0.5000   -0.0040
##    260        0.2019             nan     0.5000   -0.0023
##    280        0.1813             nan     0.5000   -0.0015
##    300        0.1688             nan     0.5000   -0.0024
##    320        0.1525             nan     0.5000   -0.0000
##    340        0.1393             nan     0.5000   -0.0022
##    360        0.1299             nan     0.5000   -0.0008
##    380        0.1204             nan     0.5000   -0.0031
##    400        0.1138             nan     0.5000   -0.0015
##    420        0.1049             nan     0.5000   -0.0015
##    440        0.0991             nan     0.5000   -0.0021
##    460        0.0915             nan     0.5000   -0.0003
##    480        0.0875             nan     0.5000   -0.0002
##    500        0.0791             nan     0.5000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1042             nan     0.5000    0.0561
##      2        1.0394             nan     0.5000    0.0199
##      3        0.9942             nan     0.5000    0.0131
##      4        0.9624             nan     0.5000    0.0074
##      5        0.9453             nan     0.5000   -0.0047
##      6        0.9053             nan     0.5000    0.0082
##      7        0.9003             nan     0.5000   -0.0128
##      8        0.8864             nan     0.5000    0.0012
##      9        0.8820             nan     0.5000   -0.0103
##     10        0.8690             nan     0.5000   -0.0021
##     20        0.7985             nan     0.5000   -0.0058
##     40        0.6927             nan     0.5000   -0.0045
##     60        0.6120             nan     0.5000   -0.0083
##     80        0.5625             nan     0.5000   -0.0051
##    100        0.4915             nan     0.5000   -0.0025
##    120        0.4469             nan     0.5000   -0.0055
##    140        0.3953             nan     0.5000   -0.0034
##    160        0.3545             nan     0.5000   -0.0058
##    180        0.3307             nan     0.5000   -0.0028
##    200        0.3027             nan     0.5000   -0.0066
##    220        0.2804             nan     0.5000   -0.0070
##    240        0.2471             nan     0.5000   -0.0036
##    260        0.2279             nan     0.5000   -0.0052
##    280        0.2034             nan     0.5000   -0.0035
##    300        0.1802             nan     0.5000   -0.0028
##    320        0.1642             nan     0.5000   -0.0002
##    340        0.1547             nan     0.5000   -0.0025
##    360        0.1402             nan     0.5000   -0.0006
##    380        0.1273             nan     0.5000   -0.0024
##    400        0.1165             nan     0.5000   -0.0006
##    420        0.1091             nan     0.5000   -0.0020
##    440        0.0981             nan     0.5000   -0.0006
##    460        0.0916             nan     0.5000   -0.0018
##    480        0.0835             nan     0.5000   -0.0003
##    500        0.0778             nan     0.5000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1326             nan     0.5000    0.0525
##      2        1.0410             nan     0.5000    0.0435
##      3        0.9923             nan     0.5000    0.0108
##      4        0.9619             nan     0.5000    0.0036
##      5        0.9382             nan     0.5000   -0.0053
##      6        0.9161             nan     0.5000    0.0023
##      7        0.8854             nan     0.5000    0.0107
##      8        0.8701             nan     0.5000   -0.0018
##      9        0.8651             nan     0.5000   -0.0111
##     10        0.8611             nan     0.5000   -0.0091
##     20        0.7969             nan     0.5000   -0.0035
##     40        0.6945             nan     0.5000   -0.0033
##     60        0.6335             nan     0.5000   -0.0161
##     80        0.5533             nan     0.5000   -0.0068
##    100        0.5088             nan     0.5000   -0.0058
##    120        0.4539             nan     0.5000   -0.0064
##    140        0.4380             nan     0.5000   -0.0097
##    160        0.3944             nan     0.5000   -0.0047
##    180        0.3546             nan     0.5000   -0.0082
##    200        0.3225             nan     0.5000   -0.0059
##    220        0.2876             nan     0.5000   -0.0015
##    240        0.2647             nan     0.5000   -0.0019
##    260        0.2408             nan     0.5000   -0.0046
##    280        0.2173             nan     0.5000   -0.0046
##    300        0.1967             nan     0.5000   -0.0011
##    320        0.1816             nan     0.5000   -0.0029
##    340        0.1700             nan     0.5000   -0.0025
##    360        0.1543             nan     0.5000   -0.0019
##    380        0.1435             nan     0.5000   -0.0011
##    400        0.1352             nan     0.5000   -0.0031
##    420        0.1257             nan     0.5000   -0.0017
##    440        0.1138             nan     0.5000   -0.0023
##    460        0.1035             nan     0.5000   -0.0014
##    480        0.0952             nan     0.5000   -0.0004
##    500        0.0899             nan     0.5000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1018             nan     0.5000    0.0837
##      2        1.0002             nan     0.5000    0.0386
##      3        0.9592             nan     0.5000    0.0017
##      4        0.9250             nan     0.5000    0.0094
##      5        0.9038             nan     0.5000   -0.0076
##      6        0.8880             nan     0.5000   -0.0115
##      7        0.8605             nan     0.5000   -0.0066
##      8        0.8464             nan     0.5000   -0.0109
##      9        0.8327             nan     0.5000   -0.0138
##     10        0.8296             nan     0.5000   -0.0146
##     20        0.7554             nan     0.5000   -0.0259
##     40    13569.6981             nan     0.5000   -0.0077
##     60    13598.8695             nan     0.5000   -0.0002
##     80    13598.7847             nan     0.5000   -0.0003
##    100           inf             nan     0.5000   -0.0183
##    120           inf             nan     0.5000   -0.0015
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180 7278132543442879774820.0000             nan     0.5000       inf
##    200 7278132543442879774820.0000             nan     0.5000    0.0001
##    220 7278132543442879774820.0000             nan     0.5000   -0.0001
##    240 7278132543442879774820.0000             nan     0.5000   -0.0110
##    260 7278132543442879774820.0000             nan     0.5000   -0.0001
##    280 7278132543442879774820.0000             nan     0.5000   -0.0020
##    300 7278132543442879774820.0000             nan     0.5000   -0.0006
##    320 7278132543442879774820.0000             nan     0.5000    0.0000
##    340 7278132543442879774820.0000             nan     0.5000   -0.0007
##    360 7278132543442879774820.0000             nan     0.5000    0.0000
##    380 7278132543442879774820.0000             nan     0.5000    0.0004
##    400 7278132543442879774820.0000             nan     0.5000   -0.0025
##    420 7278132543442879774820.0000             nan     0.5000   -0.0009
##    440 7278132543442879774820.0000             nan     0.5000   -0.0002
##    460 7278132543442879774820.0000             nan     0.5000   -0.0006
##    480 7278132543442879774820.0000             nan     0.5000   -0.0001
##    500 7278132543442879774820.0000             nan     0.5000   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1187             nan     0.5000    0.0625
##      2        0.9977             nan     0.5000    0.0372
##      3        0.9664             nan     0.5000   -0.0017
##      4        0.9414             nan     0.5000   -0.0048
##      5        0.9081             nan     0.5000    0.0134
##      6        0.8969             nan     0.5000   -0.0163
##      7        0.8762             nan     0.5000   -0.0082
##      8        0.8475             nan     0.5000    0.0056
##      9        0.8225             nan     0.5000   -0.0030
##     10        0.8136             nan     0.5000   -0.0075
##     20        0.7423             nan     0.5000   -0.0107
##     40        0.5888             nan     0.5000   -0.0015
##     60        0.4832             nan     0.5000   -0.0053
##     80        0.4202             nan     0.5000   -0.0072
##    100        0.3535             nan     0.5000   -0.0059
##    120        0.2869             nan     0.5000   -0.0065
##    140        0.2248             nan     0.5000    0.0001
##    160        0.1949             nan     0.5000   -0.0023
##    180        0.1726             nan     0.5000   -0.0004
##    200        0.1464             nan     0.5000   -0.0030
##    220        0.1276             nan     0.5000   -0.0035
##    240        0.1141             nan     0.5000   -0.0002
##    260        0.0984             nan     0.5000   -0.0013
##    280        0.0852             nan     0.5000   -0.0011
##    300        0.0748             nan     0.5000   -0.0004
##    320        0.0671             nan     0.5000   -0.0014
##    340        0.0589             nan     0.5000   -0.0004
##    360        0.0522             nan     0.5000   -0.0004
##    380        0.0459             nan     0.5000   -0.0011
##    400        0.0399             nan     0.5000   -0.0004
##    420        0.0350             nan     0.5000   -0.0003
##    440        0.0319             nan     0.5000   -0.0004
##    460        0.0274             nan     0.5000   -0.0007
##    480        0.0247             nan     0.5000   -0.0001
##    500        0.0223             nan     0.5000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0992             nan     0.5000    0.0751
##      2        1.0073             nan     0.5000    0.0333
##      3        0.9478             nan     0.5000    0.0231
##      4        0.9177             nan     0.5000    0.0040
##      5        0.8962             nan     0.5000    0.0006
##      6        0.8896             nan     0.5000   -0.0181
##      7        0.8670             nan     0.5000   -0.0067
##      8        0.8402             nan     0.5000   -0.0024
##      9        0.8316             nan     0.5000   -0.0161
##     10        0.8122             nan     0.5000   -0.0124
##     20        0.7193             nan     0.5000   -0.0056
##     40        0.6159             nan     0.5000   -0.0080
##     60        0.5024             nan     0.5000   -0.0077
##     80        0.4348             nan     0.5000   -0.0032
##    100        0.3516             nan     0.5000   -0.0075
##    120        0.2744             nan     0.5000   -0.0057
##    140        0.2238             nan     0.5000   -0.0042
##    160        0.1828             nan     0.5000   -0.0031
##    180        0.1571             nan     0.5000   -0.0011
##    200        0.1347             nan     0.5000   -0.0021
##    220        0.1165             nan     0.5000   -0.0004
##    240        0.1018             nan     0.5000   -0.0020
##    260        0.0875             nan     0.5000   -0.0010
##    280        0.0760             nan     0.5000   -0.0011
##    300        0.0666             nan     0.5000   -0.0011
##    320        0.0566             nan     0.5000   -0.0005
##    340        0.0504             nan     0.5000   -0.0009
##    360        0.0443             nan     0.5000   -0.0002
##    380        0.0393             nan     0.5000   -0.0004
##    400        0.0363             nan     0.5000   -0.0007
##    420        0.0326             nan     0.5000   -0.0008
##    440        0.0294             nan     0.5000   -0.0005
##    460        0.0256             nan     0.5000   -0.0008
##    480        0.0224             nan     0.5000   -0.0002
##    500        0.0201             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1406             nan     1.0000    0.0651
##      2        1.0974             nan     1.0000   -0.0001
##      3        1.0463             nan     1.0000    0.0250
##      4        1.0443             nan     1.0000   -0.0258
##      5        1.0130             nan     1.0000    0.0096
##      6        1.0163             nan     1.0000   -0.0186
##      7        1.0255             nan     1.0000   -0.0200
##      8        1.0201             nan     1.0000   -0.0113
##      9        1.0248             nan     1.0000   -0.0203
##     10        1.0345             nan     1.0000   -0.0250
##     20        0.9814             nan     1.0000   -0.0109
##     40        1.1722             nan     1.0000   -0.0372
##     60        1.8367             nan     1.0000   -0.0019
##     80        5.2571             nan     1.0000   -0.0010
##    100        5.2802             nan     1.0000   -0.0007
##    120        5.2626             nan     1.0000    0.0020
##    140        5.2111             nan     1.0000   -0.0012
##    160        5.2195             nan     1.0000    0.0009
##    180      391.2125             nan     1.0000   -0.0169
##    200 4015659054.2571             nan     1.0000   -0.0098
##    220 4015659054.1409             nan     1.0000    0.0019
##    240 4015659055.6829             nan     1.0000   -0.9159
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1429             nan     1.0000    0.0533
##      2        1.0508             nan     1.0000    0.0422
##      3        0.9938             nan     1.0000    0.0085
##      4        0.9551             nan     1.0000   -0.0032
##      5        0.9580             nan     1.0000   -0.0157
##      6        0.9537             nan     1.0000   -0.0083
##      7        0.9311             nan     1.0000   -0.0004
##      8        0.9196             nan     1.0000   -0.0043
##      9        0.9182             nan     1.0000   -0.0150
##     10        0.9105             nan     1.0000   -0.0028
##     20        0.8827             nan     1.0000   -0.0084
##     40       43.3363             nan     1.0000   -0.0002
##     60       43.2636             nan     1.0000   -0.0165
##     80 57460465.6053             nan     1.0000   -0.0042
##    100 57460465.5679             nan     1.0000   -0.0047
##    120 57460465.5400             nan     1.0000    0.0014
##    140 57460467.3999             nan     1.0000   -1.8905
##    160 57460467.3755             nan     1.0000   -0.0023
##    180 57460467.3403             nan     1.0000    0.0004
##    200           inf             nan     1.0000   -0.0000
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1547             nan     1.0000    0.0300
##      2        1.0975             nan     1.0000    0.0043
##      3        1.0471             nan     1.0000    0.0206
##      4        1.0037             nan     1.0000    0.0095
##      5        0.9991             nan     1.0000   -0.0065
##      6        1.0029             nan     1.0000   -0.0198
##      7        0.9890             nan     1.0000   -0.0061
##      8        0.9654             nan     1.0000    0.0071
##      9        0.9554             nan     1.0000   -0.0094
##     10        0.9558             nan     1.0000   -0.0213
##     20        0.9414             nan     1.0000   -0.0323
##     40        1.0048             nan     1.0000   -0.0075
##     60        0.9676             nan     1.0000   -0.0293
##     80        0.8901             nan     1.0000   -0.0141
##    100 78097103364806.4688             nan     1.0000   -0.0009
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000   -0.0145
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0751             nan     1.0000    0.1093
##      2        1.0325             nan     1.0000   -0.0158
##      3        0.9792             nan     1.0000    0.0103
##      4        0.9767             nan     1.0000   -0.0206
##      5        0.9532             nan     1.0000   -0.0195
##      6        0.9362             nan     1.0000   -0.0154
##      7        0.9640             nan     1.0000   -0.0625
##      8        0.9345             nan     1.0000    0.0078
##      9        0.9314             nan     1.0000   -0.0204
##     10        0.9330             nan     1.0000   -0.0191
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0699             nan     1.0000    0.0836
##      2        0.9871             nan     1.0000    0.0241
##      3        0.9588             nan     1.0000    0.0007
##      4        0.9080             nan     1.0000    0.0130
##      5        0.8900             nan     1.0000   -0.0149
##      6        0.9102             nan     1.0000   -0.0433
##      7        0.9000             nan     1.0000   -0.0124
##      8        0.8929             nan     1.0000   -0.0159
##      9        0.8999             nan     1.0000   -0.0284
##     10        0.8732             nan     1.0000   -0.0041
##     20        0.8710             nan     1.0000   -0.0145
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0684             nan     1.0000    0.1026
##      2        0.9620             nan     1.0000    0.0333
##      3        0.9347             nan     1.0000   -0.0090
##      4        1.0411             nan     1.0000   -0.1455
##      5        0.9767             nan     1.0000    0.0313
##      6        0.9828             nan     1.0000   -0.0260
##      7        0.9977             nan     1.0000    0.0028
##      8        1.0153             nan     1.0000   -0.0541
##      9        1.0156             nan     1.0000   -0.0276
##     10        0.9966             nan     1.0000   -0.0061
##     20        0.9612             nan     1.0000   -0.0217
##     40        1.0398             nan     1.0000   -0.1048
##     60        0.9066             nan     1.0000   -0.0262
##     80        0.9370             nan     1.0000   -0.0075
##    100       13.8214             nan     1.0000  -13.1257
##    120    82312.9346             nan     1.0000    1.7357
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0844             nan     1.0000    0.0617
##      2        1.0530             nan     1.0000   -0.0364
##      3        1.1251             nan     1.0000   -0.1134
##      4        1.1372             nan     1.0000   -0.0766
##      5        1.1780             nan     1.0000   -0.0982
##      6        1.1202             nan     1.0000   -0.0040
##      7        1.0752             nan     1.0000   -0.0219
##      8        1.0765             nan     1.0000   -0.0797
##      9        1.0245             nan     1.0000   -0.0015
##     10        1.0541             nan     1.0000   -0.0973
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0465             nan     1.0000    0.0772
##      2        0.9910             nan     1.0000    0.0016
##      3        0.9434             nan     1.0000    0.0011
##      4        0.9342             nan     1.0000   -0.0192
##      5        0.9481             nan     1.0000   -0.0582
##      6        0.9330             nan     1.0000   -0.0226
##      7        0.9501             nan     1.0000   -0.0581
##      8        0.9412             nan     1.0000   -0.0288
##      9        0.8975             nan     1.0000    0.0034
##     10        0.8834             nan     1.0000   -0.0386
##     20        0.9797             nan     1.0000   -0.0196
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0549             nan     1.0000    0.1055
##      2        1.0463             nan     1.0000   -0.0453
##      3        1.0247             nan     1.0000   -0.0537
##      4        0.9513             nan     1.0000    0.0223
##      5        0.9510             nan     1.0000   -0.0408
##      6        0.9451             nan     1.0000   -0.0310
##      7        0.9342             nan     1.0000   -0.0185
##      8        0.9510             nan     1.0000   -0.0666
##      9        0.9350             nan     1.0000   -0.0244
##     10        0.9640             nan     1.0000   -0.0570
##     20        3.0920             nan     1.0000   -0.0487
##     40       28.6800             nan     1.0000    0.0037
##     60 187557354839.2242             nan     1.0000   -0.1835
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2860             nan     0.0010    0.0001
##     40        1.2791             nan     0.0010    0.0002
##     60        1.2722             nan     0.0010    0.0001
##     80        1.2660             nan     0.0010    0.0002
##    100        1.2596             nan     0.0010    0.0001
##    120        1.2535             nan     0.0010    0.0001
##    140        1.2477             nan     0.0010    0.0001
##    160        1.2419             nan     0.0010    0.0001
##    180        1.2362             nan     0.0010    0.0001
##    200        1.2309             nan     0.0010    0.0001
##    220        1.2255             nan     0.0010    0.0001
##    240        1.2203             nan     0.0010    0.0001
##    260        1.2153             nan     0.0010    0.0001
##    280        1.2105             nan     0.0010    0.0001
##    300        1.2059             nan     0.0010    0.0001
##    320        1.2015             nan     0.0010    0.0001
##    340        1.1973             nan     0.0010    0.0001
##    360        1.1931             nan     0.0010    0.0001
##    380        1.1888             nan     0.0010    0.0001
##    400        1.1848             nan     0.0010    0.0001
##    420        1.1809             nan     0.0010    0.0001
##    440        1.1770             nan     0.0010    0.0001
##    460        1.1731             nan     0.0010    0.0001
##    480        1.1694             nan     0.0010    0.0001
##    500        1.1656             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2790             nan     0.0010    0.0002
##     60        1.2723             nan     0.0010    0.0002
##     80        1.2658             nan     0.0010    0.0002
##    100        1.2592             nan     0.0010    0.0001
##    120        1.2531             nan     0.0010    0.0002
##    140        1.2475             nan     0.0010    0.0001
##    160        1.2417             nan     0.0010    0.0001
##    180        1.2360             nan     0.0010    0.0001
##    200        1.2306             nan     0.0010    0.0001
##    220        1.2254             nan     0.0010    0.0001
##    240        1.2203             nan     0.0010    0.0001
##    260        1.2152             nan     0.0010    0.0001
##    280        1.2106             nan     0.0010    0.0001
##    300        1.2058             nan     0.0010    0.0001
##    320        1.2014             nan     0.0010    0.0001
##    340        1.1970             nan     0.0010    0.0001
##    360        1.1927             nan     0.0010    0.0001
##    380        1.1886             nan     0.0010    0.0001
##    400        1.1844             nan     0.0010    0.0001
##    420        1.1803             nan     0.0010    0.0001
##    440        1.1764             nan     0.0010    0.0001
##    460        1.1725             nan     0.0010    0.0001
##    480        1.1688             nan     0.0010    0.0001
##    500        1.1650             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2862             nan     0.0010    0.0002
##     40        1.2789             nan     0.0010    0.0002
##     60        1.2720             nan     0.0010    0.0002
##     80        1.2654             nan     0.0010    0.0002
##    100        1.2591             nan     0.0010    0.0001
##    120        1.2529             nan     0.0010    0.0002
##    140        1.2469             nan     0.0010    0.0001
##    160        1.2415             nan     0.0010    0.0001
##    180        1.2358             nan     0.0010    0.0001
##    200        1.2305             nan     0.0010    0.0001
##    220        1.2252             nan     0.0010    0.0001
##    240        1.2205             nan     0.0010    0.0001
##    260        1.2156             nan     0.0010    0.0001
##    280        1.2109             nan     0.0010    0.0001
##    300        1.2064             nan     0.0010    0.0001
##    320        1.2019             nan     0.0010    0.0001
##    340        1.1975             nan     0.0010    0.0001
##    360        1.1932             nan     0.0010    0.0001
##    380        1.1890             nan     0.0010    0.0001
##    400        1.1848             nan     0.0010    0.0001
##    420        1.1809             nan     0.0010    0.0001
##    440        1.1771             nan     0.0010    0.0001
##    460        1.1731             nan     0.0010    0.0001
##    480        1.1694             nan     0.0010    0.0001
##    500        1.1656             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2836             nan     0.0010    0.0002
##     40        1.2742             nan     0.0010    0.0002
##     60        1.2654             nan     0.0010    0.0002
##     80        1.2568             nan     0.0010    0.0002
##    100        1.2485             nan     0.0010    0.0002
##    120        1.2405             nan     0.0010    0.0001
##    140        1.2326             nan     0.0010    0.0001
##    160        1.2250             nan     0.0010    0.0002
##    180        1.2174             nan     0.0010    0.0002
##    200        1.2102             nan     0.0010    0.0001
##    220        1.2032             nan     0.0010    0.0001
##    240        1.1963             nan     0.0010    0.0002
##    260        1.1898             nan     0.0010    0.0002
##    280        1.1834             nan     0.0010    0.0001
##    300        1.1769             nan     0.0010    0.0001
##    320        1.1707             nan     0.0010    0.0001
##    340        1.1648             nan     0.0010    0.0001
##    360        1.1592             nan     0.0010    0.0001
##    380        1.1537             nan     0.0010    0.0001
##    400        1.1482             nan     0.0010    0.0001
##    420        1.1429             nan     0.0010    0.0001
##    440        1.1376             nan     0.0010    0.0001
##    460        1.1326             nan     0.0010    0.0001
##    480        1.1277             nan     0.0010    0.0001
##    500        1.1228             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0003
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2838             nan     0.0010    0.0002
##     40        1.2748             nan     0.0010    0.0002
##     60        1.2660             nan     0.0010    0.0002
##     80        1.2574             nan     0.0010    0.0001
##    100        1.2490             nan     0.0010    0.0002
##    120        1.2410             nan     0.0010    0.0002
##    140        1.2333             nan     0.0010    0.0002
##    160        1.2257             nan     0.0010    0.0002
##    180        1.2181             nan     0.0010    0.0002
##    200        1.2109             nan     0.0010    0.0002
##    220        1.2040             nan     0.0010    0.0001
##    240        1.1971             nan     0.0010    0.0001
##    260        1.1904             nan     0.0010    0.0001
##    280        1.1838             nan     0.0010    0.0001
##    300        1.1777             nan     0.0010    0.0001
##    320        1.1717             nan     0.0010    0.0001
##    340        1.1656             nan     0.0010    0.0001
##    360        1.1598             nan     0.0010    0.0001
##    380        1.1543             nan     0.0010    0.0001
##    400        1.1489             nan     0.0010    0.0001
##    420        1.1435             nan     0.0010    0.0001
##    440        1.1383             nan     0.0010    0.0001
##    460        1.1331             nan     0.0010    0.0001
##    480        1.1281             nan     0.0010    0.0001
##    500        1.1234             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2837             nan     0.0010    0.0002
##     40        1.2744             nan     0.0010    0.0002
##     60        1.2655             nan     0.0010    0.0002
##     80        1.2570             nan     0.0010    0.0002
##    100        1.2488             nan     0.0010    0.0002
##    120        1.2409             nan     0.0010    0.0002
##    140        1.2329             nan     0.0010    0.0002
##    160        1.2252             nan     0.0010    0.0002
##    180        1.2179             nan     0.0010    0.0002
##    200        1.2108             nan     0.0010    0.0001
##    220        1.2038             nan     0.0010    0.0002
##    240        1.1972             nan     0.0010    0.0001
##    260        1.1907             nan     0.0010    0.0002
##    280        1.1844             nan     0.0010    0.0001
##    300        1.1781             nan     0.0010    0.0001
##    320        1.1721             nan     0.0010    0.0002
##    340        1.1661             nan     0.0010    0.0001
##    360        1.1604             nan     0.0010    0.0001
##    380        1.1549             nan     0.0010    0.0001
##    400        1.1494             nan     0.0010    0.0001
##    420        1.1443             nan     0.0010    0.0001
##    440        1.1391             nan     0.0010    0.0001
##    460        1.1340             nan     0.0010    0.0001
##    480        1.1290             nan     0.0010    0.0001
##    500        1.1243             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2912             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0003
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2894             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2824             nan     0.0010    0.0002
##     40        1.2720             nan     0.0010    0.0002
##     60        1.2614             nan     0.0010    0.0003
##     80        1.2514             nan     0.0010    0.0002
##    100        1.2418             nan     0.0010    0.0002
##    120        1.2324             nan     0.0010    0.0002
##    140        1.2234             nan     0.0010    0.0002
##    160        1.2146             nan     0.0010    0.0002
##    180        1.2061             nan     0.0010    0.0002
##    200        1.1978             nan     0.0010    0.0001
##    220        1.1894             nan     0.0010    0.0002
##    240        1.1820             nan     0.0010    0.0002
##    260        1.1746             nan     0.0010    0.0002
##    280        1.1672             nan     0.0010    0.0002
##    300        1.1600             nan     0.0010    0.0001
##    320        1.1531             nan     0.0010    0.0001
##    340        1.1462             nan     0.0010    0.0001
##    360        1.1397             nan     0.0010    0.0001
##    380        1.1331             nan     0.0010    0.0002
##    400        1.1267             nan     0.0010    0.0001
##    420        1.1204             nan     0.0010    0.0001
##    440        1.1146             nan     0.0010    0.0001
##    460        1.1088             nan     0.0010    0.0001
##    480        1.1034             nan     0.0010    0.0001
##    500        1.0980             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2895             nan     0.0010    0.0003
##      8        1.2890             nan     0.0010    0.0002
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0003
##     20        1.2824             nan     0.0010    0.0002
##     40        1.2719             nan     0.0010    0.0003
##     60        1.2616             nan     0.0010    0.0002
##     80        1.2515             nan     0.0010    0.0002
##    100        1.2419             nan     0.0010    0.0002
##    120        1.2320             nan     0.0010    0.0002
##    140        1.2230             nan     0.0010    0.0002
##    160        1.2144             nan     0.0010    0.0002
##    180        1.2059             nan     0.0010    0.0002
##    200        1.1978             nan     0.0010    0.0002
##    220        1.1896             nan     0.0010    0.0002
##    240        1.1822             nan     0.0010    0.0002
##    260        1.1748             nan     0.0010    0.0002
##    280        1.1674             nan     0.0010    0.0002
##    300        1.1604             nan     0.0010    0.0001
##    320        1.1533             nan     0.0010    0.0001
##    340        1.1464             nan     0.0010    0.0001
##    360        1.1398             nan     0.0010    0.0001
##    380        1.1334             nan     0.0010    0.0001
##    400        1.1273             nan     0.0010    0.0001
##    420        1.1213             nan     0.0010    0.0001
##    440        1.1155             nan     0.0010    0.0001
##    460        1.1096             nan     0.0010    0.0001
##    480        1.1041             nan     0.0010    0.0001
##    500        1.0985             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2916             nan     0.0010    0.0003
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2905             nan     0.0010    0.0003
##      6        1.2899             nan     0.0010    0.0003
##      7        1.2893             nan     0.0010    0.0002
##      8        1.2887             nan     0.0010    0.0002
##      9        1.2882             nan     0.0010    0.0002
##     10        1.2876             nan     0.0010    0.0002
##     20        1.2820             nan     0.0010    0.0003
##     40        1.2715             nan     0.0010    0.0002
##     60        1.2611             nan     0.0010    0.0003
##     80        1.2509             nan     0.0010    0.0002
##    100        1.2411             nan     0.0010    0.0002
##    120        1.2315             nan     0.0010    0.0002
##    140        1.2226             nan     0.0010    0.0002
##    160        1.2139             nan     0.0010    0.0002
##    180        1.2053             nan     0.0010    0.0002
##    200        1.1969             nan     0.0010    0.0002
##    220        1.1889             nan     0.0010    0.0002
##    240        1.1813             nan     0.0010    0.0002
##    260        1.1737             nan     0.0010    0.0002
##    280        1.1664             nan     0.0010    0.0001
##    300        1.1593             nan     0.0010    0.0001
##    320        1.1525             nan     0.0010    0.0001
##    340        1.1458             nan     0.0010    0.0001
##    360        1.1393             nan     0.0010    0.0001
##    380        1.1330             nan     0.0010    0.0002
##    400        1.1270             nan     0.0010    0.0001
##    420        1.1209             nan     0.0010    0.0001
##    440        1.1151             nan     0.0010    0.0001
##    460        1.1093             nan     0.0010    0.0001
##    480        1.1039             nan     0.0010    0.0001
##    500        1.0985             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2608             nan     0.1000    0.0163
##      2        1.2300             nan     0.1000    0.0138
##      3        1.2035             nan     0.1000    0.0120
##      4        1.1821             nan     0.1000    0.0093
##      5        1.1659             nan     0.1000    0.0076
##      6        1.1478             nan     0.1000    0.0077
##      7        1.1344             nan     0.1000    0.0055
##      8        1.1181             nan     0.1000    0.0071
##      9        1.1042             nan     0.1000    0.0066
##     10        1.0902             nan     0.1000    0.0054
##     20        0.9984             nan     0.1000    0.0023
##     40        0.9181             nan     0.1000    0.0004
##     60        0.8754             nan     0.1000    0.0005
##     80        0.8489             nan     0.1000   -0.0009
##    100        0.8305             nan     0.1000   -0.0008
##    120        0.8135             nan     0.1000   -0.0010
##    140        0.8017             nan     0.1000   -0.0009
##    160        0.7896             nan     0.1000   -0.0005
##    180        0.7827             nan     0.1000   -0.0015
##    200        0.7728             nan     0.1000   -0.0007
##    220        0.7630             nan     0.1000   -0.0010
##    240        0.7558             nan     0.1000   -0.0002
##    260        0.7472             nan     0.1000   -0.0014
##    280        0.7395             nan     0.1000   -0.0008
##    300        0.7309             nan     0.1000   -0.0006
##    320        0.7255             nan     0.1000   -0.0004
##    340        0.7190             nan     0.1000   -0.0003
##    360        0.7128             nan     0.1000   -0.0008
##    380        0.7073             nan     0.1000   -0.0001
##    400        0.7025             nan     0.1000   -0.0006
##    420        0.6966             nan     0.1000   -0.0009
##    440        0.6909             nan     0.1000   -0.0010
##    460        0.6853             nan     0.1000   -0.0007
##    480        0.6822             nan     0.1000   -0.0022
##    500        0.6789             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2628             nan     0.1000    0.0143
##      2        1.2303             nan     0.1000    0.0149
##      3        1.2054             nan     0.1000    0.0118
##      4        1.1815             nan     0.1000    0.0098
##      5        1.1639             nan     0.1000    0.0065
##      6        1.1484             nan     0.1000    0.0063
##      7        1.1338             nan     0.1000    0.0061
##      8        1.1168             nan     0.1000    0.0049
##      9        1.1063             nan     0.1000    0.0033
##     10        1.0949             nan     0.1000    0.0046
##     20        1.0037             nan     0.1000    0.0026
##     40        0.9192             nan     0.1000    0.0003
##     60        0.8749             nan     0.1000   -0.0006
##     80        0.8523             nan     0.1000   -0.0015
##    100        0.8337             nan     0.1000   -0.0001
##    120        0.8174             nan     0.1000   -0.0004
##    140        0.8014             nan     0.1000   -0.0003
##    160        0.7902             nan     0.1000   -0.0012
##    180        0.7802             nan     0.1000   -0.0006
##    200        0.7718             nan     0.1000   -0.0006
##    220        0.7644             nan     0.1000   -0.0012
##    240        0.7586             nan     0.1000   -0.0006
##    260        0.7542             nan     0.1000   -0.0014
##    280        0.7460             nan     0.1000   -0.0007
##    300        0.7396             nan     0.1000   -0.0014
##    320        0.7328             nan     0.1000   -0.0005
##    340        0.7259             nan     0.1000   -0.0006
##    360        0.7198             nan     0.1000   -0.0006
##    380        0.7148             nan     0.1000   -0.0006
##    400        0.7089             nan     0.1000   -0.0015
##    420        0.7049             nan     0.1000   -0.0011
##    440        0.7006             nan     0.1000   -0.0011
##    460        0.6944             nan     0.1000   -0.0008
##    480        0.6890             nan     0.1000   -0.0016
##    500        0.6859             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2574             nan     0.1000    0.0166
##      2        1.2253             nan     0.1000    0.0133
##      3        1.2007             nan     0.1000    0.0108
##      4        1.1815             nan     0.1000    0.0073
##      5        1.1603             nan     0.1000    0.0073
##      6        1.1422             nan     0.1000    0.0078
##      7        1.1283             nan     0.1000    0.0074
##      8        1.1127             nan     0.1000    0.0054
##      9        1.0965             nan     0.1000    0.0059
##     10        1.0856             nan     0.1000    0.0045
##     20        1.0010             nan     0.1000    0.0021
##     40        0.9209             nan     0.1000   -0.0003
##     60        0.8805             nan     0.1000    0.0002
##     80        0.8467             nan     0.1000   -0.0013
##    100        0.8289             nan     0.1000   -0.0009
##    120        0.8135             nan     0.1000   -0.0004
##    140        0.8022             nan     0.1000   -0.0009
##    160        0.7918             nan     0.1000   -0.0006
##    180        0.7815             nan     0.1000   -0.0004
##    200        0.7719             nan     0.1000   -0.0007
##    220        0.7614             nan     0.1000   -0.0007
##    240        0.7535             nan     0.1000   -0.0004
##    260        0.7459             nan     0.1000   -0.0007
##    280        0.7397             nan     0.1000   -0.0007
##    300        0.7329             nan     0.1000   -0.0011
##    320        0.7250             nan     0.1000   -0.0011
##    340        0.7193             nan     0.1000   -0.0005
##    360        0.7135             nan     0.1000   -0.0011
##    380        0.7077             nan     0.1000   -0.0006
##    400        0.7025             nan     0.1000   -0.0013
##    420        0.6980             nan     0.1000   -0.0011
##    440        0.6928             nan     0.1000   -0.0015
##    460        0.6874             nan     0.1000   -0.0010
##    480        0.6830             nan     0.1000   -0.0003
##    500        0.6768             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2463             nan     0.1000    0.0195
##      2        1.2079             nan     0.1000    0.0171
##      3        1.1774             nan     0.1000    0.0146
##      4        1.1429             nan     0.1000    0.0115
##      5        1.1214             nan     0.1000    0.0090
##      6        1.1028             nan     0.1000    0.0044
##      7        1.0820             nan     0.1000    0.0057
##      8        1.0634             nan     0.1000    0.0087
##      9        1.0474             nan     0.1000    0.0056
##     10        1.0307             nan     0.1000    0.0063
##     20        0.9393             nan     0.1000    0.0018
##     40        0.8499             nan     0.1000   -0.0000
##     60        0.7997             nan     0.1000   -0.0021
##     80        0.7607             nan     0.1000   -0.0026
##    100        0.7264             nan     0.1000   -0.0008
##    120        0.7039             nan     0.1000   -0.0010
##    140        0.6840             nan     0.1000   -0.0009
##    160        0.6637             nan     0.1000   -0.0017
##    180        0.6442             nan     0.1000   -0.0012
##    200        0.6281             nan     0.1000   -0.0010
##    220        0.6133             nan     0.1000   -0.0019
##    240        0.5944             nan     0.1000   -0.0011
##    260        0.5816             nan     0.1000   -0.0007
##    280        0.5663             nan     0.1000   -0.0008
##    300        0.5507             nan     0.1000   -0.0011
##    320        0.5409             nan     0.1000   -0.0012
##    340        0.5287             nan     0.1000   -0.0009
##    360        0.5153             nan     0.1000   -0.0007
##    380        0.5040             nan     0.1000   -0.0006
##    400        0.4941             nan     0.1000   -0.0005
##    420        0.4830             nan     0.1000   -0.0004
##    440        0.4746             nan     0.1000   -0.0004
##    460        0.4626             nan     0.1000   -0.0003
##    480        0.4545             nan     0.1000   -0.0008
##    500        0.4446             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2467             nan     0.1000    0.0186
##      2        1.2141             nan     0.1000    0.0184
##      3        1.1779             nan     0.1000    0.0135
##      4        1.1468             nan     0.1000    0.0132
##      5        1.1191             nan     0.1000    0.0121
##      6        1.0962             nan     0.1000    0.0092
##      7        1.0747             nan     0.1000    0.0091
##      8        1.0559             nan     0.1000    0.0075
##      9        1.0396             nan     0.1000    0.0078
##     10        1.0275             nan     0.1000    0.0050
##     20        0.9354             nan     0.1000   -0.0007
##     40        0.8454             nan     0.1000    0.0008
##     60        0.7910             nan     0.1000   -0.0006
##     80        0.7603             nan     0.1000   -0.0017
##    100        0.7281             nan     0.1000   -0.0014
##    120        0.7003             nan     0.1000   -0.0006
##    140        0.6771             nan     0.1000   -0.0012
##    160        0.6577             nan     0.1000   -0.0013
##    180        0.6345             nan     0.1000   -0.0011
##    200        0.6159             nan     0.1000   -0.0001
##    220        0.6002             nan     0.1000   -0.0012
##    240        0.5854             nan     0.1000   -0.0009
##    260        0.5707             nan     0.1000   -0.0008
##    280        0.5587             nan     0.1000   -0.0007
##    300        0.5448             nan     0.1000   -0.0008
##    320        0.5344             nan     0.1000   -0.0011
##    340        0.5229             nan     0.1000   -0.0016
##    360        0.5084             nan     0.1000   -0.0014
##    380        0.4961             nan     0.1000   -0.0013
##    400        0.4823             nan     0.1000   -0.0012
##    420        0.4698             nan     0.1000   -0.0004
##    440        0.4610             nan     0.1000   -0.0015
##    460        0.4481             nan     0.1000   -0.0008
##    480        0.4373             nan     0.1000   -0.0010
##    500        0.4239             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2493             nan     0.1000    0.0191
##      2        1.2098             nan     0.1000    0.0191
##      3        1.1752             nan     0.1000    0.0154
##      4        1.1469             nan     0.1000    0.0135
##      5        1.1264             nan     0.1000    0.0055
##      6        1.1038             nan     0.1000    0.0087
##      7        1.0842             nan     0.1000    0.0084
##      8        1.0646             nan     0.1000    0.0073
##      9        1.0463             nan     0.1000    0.0048
##     10        1.0328             nan     0.1000    0.0047
##     20        0.9343             nan     0.1000    0.0010
##     40        0.8384             nan     0.1000   -0.0009
##     60        0.7867             nan     0.1000    0.0011
##     80        0.7527             nan     0.1000   -0.0016
##    100        0.7247             nan     0.1000   -0.0014
##    120        0.6968             nan     0.1000   -0.0009
##    140        0.6762             nan     0.1000   -0.0021
##    160        0.6568             nan     0.1000   -0.0014
##    180        0.6429             nan     0.1000   -0.0033
##    200        0.6242             nan     0.1000   -0.0020
##    220        0.6038             nan     0.1000   -0.0004
##    240        0.5905             nan     0.1000   -0.0009
##    260        0.5735             nan     0.1000   -0.0003
##    280        0.5580             nan     0.1000   -0.0014
##    300        0.5422             nan     0.1000    0.0001
##    320        0.5289             nan     0.1000   -0.0021
##    340        0.5149             nan     0.1000   -0.0002
##    360        0.5015             nan     0.1000   -0.0009
##    380        0.4896             nan     0.1000   -0.0006
##    400        0.4797             nan     0.1000   -0.0017
##    420        0.4693             nan     0.1000   -0.0013
##    440        0.4562             nan     0.1000   -0.0004
##    460        0.4484             nan     0.1000   -0.0018
##    480        0.4403             nan     0.1000   -0.0018
##    500        0.4324             nan     0.1000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2400             nan     0.1000    0.0251
##      2        1.1929             nan     0.1000    0.0212
##      3        1.1507             nan     0.1000    0.0156
##      4        1.1190             nan     0.1000    0.0143
##      5        1.0907             nan     0.1000    0.0112
##      6        1.0663             nan     0.1000    0.0099
##      7        1.0404             nan     0.1000    0.0087
##      8        1.0252             nan     0.1000    0.0057
##      9        1.0083             nan     0.1000    0.0059
##     10        0.9918             nan     0.1000    0.0041
##     20        0.8866             nan     0.1000    0.0014
##     40        0.7962             nan     0.1000   -0.0018
##     60        0.7291             nan     0.1000    0.0005
##     80        0.6874             nan     0.1000   -0.0018
##    100        0.6422             nan     0.1000   -0.0016
##    120        0.5989             nan     0.1000   -0.0002
##    140        0.5735             nan     0.1000   -0.0017
##    160        0.5476             nan     0.1000   -0.0015
##    180        0.5196             nan     0.1000   -0.0013
##    200        0.4972             nan     0.1000   -0.0004
##    220        0.4759             nan     0.1000   -0.0012
##    240        0.4552             nan     0.1000   -0.0007
##    260        0.4352             nan     0.1000   -0.0013
##    280        0.4160             nan     0.1000   -0.0009
##    300        0.3963             nan     0.1000   -0.0012
##    320        0.3786             nan     0.1000   -0.0004
##    340        0.3670             nan     0.1000    0.0001
##    360        0.3545             nan     0.1000   -0.0008
##    380        0.3419             nan     0.1000   -0.0014
##    400        0.3270             nan     0.1000   -0.0014
##    420        0.3134             nan     0.1000   -0.0001
##    440        0.3019             nan     0.1000   -0.0008
##    460        0.2906             nan     0.1000   -0.0015
##    480        0.2790             nan     0.1000   -0.0005
##    500        0.2688             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2421             nan     0.1000    0.0226
##      2        1.1930             nan     0.1000    0.0201
##      3        1.1540             nan     0.1000    0.0166
##      4        1.1230             nan     0.1000    0.0102
##      5        1.0981             nan     0.1000    0.0092
##      6        1.0717             nan     0.1000    0.0112
##      7        1.0494             nan     0.1000    0.0108
##      8        1.0277             nan     0.1000    0.0085
##      9        1.0103             nan     0.1000    0.0063
##     10        0.9952             nan     0.1000    0.0049
##     20        0.8879             nan     0.1000    0.0022
##     40        0.7912             nan     0.1000   -0.0011
##     60        0.7334             nan     0.1000   -0.0004
##     80        0.6863             nan     0.1000   -0.0007
##    100        0.6476             nan     0.1000   -0.0003
##    120        0.6174             nan     0.1000   -0.0010
##    140        0.5836             nan     0.1000   -0.0014
##    160        0.5534             nan     0.1000   -0.0017
##    180        0.5254             nan     0.1000   -0.0007
##    200        0.4969             nan     0.1000   -0.0002
##    220        0.4747             nan     0.1000   -0.0016
##    240        0.4557             nan     0.1000   -0.0014
##    260        0.4356             nan     0.1000   -0.0015
##    280        0.4170             nan     0.1000   -0.0009
##    300        0.3958             nan     0.1000   -0.0005
##    320        0.3814             nan     0.1000   -0.0021
##    340        0.3644             nan     0.1000   -0.0016
##    360        0.3524             nan     0.1000   -0.0010
##    380        0.3375             nan     0.1000   -0.0017
##    400        0.3228             nan     0.1000   -0.0002
##    420        0.3113             nan     0.1000   -0.0006
##    440        0.2986             nan     0.1000   -0.0004
##    460        0.2851             nan     0.1000   -0.0003
##    480        0.2758             nan     0.1000   -0.0006
##    500        0.2664             nan     0.1000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2454             nan     0.1000    0.0221
##      2        1.1984             nan     0.1000    0.0225
##      3        1.1577             nan     0.1000    0.0193
##      4        1.1216             nan     0.1000    0.0163
##      5        1.0923             nan     0.1000    0.0121
##      6        1.0702             nan     0.1000    0.0085
##      7        1.0456             nan     0.1000    0.0086
##      8        1.0243             nan     0.1000    0.0063
##      9        1.0103             nan     0.1000    0.0031
##     10        0.9971             nan     0.1000    0.0030
##     20        0.8904             nan     0.1000    0.0014
##     40        0.7996             nan     0.1000   -0.0019
##     60        0.7390             nan     0.1000   -0.0004
##     80        0.6893             nan     0.1000   -0.0018
##    100        0.6550             nan     0.1000   -0.0001
##    120        0.6254             nan     0.1000    0.0000
##    140        0.5924             nan     0.1000   -0.0010
##    160        0.5652             nan     0.1000   -0.0019
##    180        0.5434             nan     0.1000   -0.0013
##    200        0.5193             nan     0.1000   -0.0007
##    220        0.4923             nan     0.1000   -0.0016
##    240        0.4716             nan     0.1000   -0.0010
##    260        0.4551             nan     0.1000   -0.0006
##    280        0.4366             nan     0.1000   -0.0007
##    300        0.4219             nan     0.1000   -0.0015
##    320        0.4046             nan     0.1000   -0.0005
##    340        0.3899             nan     0.1000   -0.0013
##    360        0.3733             nan     0.1000   -0.0009
##    380        0.3604             nan     0.1000   -0.0012
##    400        0.3448             nan     0.1000   -0.0004
##    420        0.3333             nan     0.1000   -0.0010
##    440        0.3184             nan     0.1000   -0.0005
##    460        0.3064             nan     0.1000   -0.0007
##    480        0.2971             nan     0.1000   -0.0015
##    500        0.2866             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2285             nan     0.2000    0.0291
##      2        1.1802             nan     0.2000    0.0219
##      3        1.1483             nan     0.2000    0.0145
##      4        1.1134             nan     0.2000    0.0149
##      5        1.0831             nan     0.2000    0.0118
##      6        1.0646             nan     0.2000    0.0077
##      7        1.0469             nan     0.2000    0.0074
##      8        1.0326             nan     0.2000    0.0040
##      9        1.0164             nan     0.2000    0.0074
##     10        0.9997             nan     0.2000    0.0063
##     20        0.9185             nan     0.2000   -0.0002
##     40        0.8497             nan     0.2000   -0.0007
##     60        0.8131             nan     0.2000   -0.0004
##     80        0.7920             nan     0.2000   -0.0009
##    100        0.7754             nan     0.2000   -0.0024
##    120        0.7548             nan     0.2000    0.0001
##    140        0.7417             nan     0.2000   -0.0034
##    160        0.7275             nan     0.2000   -0.0015
##    180        0.7181             nan     0.2000   -0.0025
##    200        0.7087             nan     0.2000   -0.0042
##    220        0.6968             nan     0.2000   -0.0024
##    240        0.6830             nan     0.2000   -0.0020
##    260        0.6752             nan     0.2000   -0.0026
##    280        0.6697             nan     0.2000   -0.0018
##    300        0.6612             nan     0.2000   -0.0017
##    320        0.6538             nan     0.2000   -0.0024
##    340        0.6492             nan     0.2000   -0.0020
##    360        0.6426             nan     0.2000   -0.0025
##    380        0.6323             nan     0.2000   -0.0008
##    400        0.6277             nan     0.2000   -0.0017
##    420        0.6211             nan     0.2000   -0.0018
##    440        0.6131             nan     0.2000   -0.0018
##    460        0.6076             nan     0.2000   -0.0013
##    480        0.6043             nan     0.2000   -0.0013
##    500        0.5988             nan     0.2000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2236             nan     0.2000    0.0336
##      2        1.1840             nan     0.2000    0.0179
##      3        1.1454             nan     0.2000    0.0174
##      4        1.1151             nan     0.2000    0.0107
##      5        1.0840             nan     0.2000    0.0102
##      6        1.0608             nan     0.2000    0.0028
##      7        1.0355             nan     0.2000    0.0110
##      8        1.0231             nan     0.2000    0.0039
##      9        1.0099             nan     0.2000    0.0044
##     10        0.9935             nan     0.2000    0.0082
##     20        0.9211             nan     0.2000   -0.0006
##     40        0.8535             nan     0.2000   -0.0014
##     60        0.8169             nan     0.2000   -0.0007
##     80        0.7896             nan     0.2000   -0.0009
##    100        0.7687             nan     0.2000   -0.0011
##    120        0.7533             nan     0.2000   -0.0015
##    140        0.7392             nan     0.2000   -0.0013
##    160        0.7253             nan     0.2000   -0.0007
##    180        0.7155             nan     0.2000   -0.0013
##    200        0.7026             nan     0.2000   -0.0041
##    220        0.6943             nan     0.2000   -0.0007
##    240        0.6836             nan     0.2000   -0.0007
##    260        0.6728             nan     0.2000   -0.0014
##    280        0.6639             nan     0.2000   -0.0012
##    300        0.6578             nan     0.2000   -0.0014
##    320        0.6476             nan     0.2000   -0.0021
##    340        0.6364             nan     0.2000   -0.0019
##    360        0.6279             nan     0.2000   -0.0007
##    380        0.6224             nan     0.2000   -0.0005
##    400        0.6176             nan     0.2000   -0.0016
##    420        0.6086             nan     0.2000   -0.0016
##    440        0.6058             nan     0.2000   -0.0020
##    460        0.6017             nan     0.2000   -0.0036
##    480        0.5976             nan     0.2000   -0.0014
##    500        0.5928             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2233             nan     0.2000    0.0324
##      2        1.1773             nan     0.2000    0.0185
##      3        1.1422             nan     0.2000    0.0141
##      4        1.1127             nan     0.2000    0.0128
##      5        1.0835             nan     0.2000    0.0124
##      6        1.0596             nan     0.2000    0.0086
##      7        1.0412             nan     0.2000    0.0067
##      8        1.0204             nan     0.2000    0.0055
##      9        1.0063             nan     0.2000    0.0036
##     10        0.9918             nan     0.2000    0.0053
##     20        0.9140             nan     0.2000    0.0006
##     40        0.8462             nan     0.2000   -0.0051
##     60        0.8153             nan     0.2000    0.0004
##     80        0.7890             nan     0.2000   -0.0017
##    100        0.7716             nan     0.2000   -0.0022
##    120        0.7559             nan     0.2000   -0.0023
##    140        0.7441             nan     0.2000   -0.0051
##    160        0.7331             nan     0.2000   -0.0015
##    180        0.7217             nan     0.2000   -0.0025
##    200        0.7066             nan     0.2000   -0.0006
##    220        0.6947             nan     0.2000   -0.0010
##    240        0.6843             nan     0.2000   -0.0028
##    260        0.6776             nan     0.2000   -0.0015
##    280        0.6673             nan     0.2000   -0.0019
##    300        0.6617             nan     0.2000   -0.0024
##    320        0.6527             nan     0.2000   -0.0021
##    340        0.6438             nan     0.2000   -0.0015
##    360        0.6389             nan     0.2000   -0.0010
##    380        0.6294             nan     0.2000   -0.0012
##    400        0.6259             nan     0.2000   -0.0030
##    420        0.6176             nan     0.2000   -0.0022
##    440        0.6106             nan     0.2000   -0.0018
##    460        0.6034             nan     0.2000   -0.0017
##    480        0.5978             nan     0.2000   -0.0009
##    500        0.5944             nan     0.2000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2139             nan     0.2000    0.0406
##      2        1.1530             nan     0.2000    0.0323
##      3        1.1056             nan     0.2000    0.0213
##      4        1.0613             nan     0.2000    0.0175
##      5        1.0259             nan     0.2000    0.0156
##      6        1.0005             nan     0.2000    0.0100
##      7        0.9772             nan     0.2000    0.0074
##      8        0.9601             nan     0.2000    0.0001
##      9        0.9410             nan     0.2000    0.0078
##     10        0.9296             nan     0.2000    0.0028
##     20        0.8449             nan     0.2000   -0.0022
##     40        0.7566             nan     0.2000   -0.0053
##     60        0.7066             nan     0.2000   -0.0004
##     80        0.6730             nan     0.2000   -0.0012
##    100        0.6352             nan     0.2000   -0.0013
##    120        0.5953             nan     0.2000   -0.0044
##    140        0.5674             nan     0.2000   -0.0038
##    160        0.5321             nan     0.2000   -0.0038
##    180        0.5106             nan     0.2000   -0.0015
##    200        0.4913             nan     0.2000   -0.0040
##    220        0.4709             nan     0.2000   -0.0020
##    240        0.4546             nan     0.2000   -0.0032
##    260        0.4304             nan     0.2000   -0.0018
##    280        0.4077             nan     0.2000   -0.0026
##    300        0.3856             nan     0.2000   -0.0014
##    320        0.3736             nan     0.2000   -0.0082
##    340        0.3571             nan     0.2000   -0.0010
##    360        0.3435             nan     0.2000   -0.0015
##    380        0.3329             nan     0.2000   -0.0006
##    400        0.3161             nan     0.2000   -0.0017
##    420        0.3049             nan     0.2000   -0.0013
##    440        0.2966             nan     0.2000   -0.0003
##    460        0.2883             nan     0.2000   -0.0019
##    480        0.2769             nan     0.2000   -0.0012
##    500        0.2688             nan     0.2000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2035             nan     0.2000    0.0354
##      2        1.1599             nan     0.2000    0.0133
##      3        1.1006             nan     0.2000    0.0221
##      4        1.0551             nan     0.2000    0.0234
##      5        1.0191             nan     0.2000    0.0141
##      6        0.9954             nan     0.2000    0.0062
##      7        0.9719             nan     0.2000    0.0066
##      8        0.9559             nan     0.2000    0.0027
##      9        0.9449             nan     0.2000   -0.0013
##     10        0.9291             nan     0.2000    0.0025
##     20        0.8563             nan     0.2000   -0.0009
##     40        0.7731             nan     0.2000   -0.0026
##     60        0.7218             nan     0.2000   -0.0024
##     80        0.6846             nan     0.2000   -0.0024
##    100        0.6552             nan     0.2000   -0.0030
##    120        0.6202             nan     0.2000   -0.0014
##    140        0.5937             nan     0.2000   -0.0029
##    160        0.5586             nan     0.2000   -0.0006
##    180        0.5328             nan     0.2000   -0.0046
##    200        0.5101             nan     0.2000   -0.0026
##    220        0.4829             nan     0.2000   -0.0026
##    240        0.4660             nan     0.2000   -0.0036
##    260        0.4451             nan     0.2000   -0.0021
##    280        0.4218             nan     0.2000   -0.0008
##    300        0.4029             nan     0.2000   -0.0008
##    320        0.3915             nan     0.2000   -0.0010
##    340        0.3724             nan     0.2000   -0.0035
##    360        0.3586             nan     0.2000   -0.0009
##    380        0.3445             nan     0.2000   -0.0022
##    400        0.3298             nan     0.2000   -0.0018
##    420        0.3160             nan     0.2000   -0.0004
##    440        0.3045             nan     0.2000   -0.0006
##    460        0.2897             nan     0.2000   -0.0024
##    480        0.2780             nan     0.2000   -0.0015
##    500        0.2683             nan     0.2000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2152             nan     0.2000    0.0387
##      2        1.1448             nan     0.2000    0.0295
##      3        1.0845             nan     0.2000    0.0232
##      4        1.0530             nan     0.2000    0.0084
##      5        1.0247             nan     0.2000    0.0114
##      6        0.9996             nan     0.2000    0.0074
##      7        0.9737             nan     0.2000    0.0086
##      8        0.9603             nan     0.2000    0.0041
##      9        0.9423             nan     0.2000    0.0052
##     10        0.9318             nan     0.2000    0.0005
##     20        0.8555             nan     0.2000   -0.0035
##     40        0.7749             nan     0.2000   -0.0018
##     60        0.7203             nan     0.2000   -0.0006
##     80        0.6751             nan     0.2000   -0.0012
##    100        0.6488             nan     0.2000   -0.0025
##    120        0.6036             nan     0.2000   -0.0004
##    140        0.5764             nan     0.2000   -0.0006
##    160        0.5448             nan     0.2000   -0.0017
##    180        0.5239             nan     0.2000   -0.0016
##    200        0.4962             nan     0.2000   -0.0005
##    220        0.4752             nan     0.2000   -0.0010
##    240        0.4555             nan     0.2000   -0.0021
##    260        0.4393             nan     0.2000   -0.0024
##    280        0.4205             nan     0.2000   -0.0032
##    300        0.3983             nan     0.2000   -0.0019
##    320        0.3845             nan     0.2000   -0.0010
##    340        0.3645             nan     0.2000   -0.0010
##    360        0.3497             nan     0.2000   -0.0024
##    380        0.3367             nan     0.2000   -0.0002
##    400        0.3231             nan     0.2000   -0.0017
##    420        0.3099             nan     0.2000   -0.0002
##    440        0.2985             nan     0.2000   -0.0018
##    460        0.2887             nan     0.2000   -0.0011
##    480        0.2806             nan     0.2000   -0.0013
##    500        0.2715             nan     0.2000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1912             nan     0.2000    0.0470
##      2        1.1249             nan     0.2000    0.0281
##      3        1.0784             nan     0.2000    0.0182
##      4        1.0365             nan     0.2000    0.0150
##      5        1.0053             nan     0.2000    0.0090
##      6        0.9773             nan     0.2000    0.0055
##      7        0.9523             nan     0.2000    0.0111
##      8        0.9308             nan     0.2000    0.0042
##      9        0.9082             nan     0.2000    0.0017
##     10        0.8914             nan     0.2000    0.0004
##     20        0.8001             nan     0.2000   -0.0030
##     40        0.6976             nan     0.2000   -0.0023
##     60        0.6315             nan     0.2000   -0.0026
##     80        0.5743             nan     0.2000   -0.0070
##    100        0.5217             nan     0.2000   -0.0013
##    120        0.4841             nan     0.2000    0.0004
##    140        0.4456             nan     0.2000   -0.0018
##    160        0.4113             nan     0.2000   -0.0019
##    180        0.3706             nan     0.2000   -0.0013
##    200        0.3437             nan     0.2000   -0.0003
##    220        0.3172             nan     0.2000   -0.0006
##    240        0.2928             nan     0.2000   -0.0032
##    260        0.2705             nan     0.2000   -0.0003
##    280        0.2519             nan     0.2000   -0.0018
##    300        0.2354             nan     0.2000   -0.0011
##    320        0.2226             nan     0.2000   -0.0021
##    340        0.2082             nan     0.2000   -0.0009
##    360        0.1945             nan     0.2000   -0.0010
##    380        0.1803             nan     0.2000   -0.0017
##    400        0.1680             nan     0.2000   -0.0013
##    420        0.1589             nan     0.2000   -0.0012
##    440        0.1501             nan     0.2000   -0.0004
##    460        0.1419             nan     0.2000   -0.0005
##    480        0.1339             nan     0.2000   -0.0008
##    500        0.1266             nan     0.2000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1947             nan     0.2000    0.0467
##      2        1.1163             nan     0.2000    0.0339
##      3        1.0673             nan     0.2000    0.0163
##      4        1.0235             nan     0.2000    0.0190
##      5        0.9970             nan     0.2000    0.0056
##      6        0.9642             nan     0.2000    0.0090
##      7        0.9456             nan     0.2000    0.0033
##      8        0.9260             nan     0.2000    0.0061
##      9        0.9094             nan     0.2000    0.0017
##     10        0.8917             nan     0.2000    0.0042
##     20        0.7991             nan     0.2000   -0.0014
##     40        0.7177             nan     0.2000   -0.0041
##     60        0.6374             nan     0.2000   -0.0021
##     80        0.5658             nan     0.2000   -0.0032
##    100        0.5171             nan     0.2000   -0.0032
##    120        0.4899             nan     0.2000   -0.0185
##    140        0.4383             nan     0.2000   -0.0018
##    160        0.4069             nan     0.2000   -0.0021
##    180        0.3727             nan     0.2000   -0.0011
##    200        0.3363             nan     0.2000   -0.0009
##    220        0.3122             nan     0.2000   -0.0026
##    240        0.2942             nan     0.2000   -0.0024
##    260        0.2712             nan     0.2000   -0.0010
##    280        0.2533             nan     0.2000   -0.0009
##    300        0.2336             nan     0.2000   -0.0014
##    320        0.2182             nan     0.2000   -0.0005
##    340        0.2028             nan     0.2000   -0.0013
##    360        0.1876             nan     0.2000   -0.0008
##    380        0.1735             nan     0.2000   -0.0007
##    400        0.1654             nan     0.2000   -0.0007
##    420        0.1544             nan     0.2000   -0.0010
##    440        0.1462             nan     0.2000   -0.0006
##    460        0.1374             nan     0.2000   -0.0011
##    480        0.1298             nan     0.2000   -0.0003
##    500        0.1223             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2019             nan     0.2000    0.0398
##      2        1.1325             nan     0.2000    0.0250
##      3        1.0793             nan     0.2000    0.0235
##      4        1.0426             nan     0.2000    0.0116
##      5        1.0066             nan     0.2000    0.0126
##      6        0.9795             nan     0.2000    0.0102
##      7        0.9534             nan     0.2000    0.0091
##      8        0.9344             nan     0.2000    0.0044
##      9        0.9120             nan     0.2000    0.0025
##     10        0.8986             nan     0.2000   -0.0034
##     20        0.8043             nan     0.2000   -0.0064
##     40        0.7004             nan     0.2000   -0.0008
##     60        0.6314             nan     0.2000   -0.0039
##     80        0.5639             nan     0.2000   -0.0015
##    100        0.5097             nan     0.2000   -0.0026
##    120        0.4724             nan     0.2000   -0.0038
##    140        0.4288             nan     0.2000   -0.0033
##    160        0.3952             nan     0.2000   -0.0023
##    180        0.3637             nan     0.2000   -0.0013
##    200        0.3389             nan     0.2000   -0.0022
##    220        0.3130             nan     0.2000   -0.0007
##    240        0.2914             nan     0.2000   -0.0023
##    260        0.2680             nan     0.2000   -0.0005
##    280        0.2470             nan     0.2000   -0.0009
##    300        0.2313             nan     0.2000   -0.0023
##    320        0.2157             nan     0.2000   -0.0019
##    340        0.2005             nan     0.2000   -0.0015
##    360        0.1880             nan     0.2000   -0.0008
##    380        0.1757             nan     0.2000   -0.0007
##    400        0.1648             nan     0.2000   -0.0012
##    420        0.1543             nan     0.2000   -0.0006
##    440        0.1467             nan     0.2000   -0.0007
##    460        0.1389             nan     0.2000   -0.0009
##    480        0.1308             nan     0.2000   -0.0005
##    500        0.1252             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2011             nan     0.3000    0.0408
##      2        1.1300             nan     0.3000    0.0267
##      3        1.0925             nan     0.3000    0.0158
##      4        1.0548             nan     0.3000    0.0185
##      5        1.0236             nan     0.3000    0.0113
##      6        1.0055             nan     0.3000    0.0058
##      7        0.9860             nan     0.3000    0.0056
##      8        0.9718             nan     0.3000    0.0025
##      9        0.9492             nan     0.3000    0.0070
##     10        0.9402             nan     0.3000    0.0031
##     20        0.8734             nan     0.3000   -0.0008
##     40        0.8245             nan     0.3000    0.0007
##     60        0.7895             nan     0.3000   -0.0032
##     80        0.7557             nan     0.3000   -0.0047
##    100        0.7316             nan     0.3000   -0.0041
##    120        0.7093             nan     0.3000   -0.0008
##    140        0.6885             nan     0.3000   -0.0016
##    160        0.6724             nan     0.3000   -0.0000
##    180        0.6596             nan     0.3000   -0.0016
##    200        0.6531             nan     0.3000   -0.0042
##    220        0.6408             nan     0.3000   -0.0018
##    240        0.6241             nan     0.3000   -0.0012
##    260        0.6134             nan     0.3000   -0.0029
##    280        0.6058             nan     0.3000   -0.0023
##    300        0.6003             nan     0.3000   -0.0024
##    320        0.5935             nan     0.3000   -0.0035
##    340        0.5833             nan     0.3000   -0.0047
##    360        0.5763             nan     0.3000   -0.0049
##    380        0.5701             nan     0.3000   -0.0029
##    400        0.5587             nan     0.3000   -0.0037
##    420        0.5565             nan     0.3000   -0.0029
##    440        0.5451             nan     0.3000   -0.0026
##    460        0.5392             nan     0.3000   -0.0016
##    480        0.5329             nan     0.3000   -0.0013
##    500        0.5230             nan     0.3000   -0.0040
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2021             nan     0.3000    0.0471
##      2        1.1384             nan     0.3000    0.0278
##      3        1.0970             nan     0.3000    0.0196
##      4        1.0734             nan     0.3000    0.0071
##      5        1.0356             nan     0.3000    0.0163
##      6        1.0105             nan     0.3000    0.0013
##      7        0.9944             nan     0.3000    0.0024
##      8        0.9713             nan     0.3000    0.0110
##      9        0.9566             nan     0.3000    0.0036
##     10        0.9461             nan     0.3000   -0.0010
##     20        0.8739             nan     0.3000   -0.0025
##     40        0.8269             nan     0.3000   -0.0080
##     60        0.7994             nan     0.3000   -0.0033
##     80        0.7705             nan     0.3000   -0.0016
##    100        0.7485             nan     0.3000   -0.0044
##    120        0.7336             nan     0.3000   -0.0027
##    140        0.7130             nan     0.3000   -0.0044
##    160        0.6997             nan     0.3000   -0.0043
##    180        0.6834             nan     0.3000   -0.0036
##    200        0.6729             nan     0.3000   -0.0017
##    220        0.6619             nan     0.3000   -0.0030
##    240        0.6476             nan     0.3000   -0.0039
##    260        0.6348             nan     0.3000   -0.0016
##    280        0.6248             nan     0.3000   -0.0035
##    300        0.6116             nan     0.3000   -0.0009
##    320        0.6055             nan     0.3000   -0.0040
##    340        0.5983             nan     0.3000   -0.0019
##    360        0.5910             nan     0.3000   -0.0016
##    380        0.5858             nan     0.3000   -0.0026
##    400        0.5723             nan     0.3000   -0.0020
##    420        0.5673             nan     0.3000   -0.0011
##    440        0.5635             nan     0.3000   -0.0029
##    460        0.5527             nan     0.3000   -0.0034
##    480        0.5454             nan     0.3000   -0.0058
##    500        0.5356             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2025             nan     0.3000    0.0430
##      2        1.1464             nan     0.3000    0.0273
##      3        1.1076             nan     0.3000    0.0202
##      4        1.0679             nan     0.3000    0.0168
##      5        1.0368             nan     0.3000    0.0178
##      6        1.0115             nan     0.3000    0.0097
##      7        0.9925             nan     0.3000    0.0034
##      8        0.9853             nan     0.3000   -0.0023
##      9        0.9738             nan     0.3000   -0.0000
##     10        0.9591             nan     0.3000    0.0034
##     20        0.8935             nan     0.3000   -0.0005
##     40        0.8362             nan     0.3000   -0.0001
##     60        0.8042             nan     0.3000   -0.0064
##     80        0.7730             nan     0.3000   -0.0018
##    100        0.7471             nan     0.3000   -0.0009
##    120        0.7319             nan     0.3000   -0.0037
##    140        0.7164             nan     0.3000   -0.0021
##    160        0.7081             nan     0.3000   -0.0043
##    180        0.6981             nan     0.3000   -0.0059
##    200        0.6853             nan     0.3000   -0.0039
##    220        0.6709             nan     0.3000   -0.0011
##    240        0.6558             nan     0.3000   -0.0016
##    260        0.6470             nan     0.3000   -0.0028
##    280        0.6342             nan     0.3000   -0.0016
##    300        0.6246             nan     0.3000   -0.0011
##    320        0.6126             nan     0.3000   -0.0015
##    340        0.6035             nan     0.3000   -0.0023
##    360        0.5995             nan     0.3000   -0.0004
##    380        0.5908             nan     0.3000   -0.0029
##    400        0.5851             nan     0.3000   -0.0009
##    420        0.5771             nan     0.3000   -0.0032
##    440        0.5687             nan     0.3000   -0.0036
##    460        0.5556             nan     0.3000   -0.0010
##    480        0.5518             nan     0.3000   -0.0046
##    500        0.5485             nan     0.3000   -0.0039
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1783             nan     0.3000    0.0578
##      2        1.0957             nan     0.3000    0.0384
##      3        1.0592             nan     0.3000    0.0131
##      4        1.0126             nan     0.3000    0.0196
##      5        0.9794             nan     0.3000    0.0086
##      6        0.9483             nan     0.3000    0.0119
##      7        0.9279             nan     0.3000    0.0021
##      8        0.9130             nan     0.3000    0.0012
##      9        0.9025             nan     0.3000    0.0000
##     10        0.8888             nan     0.3000    0.0034
##     20        0.8144             nan     0.3000   -0.0029
##     40        0.7325             nan     0.3000   -0.0060
##     60        0.6673             nan     0.3000   -0.0015
##     80        0.6107             nan     0.3000   -0.0004
##    100        0.5743             nan     0.3000   -0.0026
##    120        0.5275             nan     0.3000   -0.0025
##    140        0.4907             nan     0.3000   -0.0010
##    160        0.4604             nan     0.3000   -0.0013
##    180        0.4312             nan     0.3000   -0.0042
##    200        0.3976             nan     0.3000   -0.0002
##    220        0.3727             nan     0.3000   -0.0016
##    240        0.3484             nan     0.3000   -0.0025
##    260        0.3281             nan     0.3000   -0.0021
##    280        0.3064             nan     0.3000   -0.0020
##    300        0.2921             nan     0.3000   -0.0035
##    320        0.2747             nan     0.3000   -0.0024
##    340        0.2554             nan     0.3000   -0.0006
##    360        0.2459             nan     0.3000   -0.0019
##    380        0.2347             nan     0.3000   -0.0030
##    400        0.2197             nan     0.3000   -0.0011
##    420        0.2088             nan     0.3000   -0.0015
##    440        0.2005             nan     0.3000   -0.0028
##    460        0.1908             nan     0.3000   -0.0029
##    480        0.1820             nan     0.3000   -0.0016
##    500        0.1742             nan     0.3000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1721             nan     0.3000    0.0561
##      2        1.1001             nan     0.3000    0.0339
##      3        1.0488             nan     0.3000    0.0199
##      4        1.0114             nan     0.3000    0.0154
##      5        0.9811             nan     0.3000    0.0117
##      6        0.9509             nan     0.3000    0.0081
##      7        0.9337             nan     0.3000    0.0031
##      8        0.9188             nan     0.3000    0.0010
##      9        0.9075             nan     0.3000   -0.0014
##     10        0.8922             nan     0.3000    0.0014
##     20        0.8198             nan     0.3000   -0.0090
##     40        0.7218             nan     0.3000   -0.0037
##     60        0.6614             nan     0.3000   -0.0016
##     80        0.6051             nan     0.3000   -0.0061
##    100        0.5681             nan     0.3000   -0.0034
##    120        0.5269             nan     0.3000   -0.0053
##    140        0.4912             nan     0.3000   -0.0043
##    160        0.4617             nan     0.3000   -0.0023
##    180           inf             nan     0.3000       nan
##    200           inf             nan     0.3000       nan
##    220           inf             nan     0.3000       nan
##    240           inf             nan     0.3000       nan
##    260           inf             nan     0.3000       nan
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1780             nan     0.3000    0.0546
##      2        1.1037             nan     0.3000    0.0231
##      3        1.0389             nan     0.3000    0.0217
##      4        1.0025             nan     0.3000    0.0099
##      5        0.9714             nan     0.3000    0.0129
##      6        0.9519             nan     0.3000    0.0005
##      7        0.9328             nan     0.3000    0.0014
##      8        0.9226             nan     0.3000   -0.0044
##      9        0.9086             nan     0.3000    0.0050
##     10        0.8963             nan     0.3000    0.0041
##     20        0.8107             nan     0.3000   -0.0048
##     40        0.7383             nan     0.3000   -0.0033
##     60        0.6853             nan     0.3000   -0.0034
##     80        0.6312             nan     0.3000   -0.0048
##    100        0.5762             nan     0.3000   -0.0030
##    120        0.5425             nan     0.3000   -0.0004
##    140        0.4985             nan     0.3000   -0.0048
##    160        0.4646             nan     0.3000   -0.0012
##    180        0.4401             nan     0.3000   -0.0021
##    200        0.4113             nan     0.3000   -0.0042
##    220        0.3815             nan     0.3000   -0.0004
##    240        0.3532             nan     0.3000   -0.0016
##    260        0.3343             nan     0.3000   -0.0032
##    280        0.3170             nan     0.3000   -0.0040
##    300        0.3005             nan     0.3000   -0.0016
##    320        0.2806             nan     0.3000   -0.0012
##    340        0.2669             nan     0.3000   -0.0017
##    360        0.2554             nan     0.3000   -0.0014
##    380        0.2430             nan     0.3000   -0.0009
##    400        0.2364             nan     0.3000   -0.0019
##    420        0.2233             nan     0.3000   -0.0001
##    440        0.2132             nan     0.3000   -0.0014
##    460        0.2050             nan     0.3000   -0.0013
##    480        0.1956             nan     0.3000   -0.0014
##    500        0.1868             nan     0.3000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1635             nan     0.3000    0.0500
##      2        1.0794             nan     0.3000    0.0362
##      3        1.0094             nan     0.3000    0.0218
##      4        0.9675             nan     0.3000    0.0122
##      5        0.9388             nan     0.3000    0.0085
##      6        0.9068             nan     0.3000    0.0089
##      7        0.8885             nan     0.3000    0.0007
##      8        0.8734             nan     0.3000    0.0001
##      9        0.8682             nan     0.3000   -0.0079
##     10        0.8499             nan     0.3000    0.0018
##     20        0.7489             nan     0.3000    0.0002
##     40        0.6421             nan     0.3000   -0.0006
##     60        0.5632             nan     0.3000   -0.0012
##     80        0.4965             nan     0.3000   -0.0047
##    100        0.4300             nan     0.3000   -0.0022
##    120        0.3789             nan     0.3000    0.0009
##    140        0.3333             nan     0.3000   -0.0032
##    160           inf             nan     0.3000       nan
##    180           inf             nan     0.3000       nan
##    200           inf             nan     0.3000       nan
##    220           inf             nan     0.3000       nan
##    240           inf             nan     0.3000       nan
##    260           inf             nan     0.3000       nan
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1598             nan     0.3000    0.0590
##      2        1.0647             nan     0.3000    0.0433
##      3        1.0172             nan     0.3000    0.0152
##      4        0.9676             nan     0.3000    0.0191
##      5        0.9494             nan     0.3000    0.0009
##      6        0.9171             nan     0.3000    0.0083
##      7        0.8946             nan     0.3000    0.0023
##      8        0.8757             nan     0.3000    0.0016
##      9        0.8581             nan     0.3000    0.0035
##     10        0.8431             nan     0.3000    0.0002
##     20        0.7489             nan     0.3000    0.0006
##     40        0.6525             nan     0.3000   -0.0046
##     60        0.5845             nan     0.3000   -0.0047
##     80        0.5050             nan     0.3000   -0.0042
##    100        0.4504             nan     0.3000   -0.0031
##    120        0.4065             nan     0.3000   -0.0041
##    140        0.3499             nan     0.3000   -0.0048
##    160        0.3180             nan     0.3000   -0.0011
##    180        0.2839             nan     0.3000   -0.0015
##    200        0.2529             nan     0.3000   -0.0006
##    220        0.2228             nan     0.3000   -0.0013
##    240        0.2005             nan     0.3000   -0.0016
##    260        0.1834             nan     0.3000   -0.0013
##    280        0.1641             nan     0.3000   -0.0013
##    300        0.1502             nan     0.3000   -0.0011
##    320        0.1375             nan     0.3000   -0.0009
##    340        0.1273             nan     0.3000   -0.0021
##    360        0.1176             nan     0.3000   -0.0005
##    380        0.1076             nan     0.3000   -0.0003
##    400        0.1005             nan     0.3000   -0.0003
##    420        0.0917             nan     0.3000   -0.0010
##    440        0.0834             nan     0.3000   -0.0009
##    460        0.0765             nan     0.3000   -0.0002
##    480        0.0709             nan     0.3000   -0.0003
##    500        0.0665             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1643             nan     0.3000    0.0501
##      2        1.0778             nan     0.3000    0.0430
##      3        1.0167             nan     0.3000    0.0214
##      4        0.9707             nan     0.3000    0.0139
##      5        0.9321             nan     0.3000    0.0135
##      6        0.9130             nan     0.3000   -0.0013
##      7        0.8912             nan     0.3000    0.0005
##      8        0.8762             nan     0.3000   -0.0036
##      9        0.8657             nan     0.3000   -0.0027
##     10        0.8558             nan     0.3000   -0.0016
##     20        0.7512             nan     0.3000    0.0015
##     40        0.6287             nan     0.3000   -0.0060
##     60        0.5382             nan     0.3000   -0.0058
##     80        0.4768             nan     0.3000   -0.0038
##    100        0.4251             nan     0.3000   -0.0037
##    120        0.3719             nan     0.3000   -0.0026
##    140        0.3252             nan     0.3000   -0.0023
##    160        0.2907             nan     0.3000   -0.0044
##    180        0.2664             nan     0.3000   -0.0011
##    200        0.2329             nan     0.3000   -0.0010
##    220        0.2086             nan     0.3000   -0.0024
##    240        0.1870             nan     0.3000   -0.0009
##    260        0.1684             nan     0.3000   -0.0033
##    280        0.1557             nan     0.3000   -0.0016
##    300        0.1409             nan     0.3000   -0.0004
##    320        0.1280             nan     0.3000   -0.0006
##    340        0.1183             nan     0.3000   -0.0008
##    360        0.1082             nan     0.3000   -0.0010
##    380        0.0988             nan     0.3000   -0.0009
##    400        0.0923             nan     0.3000   -0.0011
##    420        0.0843             nan     0.3000   -0.0004
##    440        0.0757             nan     0.3000   -0.0010
##    460        0.0699             nan     0.3000   -0.0011
##    480        0.0637             nan     0.3000   -0.0004
##    500        0.0587             nan     0.3000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1699             nan     0.5000    0.0560
##      2        1.0832             nan     0.5000    0.0308
##      3        1.0296             nan     0.5000    0.0263
##      4        0.9894             nan     0.5000    0.0201
##      5        0.9692             nan     0.5000    0.0066
##      6        0.9549             nan     0.5000   -0.0018
##      7        0.9378             nan     0.5000    0.0042
##      8        0.9181             nan     0.5000    0.0052
##      9        0.9089             nan     0.5000   -0.0022
##     10        0.8969             nan     0.5000   -0.0032
##     20        0.8603             nan     0.5000   -0.0024
##     40        0.7902             nan     0.5000   -0.0018
##     60        0.7526             nan     0.5000   -0.0043
##     80        0.7106             nan     0.5000   -0.0024
##    100        0.6887             nan     0.5000   -0.0031
##    120        0.6694             nan     0.5000   -0.0036
##    140        0.6579             nan     0.5000   -0.0066
##    160        0.6347             nan     0.5000   -0.0059
##    180        0.6270             nan     0.5000   -0.0037
##    200        0.6063             nan     0.5000   -0.0019
##    220        0.5882             nan     0.5000   -0.0042
##    240        0.5843             nan     0.5000   -0.0078
##    260        0.5638             nan     0.5000   -0.0025
##    280        0.5554             nan     0.5000   -0.0040
##    300        0.5545             nan     0.5000   -0.0144
##    320        0.5426             nan     0.5000   -0.0108
##    340        0.5266             nan     0.5000   -0.0044
##    360        0.5196             nan     0.5000   -0.0019
##    380        0.5066             nan     0.5000   -0.0033
##    400        0.4958             nan     0.5000   -0.0048
##    420        0.4920             nan     0.5000   -0.0019
##    440        0.4831             nan     0.5000   -0.0043
##    460        0.4770             nan     0.5000    0.0016
##    480        0.4749             nan     0.5000   -0.0072
##    500        0.4680             nan     0.5000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1702             nan     0.5000    0.0629
##      2        1.0888             nan     0.5000    0.0308
##      3        1.0306             nan     0.5000    0.0151
##      4        0.9859             nan     0.5000    0.0151
##      5        0.9723             nan     0.5000    0.0008
##      6        0.9670             nan     0.5000   -0.0071
##      7        0.9447             nan     0.5000    0.0086
##      8        0.9323             nan     0.5000    0.0017
##      9        0.9159             nan     0.5000    0.0042
##     10        0.9111             nan     0.5000   -0.0035
##     20        0.8388             nan     0.5000    0.0021
##     40        0.7784             nan     0.5000   -0.0049
##     60        0.7377             nan     0.5000   -0.0011
##     80        0.7213             nan     0.5000   -0.0067
##    100        0.7015             nan     0.5000   -0.0075
##    120        0.6812             nan     0.5000   -0.0067
##    140        0.6426             nan     0.5000   -0.0074
##    160        0.6345             nan     0.5000   -0.0002
##    180        0.6227             nan     0.5000   -0.0015
##    200        0.6150             nan     0.5000   -0.0083
##    220        0.5945             nan     0.5000   -0.0067
##    240        0.5828             nan     0.5000   -0.0012
##    260        0.5731             nan     0.5000   -0.0047
##    280        0.5547             nan     0.5000   -0.0031
##    300        0.5512             nan     0.5000   -0.0032
##    320        0.5410             nan     0.5000   -0.0023
##    340        0.5276             nan     0.5000   -0.0003
##    360        0.5272             nan     0.5000   -0.0010
##    380        0.5113             nan     0.5000   -0.0018
##    400        0.5040             nan     0.5000   -0.0041
##    420        0.4924             nan     0.5000   -0.0018
##    440        0.4924             nan     0.5000   -0.0060
##    460        0.4747             nan     0.5000   -0.0028
##    480        0.4757             nan     0.5000   -0.0076
##    500        0.4643             nan     0.5000   -0.0033
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1581             nan     0.5000    0.0590
##      2        1.0832             nan     0.5000    0.0264
##      3        1.0312             nan     0.5000    0.0225
##      4        0.9854             nan     0.5000    0.0214
##      5        0.9687             nan     0.5000    0.0023
##      6        0.9570             nan     0.5000   -0.0016
##      7        0.9408             nan     0.5000    0.0025
##      8        0.9202             nan     0.5000    0.0036
##      9        0.9082             nan     0.5000   -0.0043
##     10        0.9022             nan     0.5000   -0.0023
##     20        0.8342             nan     0.5000    0.0017
##     40        0.7689             nan     0.5000   -0.0065
##     60        0.7409             nan     0.5000   -0.0112
##     80        0.7146             nan     0.5000   -0.0087
##    100        0.6914             nan     0.5000   -0.0027
##    120        0.6804             nan     0.5000   -0.0046
##    140        0.6550             nan     0.5000   -0.0036
##    160        0.6436             nan     0.5000   -0.0042
##    180        0.6309             nan     0.5000   -0.0079
##    200        0.6118             nan     0.5000   -0.0054
##    220        0.5965             nan     0.5000   -0.0058
##    240        0.5768             nan     0.5000   -0.0048
##    260        0.5631             nan     0.5000   -0.0035
##    280        0.5564             nan     0.5000   -0.0019
##    300        0.5459             nan     0.5000   -0.0049
##    320        0.5316             nan     0.5000   -0.0061
##    340        0.5248             nan     0.5000   -0.0025
##    360        0.5083             nan     0.5000   -0.0018
##    380        0.5047             nan     0.5000   -0.0036
##    400        0.4969             nan     0.5000   -0.0038
##    420        0.4958             nan     0.5000   -0.0093
##    440        0.4775             nan     0.5000   -0.0045
##    460        0.4643             nan     0.5000   -0.0030
##    480        0.4605             nan     0.5000   -0.0018
##    500        0.4496             nan     0.5000   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1334             nan     0.5000    0.0602
##      2        1.0439             nan     0.5000    0.0274
##      3        0.9764             nan     0.5000    0.0315
##      4        0.9490             nan     0.5000    0.0025
##      5        0.9398             nan     0.5000   -0.0180
##      6        0.9340             nan     0.5000   -0.0188
##      7        0.9071             nan     0.5000    0.0064
##      8        0.8844             nan     0.5000    0.0040
##      9        0.8598             nan     0.5000    0.0062
##     10        0.8521             nan     0.5000   -0.0080
##     20        0.7641             nan     0.5000   -0.0047
##     40        0.6877             nan     0.5000   -0.0102
##     60        0.6168             nan     0.5000   -0.0071
##     80        0.5881             nan     0.5000   -0.0052
##    100  1325029.2534             nan     0.5000   -0.0049
##    120  1325029.1995             nan     0.5000   -0.0073
##    140  1325029.1723             nan     0.5000   -0.0009
##    160  1325029.1445             nan     0.5000   -0.0026
##    180  1325029.1078             nan     0.5000   -0.0105
##    200  1325029.0880             nan     0.5000   -0.0030
##    220  1325029.0674             nan     0.5000   -0.0035
##    240  1325029.0523             nan     0.5000    0.0002
##    260  1325030.2055             nan     0.5000   -1.1729
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1241             nan     0.5000    0.0804
##      2        1.0414             nan     0.5000    0.0259
##      3        0.9920             nan     0.5000    0.0158
##      4        0.9582             nan     0.5000    0.0076
##      5        0.9369             nan     0.5000    0.0018
##      6        0.9134             nan     0.5000    0.0079
##      7        0.8988             nan     0.5000   -0.0059
##      8        0.9046             nan     0.5000   -0.0241
##      9        0.8876             nan     0.5000    0.0026
##     10        0.8774             nan     0.5000   -0.0100
##     20        0.8049             nan     0.5000   -0.0061
##     40        0.6836             nan     0.5000   -0.0023
##     60        0.6281             nan     0.5000   -0.0169
##     80        0.5708             nan     0.5000   -0.0096
##    100        0.4895             nan     0.5000   -0.0047
##    120        0.4336             nan     0.5000   -0.0057
##    140        0.3816             nan     0.5000   -0.0068
##    160        0.3392             nan     0.5000   -0.0027
##    180        0.3068             nan     0.5000   -0.0021
##    200        0.2696             nan     0.5000   -0.0027
##    220        0.2388             nan     0.5000   -0.0035
##    240        0.2232             nan     0.5000   -0.0019
##    260        0.1978             nan     0.5000   -0.0008
##    280        0.1818             nan     0.5000   -0.0006
##    300        0.1674             nan     0.5000   -0.0016
##    320        0.1536             nan     0.5000   -0.0004
##    340        0.1428             nan     0.5000   -0.0013
##    360        0.1278             nan     0.5000   -0.0028
##    380        0.1187             nan     0.5000   -0.0000
##    400        0.1075             nan     0.5000   -0.0004
##    420        0.1003             nan     0.5000   -0.0005
##    440        0.0965             nan     0.5000   -0.0015
##    460        0.0849             nan     0.5000   -0.0011
##    480        0.0794             nan     0.5000   -0.0017
##    500        0.0732             nan     0.5000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1429             nan     0.5000    0.0595
##      2        1.0520             nan     0.5000    0.0182
##      3        1.0108             nan     0.5000    0.0076
##      4        0.9618             nan     0.5000    0.0132
##      5        0.9328             nan     0.5000    0.0085
##      6        0.9193             nan     0.5000    0.0013
##      7        0.9090             nan     0.5000   -0.0065
##      8        0.8979             nan     0.5000   -0.0039
##      9        0.8865             nan     0.5000   -0.0035
##     10        0.8653             nan     0.5000    0.0003
##     20        0.7968             nan     0.5000   -0.0068
##     40        0.7178             nan     0.5000   -0.0087
##     60        0.6387             nan     0.5000   -0.0008
##     80        0.5908             nan     0.5000   -0.0072
##    100        0.5174             nan     0.5000   -0.0092
##    120        0.4798             nan     0.5000   -0.0142
##    140        0.4409             nan     0.5000   -0.0037
##    160        0.3937             nan     0.5000   -0.0059
##    180        0.3661             nan     0.5000   -0.0076
##    200        0.3117             nan     0.5000   -0.0058
##    220        0.2842             nan     0.5000   -0.0043
##    240        0.2623             nan     0.5000   -0.0029
##    260        0.2471             nan     0.5000   -0.0017
##    280        0.2234             nan     0.5000   -0.0030
##    300        0.2053             nan     0.5000   -0.0019
##    320        0.1835             nan     0.5000   -0.0016
##    340        0.1698             nan     0.5000   -0.0024
##    360        0.1601             nan     0.5000   -0.0015
##    380        0.1432             nan     0.5000   -0.0023
##    400        0.1322             nan     0.5000   -0.0034
##    420        0.1195             nan     0.5000   -0.0003
##    440        0.1063             nan     0.5000   -0.0010
##    460        0.0992             nan     0.5000   -0.0012
##    480        0.0931             nan     0.5000   -0.0012
##    500        0.0853             nan     0.5000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0978             nan     0.5000    0.0860
##      2        0.9844             nan     0.5000    0.0446
##      3        0.9411             nan     0.5000    0.0096
##      4        0.9128             nan     0.5000   -0.0111
##      5        0.8917             nan     0.5000   -0.0084
##      6        0.8683             nan     0.5000   -0.0121
##      7        0.8535             nan     0.5000   -0.0067
##      8        0.8419             nan     0.5000   -0.0160
##      9        0.8267             nan     0.5000   -0.0061
##     10        0.8104             nan     0.5000   -0.0022
##     20        0.7802             nan     0.5000   -0.0107
##     40        0.6281             nan     0.5000   -0.0146
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0988             nan     0.5000    0.0770
##      2        1.0149             nan     0.5000    0.0219
##      3        0.9609             nan     0.5000    0.0107
##      4        0.9408             nan     0.5000   -0.0109
##      5        0.9071             nan     0.5000    0.0050
##      6        0.8821             nan     0.5000   -0.0023
##      7        0.8495             nan     0.5000    0.0050
##      8        0.8386             nan     0.5000   -0.0071
##      9        0.8221             nan     0.5000   -0.0040
##     10        0.8209             nan     0.5000   -0.0212
##     20        0.7232             nan     0.5000   -0.0078
##     40        0.5891             nan     0.5000   -0.0123
##     60        0.7246             nan     0.5000   -0.0054
##     80        0.4692             nan     0.5000   -0.0019
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0761             nan     0.5000    0.1026
##      2        0.9745             nan     0.5000    0.0342
##      3        0.9337             nan     0.5000    0.0048
##      4        0.8888             nan     0.5000    0.0132
##      5        0.8565             nan     0.5000   -0.0008
##      6        0.8364             nan     0.5000   -0.0026
##      7        0.8257             nan     0.5000   -0.0096
##      8        0.8137             nan     0.5000   -0.0059
##      9        0.8101             nan     0.5000   -0.0217
##     10        0.8112             nan     0.5000   -0.0204
##     20        0.7058             nan     0.5000   -0.0092
##     40        0.5670             nan     0.5000   -0.0285
##     60        0.4574             nan     0.5000   -0.0015
##     80        0.3609             nan     0.5000   -0.0077
##    100        0.2893             nan     0.5000   -0.0050
##    120        0.2373             nan     0.5000   -0.0024
##    140        0.1952             nan     0.5000   -0.0029
##    160        0.1727             nan     0.5000   -0.0033
##    180        0.1483             nan     0.5000   -0.0027
##    200        0.1284             nan     0.5000   -0.0022
##    220        0.1077             nan     0.5000   -0.0005
##    240        0.0897             nan     0.5000    0.0002
##    260        0.0764             nan     0.5000   -0.0004
##    280        0.0674             nan     0.5000   -0.0012
##    300        0.0589             nan     0.5000   -0.0014
##    320        0.0533             nan     0.5000   -0.0016
##    340        0.0458             nan     0.5000   -0.0013
##    360        0.0412             nan     0.5000   -0.0005
##    380        0.0357             nan     0.5000   -0.0002
##    400        0.0316             nan     0.5000   -0.0004
##    420        0.0278             nan     0.5000   -0.0006
##    440        0.0246             nan     0.5000   -0.0005
##    460        0.0216             nan     0.5000   -0.0002
##    480        0.0189             nan     0.5000   -0.0002
##    500        0.0171             nan     0.5000   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1229             nan     1.0000    0.0837
##      2        1.0877             nan     1.0000   -0.0221
##      3        1.0182             nan     1.0000    0.0314
##      4        0.9980             nan     1.0000   -0.0179
##      5        0.9633             nan     1.0000    0.0028
##      6        0.9631             nan     1.0000   -0.0181
##      7        0.9425             nan     1.0000    0.0001
##      8        0.9067             nan     1.0000    0.0114
##      9        0.9071             nan     1.0000   -0.0152
##     10        0.9211             nan     1.0000   -0.0273
##     20        0.8199             nan     1.0000   -0.0086
##     40        0.7853             nan     1.0000   -0.0335
##     60 382121397.6836             nan     1.0000   -0.0088
##     80 382126016.3943             nan     1.0000    0.0005
##    100 382126016.3611             nan     1.0000    0.0000
##    120 382126016.3342             nan     1.0000   -0.0123
##    140 382126016.3292             nan     1.0000   -0.0033
##    160 382126016.3376             nan     1.0000   -0.0118
##    180 382126016.2803             nan     1.0000   -0.0022
##    200 382126016.2768             nan     1.0000   -0.0005
##    220 382126016.2546             nan     1.0000   -0.0051
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1277             nan     1.0000    0.0649
##      2        1.0442             nan     1.0000    0.0327
##      3        0.9900             nan     1.0000    0.0194
##      4        0.9692             nan     1.0000   -0.0059
##      5        0.9509             nan     1.0000    0.0005
##      6        0.9531             nan     1.0000   -0.0189
##      7        0.9454             nan     1.0000   -0.0135
##      8        0.9331             nan     1.0000   -0.0051
##      9        0.9081             nan     1.0000    0.0020
##     10        0.9157             nan     1.0000   -0.0232
##     20 44948346997.6312             nan     1.0000    0.0142
##     40 44948347000.3201             nan     1.0000   -0.0004
##     60 44948347000.3714             nan     1.0000    0.0023
##     80 44948347002.0234             nan     1.0000   -0.2762
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1304             nan     1.0000    0.0574
##      2        1.0541             nan     1.0000    0.0276
##      3        1.0071             nan     1.0000    0.0208
##      4        0.9742             nan     1.0000    0.0046
##      5        0.9643             nan     1.0000   -0.0235
##      6        0.9607             nan     1.0000   -0.0146
##      7        0.9379             nan     1.0000    0.0028
##      8        0.9346             nan     1.0000   -0.0164
##      9        0.9502             nan     1.0000   -0.0311
##     10        0.9397             nan     1.0000   -0.0114
##     20        0.8765             nan     1.0000   -0.0005
##     40        0.8000             nan     1.0000   -0.0085
##     60        0.7465             nan     1.0000   -0.0173
##     80        1.9083             nan     1.0000   -0.0000
##    100 42217376975772114944.0000             nan     1.0000   -0.0027
##    120 42217376975772114944.0000             nan     1.0000   -0.0018
##    140 42217376975772114944.0000             nan     1.0000   -0.0229
##    160 42217376975772114944.0000             nan     1.0000   -0.0005
##    180 42217376975772114944.0000             nan     1.0000    0.0093
##    200 42217376975772114944.0000             nan     1.0000    0.0046
##    220 42217376975774302208.0000             nan     1.0000 -1820470.4146
##    240 42217376975774302208.0000             nan     1.0000   -0.0109
##    260 42217376975774302208.0000             nan     1.0000   -0.0019
##    280 42217376975774302208.0000             nan     1.0000    0.0007
##    300 42217376975774302208.0000             nan     1.0000   -0.0247
##    320 42217376975774302208.0000             nan     1.0000   -0.2193
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0910             nan     1.0000    0.0638
##      2        0.9909             nan     1.0000    0.0217
##      3        0.9547             nan     1.0000   -0.0069
##      4        0.9452             nan     1.0000   -0.0264
##      5        0.9204             nan     1.0000    0.0045
##      6        0.8893             nan     1.0000   -0.0088
##      7        0.8959             nan     1.0000   -0.0329
##      8        0.8680             nan     1.0000   -0.0007
##      9        0.9534             nan     1.0000   -0.1195
##     10        0.9099             nan     1.0000    0.0023
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0818             nan     1.0000    0.0546
##      2        0.9991             nan     1.0000    0.0072
##      3        0.9888             nan     1.0000   -0.0340
##      4        0.9443             nan     1.0000   -0.0120
##      5        1.0251             nan     1.0000   -0.0970
##      6        1.0156             nan     1.0000   -0.0164
##      7        1.0192             nan     1.0000   -0.0430
##      8        0.9368             nan     1.0000    0.0052
##      9        0.9169             nan     1.0000   -0.0091
##     10        0.9224             nan     1.0000   -0.0315
##     20       55.6658             nan     1.0000   -0.0560
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0778             nan     1.0000    0.0988
##      2        1.0059             nan     1.0000    0.0187
##      3        1.0073             nan     1.0000   -0.0426
##      4        0.9823             nan     1.0000   -0.0185
##      5        0.9812             nan     1.0000   -0.0194
##      6        0.9683             nan     1.0000   -0.0188
##      7        0.9436             nan     1.0000   -0.0195
##      8        0.9087             nan     1.0000   -0.0263
##      9        0.9863             nan     1.0000   -0.0900
##     10        0.9683             nan     1.0000   -0.0276
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0472             nan     1.0000    0.1070
##      2        0.9571             nan     1.0000    0.0166
##      3        0.9199             nan     1.0000   -0.0170
##      4        0.9128             nan     1.0000   -0.0308
##      5        0.8915             nan     1.0000   -0.0252
##      6        0.8921             nan     1.0000   -0.0468
##      7        0.8512             nan     1.0000   -0.0070
##      8        0.8420             nan     1.0000   -0.0259
##      9        0.8510             nan     1.0000   -0.0511
##     10        0.8238             nan     1.0000   -0.0114
##     20        0.9330             nan     1.0000   -0.1019
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0871             nan     1.0000    0.0498
##      2        1.0198             nan     1.0000   -0.0049
##      3        0.9687             nan     1.0000   -0.0066
##      4        0.9131             nan     1.0000    0.0045
##      5        0.9310             nan     1.0000   -0.0605
##      6        0.9135             nan     1.0000   -0.0182
##      7        0.8948             nan     1.0000   -0.0173
##      8        0.8956             nan     1.0000   -0.0439
##      9        0.9202             nan     1.0000   -0.0657
##     10        0.9132             nan     1.0000   -0.0605
##     20        1.7378             nan     1.0000   -0.7957
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0768             nan     1.0000    0.0516
##      2        0.9758             nan     1.0000    0.0330
##      3        0.9234             nan     1.0000    0.0012
##      4        0.9226             nan     1.0000   -0.0423
##      5        0.9045             nan     1.0000   -0.0444
##      6        0.8830             nan     1.0000   -0.0195
##      7        0.8731             nan     1.0000   -0.0333
##      8        0.8482             nan     1.0000   -0.0212
##      9        0.8308             nan     1.0000   -0.0149
##     10        0.8336             nan     1.0000   -0.0350
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2791             nan     0.0010    0.0001
##     60        1.2724             nan     0.0010    0.0002
##     80        1.2660             nan     0.0010    0.0001
##    100        1.2597             nan     0.0010    0.0001
##    120        1.2538             nan     0.0010    0.0001
##    140        1.2482             nan     0.0010    0.0001
##    160        1.2430             nan     0.0010    0.0001
##    180        1.2377             nan     0.0010    0.0001
##    200        1.2325             nan     0.0010    0.0001
##    220        1.2276             nan     0.0010    0.0001
##    240        1.2228             nan     0.0010    0.0001
##    260        1.2181             nan     0.0010    0.0001
##    280        1.2136             nan     0.0010    0.0001
##    300        1.2090             nan     0.0010    0.0001
##    320        1.2049             nan     0.0010    0.0001
##    340        1.2007             nan     0.0010    0.0001
##    360        1.1967             nan     0.0010    0.0001
##    380        1.1927             nan     0.0010    0.0001
##    400        1.1889             nan     0.0010    0.0001
##    420        1.1851             nan     0.0010    0.0001
##    440        1.1814             nan     0.0010    0.0001
##    460        1.1778             nan     0.0010    0.0001
##    480        1.1743             nan     0.0010    0.0001
##    500        1.1708             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0001
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2793             nan     0.0010    0.0002
##     60        1.2726             nan     0.0010    0.0001
##     80        1.2662             nan     0.0010    0.0001
##    100        1.2600             nan     0.0010    0.0001
##    120        1.2541             nan     0.0010    0.0001
##    140        1.2485             nan     0.0010    0.0001
##    160        1.2431             nan     0.0010    0.0001
##    180        1.2378             nan     0.0010    0.0001
##    200        1.2328             nan     0.0010    0.0001
##    220        1.2280             nan     0.0010    0.0001
##    240        1.2232             nan     0.0010    0.0001
##    260        1.2185             nan     0.0010    0.0001
##    280        1.2140             nan     0.0010    0.0001
##    300        1.2097             nan     0.0010    0.0001
##    320        1.2053             nan     0.0010    0.0001
##    340        1.2014             nan     0.0010    0.0001
##    360        1.1972             nan     0.0010    0.0001
##    380        1.1934             nan     0.0010    0.0001
##    400        1.1895             nan     0.0010    0.0001
##    420        1.1856             nan     0.0010    0.0001
##    440        1.1820             nan     0.0010    0.0001
##    460        1.1783             nan     0.0010    0.0001
##    480        1.1748             nan     0.0010    0.0001
##    500        1.1714             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2920             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0001
##      6        1.2913             nan     0.0010    0.0002
##      7        1.2910             nan     0.0010    0.0001
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0001
##     10        1.2899             nan     0.0010    0.0002
##     20        1.2864             nan     0.0010    0.0002
##     40        1.2794             nan     0.0010    0.0002
##     60        1.2729             nan     0.0010    0.0001
##     80        1.2665             nan     0.0010    0.0001
##    100        1.2605             nan     0.0010    0.0001
##    120        1.2545             nan     0.0010    0.0001
##    140        1.2489             nan     0.0010    0.0001
##    160        1.2435             nan     0.0010    0.0001
##    180        1.2384             nan     0.0010    0.0001
##    200        1.2333             nan     0.0010    0.0001
##    220        1.2285             nan     0.0010    0.0001
##    240        1.2237             nan     0.0010    0.0001
##    260        1.2189             nan     0.0010    0.0001
##    280        1.2144             nan     0.0010    0.0001
##    300        1.2098             nan     0.0010    0.0001
##    320        1.2055             nan     0.0010    0.0001
##    340        1.2014             nan     0.0010    0.0001
##    360        1.1973             nan     0.0010    0.0001
##    380        1.1933             nan     0.0010    0.0001
##    400        1.1896             nan     0.0010    0.0001
##    420        1.1859             nan     0.0010    0.0001
##    440        1.1822             nan     0.0010    0.0001
##    460        1.1786             nan     0.0010    0.0001
##    480        1.1750             nan     0.0010    0.0001
##    500        1.1716             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0001
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2844             nan     0.0010    0.0002
##     40        1.2757             nan     0.0010    0.0002
##     60        1.2668             nan     0.0010    0.0002
##     80        1.2584             nan     0.0010    0.0002
##    100        1.2504             nan     0.0010    0.0002
##    120        1.2429             nan     0.0010    0.0001
##    140        1.2353             nan     0.0010    0.0002
##    160        1.2277             nan     0.0010    0.0002
##    180        1.2205             nan     0.0010    0.0002
##    200        1.2138             nan     0.0010    0.0001
##    220        1.2072             nan     0.0010    0.0001
##    240        1.2007             nan     0.0010    0.0001
##    260        1.1945             nan     0.0010    0.0002
##    280        1.1883             nan     0.0010    0.0001
##    300        1.1824             nan     0.0010    0.0001
##    320        1.1765             nan     0.0010    0.0001
##    340        1.1709             nan     0.0010    0.0001
##    360        1.1655             nan     0.0010    0.0001
##    380        1.1601             nan     0.0010    0.0001
##    400        1.1548             nan     0.0010    0.0001
##    420        1.1497             nan     0.0010    0.0001
##    440        1.1448             nan     0.0010    0.0001
##    460        1.1401             nan     0.0010    0.0001
##    480        1.1353             nan     0.0010    0.0001
##    500        1.1309             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0003
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2839             nan     0.0010    0.0002
##     40        1.2750             nan     0.0010    0.0002
##     60        1.2665             nan     0.0010    0.0002
##     80        1.2581             nan     0.0010    0.0002
##    100        1.2497             nan     0.0010    0.0002
##    120        1.2418             nan     0.0010    0.0001
##    140        1.2342             nan     0.0010    0.0002
##    160        1.2268             nan     0.0010    0.0002
##    180        1.2197             nan     0.0010    0.0001
##    200        1.2127             nan     0.0010    0.0001
##    220        1.2062             nan     0.0010    0.0001
##    240        1.1997             nan     0.0010    0.0001
##    260        1.1934             nan     0.0010    0.0001
##    280        1.1872             nan     0.0010    0.0001
##    300        1.1812             nan     0.0010    0.0001
##    320        1.1757             nan     0.0010    0.0001
##    340        1.1702             nan     0.0010    0.0001
##    360        1.1648             nan     0.0010    0.0001
##    380        1.1594             nan     0.0010    0.0001
##    400        1.1544             nan     0.0010    0.0001
##    420        1.1494             nan     0.0010    0.0001
##    440        1.1445             nan     0.0010    0.0001
##    460        1.1396             nan     0.0010    0.0001
##    480        1.1350             nan     0.0010    0.0001
##    500        1.1306             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2916             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2839             nan     0.0010    0.0002
##     40        1.2748             nan     0.0010    0.0002
##     60        1.2663             nan     0.0010    0.0002
##     80        1.2578             nan     0.0010    0.0002
##    100        1.2497             nan     0.0010    0.0002
##    120        1.2418             nan     0.0010    0.0002
##    140        1.2342             nan     0.0010    0.0002
##    160        1.2269             nan     0.0010    0.0002
##    180        1.2197             nan     0.0010    0.0002
##    200        1.2128             nan     0.0010    0.0001
##    220        1.2062             nan     0.0010    0.0001
##    240        1.1997             nan     0.0010    0.0001
##    260        1.1938             nan     0.0010    0.0001
##    280        1.1876             nan     0.0010    0.0001
##    300        1.1817             nan     0.0010    0.0001
##    320        1.1759             nan     0.0010    0.0001
##    340        1.1704             nan     0.0010    0.0001
##    360        1.1649             nan     0.0010    0.0001
##    380        1.1595             nan     0.0010    0.0001
##    400        1.1544             nan     0.0010    0.0001
##    420        1.1495             nan     0.0010    0.0001
##    440        1.1446             nan     0.0010    0.0001
##    460        1.1397             nan     0.0010    0.0001
##    480        1.1349             nan     0.0010    0.0001
##    500        1.1303             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2916             nan     0.0010    0.0003
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2905             nan     0.0010    0.0002
##      6        1.2899             nan     0.0010    0.0002
##      7        1.2894             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0003
##      9        1.2883             nan     0.0010    0.0003
##     10        1.2879             nan     0.0010    0.0002
##     20        1.2826             nan     0.0010    0.0002
##     40        1.2724             nan     0.0010    0.0002
##     60        1.2627             nan     0.0010    0.0002
##     80        1.2533             nan     0.0010    0.0002
##    100        1.2439             nan     0.0010    0.0002
##    120        1.2346             nan     0.0010    0.0002
##    140        1.2258             nan     0.0010    0.0002
##    160        1.2175             nan     0.0010    0.0002
##    180        1.2092             nan     0.0010    0.0002
##    200        1.2014             nan     0.0010    0.0001
##    220        1.1937             nan     0.0010    0.0002
##    240        1.1865             nan     0.0010    0.0002
##    260        1.1790             nan     0.0010    0.0001
##    280        1.1721             nan     0.0010    0.0001
##    300        1.1653             nan     0.0010    0.0001
##    320        1.1587             nan     0.0010    0.0001
##    340        1.1520             nan     0.0010    0.0001
##    360        1.1457             nan     0.0010    0.0002
##    380        1.1398             nan     0.0010    0.0001
##    400        1.1336             nan     0.0010    0.0001
##    420        1.1280             nan     0.0010    0.0001
##    440        1.1222             nan     0.0010    0.0001
##    460        1.1167             nan     0.0010    0.0001
##    480        1.1114             nan     0.0010    0.0001
##    500        1.1063             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0003
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0003
##      8        1.2890             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0002
##     20        1.2828             nan     0.0010    0.0002
##     40        1.2723             nan     0.0010    0.0002
##     60        1.2625             nan     0.0010    0.0002
##     80        1.2530             nan     0.0010    0.0002
##    100        1.2438             nan     0.0010    0.0002
##    120        1.2348             nan     0.0010    0.0002
##    140        1.2264             nan     0.0010    0.0002
##    160        1.2178             nan     0.0010    0.0002
##    180        1.2095             nan     0.0010    0.0002
##    200        1.2016             nan     0.0010    0.0002
##    220        1.1940             nan     0.0010    0.0002
##    240        1.1864             nan     0.0010    0.0002
##    260        1.1789             nan     0.0010    0.0001
##    280        1.1719             nan     0.0010    0.0002
##    300        1.1649             nan     0.0010    0.0001
##    320        1.1583             nan     0.0010    0.0001
##    340        1.1517             nan     0.0010    0.0001
##    360        1.1453             nan     0.0010    0.0001
##    380        1.1395             nan     0.0010    0.0001
##    400        1.1336             nan     0.0010    0.0001
##    420        1.1279             nan     0.0010    0.0001
##    440        1.1220             nan     0.0010    0.0001
##    460        1.1164             nan     0.0010    0.0001
##    480        1.1112             nan     0.0010    0.0001
##    500        1.1060             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0003
##      8        1.2890             nan     0.0010    0.0002
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0003
##     20        1.2826             nan     0.0010    0.0002
##     40        1.2726             nan     0.0010    0.0002
##     60        1.2628             nan     0.0010    0.0002
##     80        1.2530             nan     0.0010    0.0002
##    100        1.2437             nan     0.0010    0.0002
##    120        1.2346             nan     0.0010    0.0002
##    140        1.2259             nan     0.0010    0.0002
##    160        1.2173             nan     0.0010    0.0002
##    180        1.2092             nan     0.0010    0.0002
##    200        1.2012             nan     0.0010    0.0002
##    220        1.1936             nan     0.0010    0.0002
##    240        1.1861             nan     0.0010    0.0002
##    260        1.1790             nan     0.0010    0.0002
##    280        1.1718             nan     0.0010    0.0002
##    300        1.1651             nan     0.0010    0.0002
##    320        1.1583             nan     0.0010    0.0001
##    340        1.1520             nan     0.0010    0.0001
##    360        1.1456             nan     0.0010    0.0001
##    380        1.1394             nan     0.0010    0.0001
##    400        1.1336             nan     0.0010    0.0001
##    420        1.1278             nan     0.0010    0.0001
##    440        1.1222             nan     0.0010    0.0001
##    460        1.1168             nan     0.0010    0.0001
##    480        1.1115             nan     0.0010    0.0001
##    500        1.1062             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2545             nan     0.1000    0.0165
##      2        1.2240             nan     0.1000    0.0121
##      3        1.1980             nan     0.1000    0.0097
##      4        1.1781             nan     0.1000    0.0083
##      5        1.1624             nan     0.1000    0.0071
##      6        1.1491             nan     0.1000    0.0065
##      7        1.1319             nan     0.1000    0.0057
##      8        1.1176             nan     0.1000    0.0055
##      9        1.1053             nan     0.1000    0.0047
##     10        1.0949             nan     0.1000    0.0041
##     20        1.0154             nan     0.1000    0.0029
##     40        0.9352             nan     0.1000    0.0004
##     60        0.8980             nan     0.1000   -0.0002
##     80        0.8663             nan     0.1000    0.0003
##    100        0.8515             nan     0.1000   -0.0009
##    120        0.8406             nan     0.1000   -0.0002
##    140        0.8319             nan     0.1000   -0.0012
##    160        0.8219             nan     0.1000   -0.0011
##    180        0.8119             nan     0.1000   -0.0004
##    200        0.8044             nan     0.1000   -0.0011
##    220        0.7937             nan     0.1000    0.0001
##    240        0.7890             nan     0.1000   -0.0028
##    260        0.7825             nan     0.1000   -0.0006
##    280        0.7753             nan     0.1000   -0.0013
##    300        0.7694             nan     0.1000   -0.0006
##    320        0.7640             nan     0.1000   -0.0013
##    340        0.7585             nan     0.1000   -0.0013
##    360        0.7538             nan     0.1000   -0.0008
##    380        0.7462             nan     0.1000   -0.0014
##    400        0.7414             nan     0.1000   -0.0004
##    420        0.7389             nan     0.1000   -0.0014
##    440        0.7342             nan     0.1000   -0.0006
##    460        0.7306             nan     0.1000   -0.0011
##    480        0.7249             nan     0.1000   -0.0008
##    500        0.7206             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2613             nan     0.1000    0.0161
##      2        1.2339             nan     0.1000    0.0137
##      3        1.2114             nan     0.1000    0.0089
##      4        1.1919             nan     0.1000    0.0062
##      5        1.1696             nan     0.1000    0.0073
##      6        1.1528             nan     0.1000    0.0079
##      7        1.1363             nan     0.1000    0.0057
##      8        1.1237             nan     0.1000    0.0052
##      9        1.1108             nan     0.1000    0.0040
##     10        1.0975             nan     0.1000    0.0052
##     20        1.0120             nan     0.1000    0.0020
##     40        0.9357             nan     0.1000   -0.0002
##     60        0.8988             nan     0.1000    0.0004
##     80        0.8759             nan     0.1000   -0.0004
##    100        0.8573             nan     0.1000   -0.0007
##    120        0.8437             nan     0.1000   -0.0015
##    140        0.8265             nan     0.1000   -0.0014
##    160        0.8161             nan     0.1000   -0.0003
##    180        0.8052             nan     0.1000   -0.0003
##    200        0.7992             nan     0.1000   -0.0010
##    220        0.7908             nan     0.1000   -0.0006
##    240        0.7835             nan     0.1000   -0.0009
##    260        0.7779             nan     0.1000   -0.0009
##    280        0.7701             nan     0.1000   -0.0002
##    300        0.7646             nan     0.1000   -0.0011
##    320        0.7585             nan     0.1000   -0.0013
##    340        0.7508             nan     0.1000   -0.0008
##    360        0.7451             nan     0.1000   -0.0016
##    380        0.7388             nan     0.1000   -0.0010
##    400        0.7361             nan     0.1000   -0.0009
##    420        0.7304             nan     0.1000    0.0001
##    440        0.7266             nan     0.1000   -0.0012
##    460        0.7234             nan     0.1000   -0.0013
##    480        0.7184             nan     0.1000   -0.0007
##    500        0.7159             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2586             nan     0.1000    0.0171
##      2        1.2335             nan     0.1000    0.0115
##      3        1.2070             nan     0.1000    0.0115
##      4        1.1948             nan     0.1000    0.0048
##      5        1.1732             nan     0.1000    0.0079
##      6        1.1571             nan     0.1000    0.0061
##      7        1.1415             nan     0.1000    0.0072
##      8        1.1257             nan     0.1000    0.0060
##      9        1.1115             nan     0.1000    0.0046
##     10        1.0986             nan     0.1000    0.0048
##     20        1.0176             nan     0.1000    0.0021
##     40        0.9383             nan     0.1000   -0.0002
##     60        0.9011             nan     0.1000   -0.0008
##     80        0.8768             nan     0.1000   -0.0006
##    100        0.8584             nan     0.1000   -0.0004
##    120        0.8446             nan     0.1000   -0.0008
##    140        0.8307             nan     0.1000   -0.0014
##    160        0.8184             nan     0.1000   -0.0010
##    180        0.8103             nan     0.1000   -0.0006
##    200        0.8011             nan     0.1000   -0.0003
##    220        0.7938             nan     0.1000   -0.0012
##    240        0.7857             nan     0.1000   -0.0010
##    260        0.7788             nan     0.1000   -0.0011
##    280        0.7707             nan     0.1000   -0.0008
##    300        0.7668             nan     0.1000   -0.0004
##    320        0.7619             nan     0.1000   -0.0016
##    340        0.7532             nan     0.1000   -0.0004
##    360        0.7481             nan     0.1000   -0.0011
##    380        0.7423             nan     0.1000   -0.0007
##    400        0.7372             nan     0.1000   -0.0011
##    420        0.7315             nan     0.1000   -0.0019
##    440        0.7268             nan     0.1000   -0.0016
##    460        0.7221             nan     0.1000   -0.0000
##    480        0.7164             nan     0.1000   -0.0008
##    500        0.7114             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2529             nan     0.1000    0.0221
##      2        1.2182             nan     0.1000    0.0149
##      3        1.1973             nan     0.1000    0.0081
##      4        1.1649             nan     0.1000    0.0160
##      5        1.1449             nan     0.1000    0.0092
##      6        1.1260             nan     0.1000    0.0061
##      7        1.1018             nan     0.1000    0.0091
##      8        1.0782             nan     0.1000    0.0103
##      9        1.0606             nan     0.1000    0.0065
##     10        1.0444             nan     0.1000    0.0050
##     20        0.9515             nan     0.1000    0.0014
##     40        0.8679             nan     0.1000    0.0012
##     60        0.8139             nan     0.1000   -0.0010
##     80        0.7766             nan     0.1000   -0.0010
##    100        0.7526             nan     0.1000   -0.0017
##    120        0.7267             nan     0.1000   -0.0012
##    140        0.7078             nan     0.1000   -0.0006
##    160        0.6860             nan     0.1000   -0.0007
##    180        0.6626             nan     0.1000   -0.0004
##    200        0.6444             nan     0.1000   -0.0007
##    220        0.6247             nan     0.1000   -0.0002
##    240        0.6076             nan     0.1000   -0.0008
##    260        0.5939             nan     0.1000   -0.0006
##    280        0.5747             nan     0.1000   -0.0009
##    300        0.5602             nan     0.1000   -0.0017
##    320        0.5442             nan     0.1000   -0.0009
##    340        0.5320             nan     0.1000   -0.0013
##    360        0.5198             nan     0.1000   -0.0006
##    380        0.5082             nan     0.1000   -0.0009
##    400        0.4984             nan     0.1000   -0.0002
##    420        0.4867             nan     0.1000   -0.0016
##    440        0.4710             nan     0.1000   -0.0005
##    460        0.4613             nan     0.1000   -0.0014
##    480        0.4520             nan     0.1000   -0.0013
##    500        0.4416             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2452             nan     0.1000    0.0209
##      2        1.2095             nan     0.1000    0.0181
##      3        1.1785             nan     0.1000    0.0126
##      4        1.1501             nan     0.1000    0.0115
##      5        1.1232             nan     0.1000    0.0105
##      6        1.1000             nan     0.1000    0.0074
##      7        1.0861             nan     0.1000    0.0034
##      8        1.0707             nan     0.1000    0.0049
##      9        1.0524             nan     0.1000    0.0056
##     10        1.0380             nan     0.1000    0.0059
##     20        0.9475             nan     0.1000    0.0003
##     40        0.8667             nan     0.1000   -0.0015
##     60        0.8236             nan     0.1000   -0.0042
##     80        0.7864             nan     0.1000   -0.0004
##    100        0.7533             nan     0.1000   -0.0021
##    120        0.7300             nan     0.1000   -0.0010
##    140        0.7094             nan     0.1000   -0.0015
##    160        0.6908             nan     0.1000   -0.0009
##    180        0.6708             nan     0.1000   -0.0007
##    200        0.6505             nan     0.1000   -0.0004
##    220        0.6292             nan     0.1000   -0.0013
##    240        0.6120             nan     0.1000   -0.0008
##    260        0.5949             nan     0.1000   -0.0010
##    280        0.5802             nan     0.1000   -0.0009
##    300        0.5661             nan     0.1000   -0.0012
##    320        0.5547             nan     0.1000   -0.0010
##    340        0.5418             nan     0.1000   -0.0008
##    360        0.5305             nan     0.1000   -0.0014
##    380        0.5126             nan     0.1000   -0.0005
##    400        0.5012             nan     0.1000   -0.0019
##    420        0.4894             nan     0.1000   -0.0015
##    440        0.4755             nan     0.1000   -0.0009
##    460        0.4648             nan     0.1000   -0.0018
##    480        0.4531             nan     0.1000   -0.0002
##    500        0.4414             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2507             nan     0.1000    0.0213
##      2        1.2124             nan     0.1000    0.0181
##      3        1.1798             nan     0.1000    0.0131
##      4        1.1506             nan     0.1000    0.0137
##      5        1.1266             nan     0.1000    0.0100
##      6        1.1058             nan     0.1000    0.0082
##      7        1.0842             nan     0.1000    0.0069
##      8        1.0694             nan     0.1000    0.0042
##      9        1.0565             nan     0.1000    0.0040
##     10        1.0421             nan     0.1000    0.0049
##     20        0.9537             nan     0.1000    0.0022
##     40        0.8629             nan     0.1000   -0.0004
##     60        0.8135             nan     0.1000    0.0007
##     80        0.7767             nan     0.1000   -0.0026
##    100        0.7500             nan     0.1000   -0.0010
##    120        0.7267             nan     0.1000   -0.0009
##    140        0.7070             nan     0.1000   -0.0012
##    160        0.6857             nan     0.1000   -0.0008
##    180        0.6654             nan     0.1000   -0.0009
##    200        0.6462             nan     0.1000   -0.0013
##    220        0.6305             nan     0.1000   -0.0015
##    240        0.6158             nan     0.1000   -0.0012
##    260        0.5980             nan     0.1000   -0.0007
##    280        0.5831             nan     0.1000   -0.0009
##    300        0.5666             nan     0.1000   -0.0006
##    320        0.5501             nan     0.1000   -0.0013
##    340        0.5363             nan     0.1000   -0.0010
##    360        0.5253             nan     0.1000   -0.0010
##    380        0.5117             nan     0.1000   -0.0014
##    400        0.4997             nan     0.1000   -0.0014
##    420        0.4903             nan     0.1000   -0.0019
##    440        0.4787             nan     0.1000   -0.0023
##    460        0.4675             nan     0.1000   -0.0006
##    480        0.4583             nan     0.1000   -0.0009
##    500        0.4476             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2449             nan     0.1000    0.0214
##      2        1.2016             nan     0.1000    0.0168
##      3        1.1590             nan     0.1000    0.0163
##      4        1.1273             nan     0.1000    0.0149
##      5        1.1015             nan     0.1000    0.0105
##      6        1.0746             nan     0.1000    0.0082
##      7        1.0542             nan     0.1000    0.0057
##      8        1.0374             nan     0.1000    0.0059
##      9        1.0226             nan     0.1000    0.0042
##     10        1.0064             nan     0.1000    0.0045
##     20        0.9073             nan     0.1000   -0.0010
##     40        0.8160             nan     0.1000   -0.0006
##     60        0.7583             nan     0.1000   -0.0015
##     80        0.7098             nan     0.1000   -0.0015
##    100        0.6760             nan     0.1000   -0.0015
##    120        0.6413             nan     0.1000   -0.0003
##    140        0.6151             nan     0.1000   -0.0005
##    160        0.5821             nan     0.1000   -0.0015
##    180        0.5558             nan     0.1000   -0.0016
##    200        0.5271             nan     0.1000    0.0001
##    220        0.5032             nan     0.1000   -0.0011
##    240        0.4826             nan     0.1000   -0.0007
##    260        0.4578             nan     0.1000   -0.0010
##    280        0.4394             nan     0.1000   -0.0010
##    300        0.4182             nan     0.1000   -0.0007
##    320        0.3999             nan     0.1000   -0.0006
##    340        0.3850             nan     0.1000   -0.0009
##    360        0.3693             nan     0.1000   -0.0008
##    380        0.3556             nan     0.1000   -0.0009
##    400        0.3443             nan     0.1000   -0.0014
##    420        0.3313             nan     0.1000   -0.0010
##    440        0.3191             nan     0.1000   -0.0005
##    460        0.3076             nan     0.1000   -0.0009
##    480        0.2976             nan     0.1000   -0.0007
##    500        0.2850             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2393             nan     0.1000    0.0249
##      2        1.1982             nan     0.1000    0.0183
##      3        1.1587             nan     0.1000    0.0173
##      4        1.1270             nan     0.1000    0.0105
##      5        1.1005             nan     0.1000    0.0122
##      6        1.0743             nan     0.1000    0.0106
##      7        1.0590             nan     0.1000    0.0033
##      8        1.0376             nan     0.1000    0.0067
##      9        1.0185             nan     0.1000    0.0055
##     10        1.0022             nan     0.1000    0.0065
##     20        0.9101             nan     0.1000    0.0006
##     40        0.8138             nan     0.1000   -0.0018
##     60        0.7575             nan     0.1000   -0.0013
##     80        0.7157             nan     0.1000   -0.0003
##    100        0.6758             nan     0.1000   -0.0016
##    120        0.6463             nan     0.1000   -0.0009
##    140        0.6197             nan     0.1000   -0.0014
##    160        0.5881             nan     0.1000   -0.0008
##    180        0.5580             nan     0.1000   -0.0009
##    200        0.5306             nan     0.1000   -0.0006
##    220        0.5127             nan     0.1000   -0.0016
##    240        0.4890             nan     0.1000   -0.0016
##    260        0.4675             nan     0.1000   -0.0009
##    280        0.4479             nan     0.1000   -0.0006
##    300        0.4332             nan     0.1000   -0.0026
##    320        0.4145             nan     0.1000   -0.0013
##    340        0.3993             nan     0.1000   -0.0017
##    360        0.3833             nan     0.1000   -0.0011
##    380        0.3678             nan     0.1000   -0.0001
##    400        0.3505             nan     0.1000   -0.0015
##    420        0.3372             nan     0.1000   -0.0008
##    440        0.3250             nan     0.1000   -0.0010
##    460        0.3144             nan     0.1000   -0.0008
##    480        0.3041             nan     0.1000   -0.0008
##    500        0.2931             nan     0.1000    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2440             nan     0.1000    0.0238
##      2        1.1997             nan     0.1000    0.0212
##      3        1.1617             nan     0.1000    0.0125
##      4        1.1268             nan     0.1000    0.0149
##      5        1.1001             nan     0.1000    0.0095
##      6        1.0743             nan     0.1000    0.0115
##      7        1.0533             nan     0.1000    0.0074
##      8        1.0382             nan     0.1000    0.0048
##      9        1.0209             nan     0.1000    0.0062
##     10        1.0092             nan     0.1000    0.0015
##     20        0.9039             nan     0.1000    0.0012
##     40        0.8128             nan     0.1000    0.0005
##     60        0.7527             nan     0.1000   -0.0024
##     80        0.7078             nan     0.1000   -0.0012
##    100        0.6715             nan     0.1000   -0.0020
##    120        0.6375             nan     0.1000   -0.0006
##    140        0.6059             nan     0.1000   -0.0011
##    160        0.5764             nan     0.1000   -0.0008
##    180        0.5551             nan     0.1000   -0.0019
##    200        0.5303             nan     0.1000   -0.0022
##    220        0.5100             nan     0.1000   -0.0009
##    240        0.4856             nan     0.1000   -0.0006
##    260        0.4634             nan     0.1000   -0.0008
##    280        0.4409             nan     0.1000   -0.0017
##    300        0.4237             nan     0.1000   -0.0008
##    320        0.4081             nan     0.1000   -0.0006
##    340        0.3926             nan     0.1000   -0.0014
##    360        0.3774             nan     0.1000   -0.0009
##    380        0.3620             nan     0.1000   -0.0011
##    400        0.3504             nan     0.1000   -0.0008
##    420        0.3376             nan     0.1000   -0.0015
##    440        0.3241             nan     0.1000   -0.0007
##    460        0.3126             nan     0.1000   -0.0013
##    480        0.2994             nan     0.1000   -0.0006
##    500        0.2876             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2210             nan     0.2000    0.0307
##      2        1.1812             nan     0.2000    0.0133
##      3        1.1548             nan     0.2000    0.0087
##      4        1.1249             nan     0.2000    0.0131
##      5        1.0990             nan     0.2000    0.0102
##      6        1.0757             nan     0.2000    0.0063
##      7        1.0557             nan     0.2000    0.0095
##      8        1.0419             nan     0.2000    0.0025
##      9        1.0236             nan     0.2000    0.0052
##     10        1.0134             nan     0.2000    0.0025
##     20        0.9400             nan     0.2000    0.0012
##     40        0.8763             nan     0.2000   -0.0019
##     60        0.8442             nan     0.2000   -0.0013
##     80        0.8228             nan     0.2000   -0.0008
##    100        0.8040             nan     0.2000   -0.0025
##    120        0.7921             nan     0.2000   -0.0043
##    140        0.7817             nan     0.2000   -0.0015
##    160        0.7676             nan     0.2000   -0.0012
##    180        0.7593             nan     0.2000   -0.0015
##    200        0.7507             nan     0.2000   -0.0014
##    220        0.7405             nan     0.2000   -0.0007
##    240        0.7355             nan     0.2000   -0.0021
##    260        0.7246             nan     0.2000   -0.0014
##    280        0.7166             nan     0.2000   -0.0013
##    300        0.7118             nan     0.2000   -0.0006
##    320        0.7059             nan     0.2000   -0.0018
##    340        0.6986             nan     0.2000   -0.0007
##    360        0.6916             nan     0.2000   -0.0006
##    380        0.6840             nan     0.2000   -0.0034
##    400        0.6790             nan     0.2000   -0.0020
##    420        0.6753             nan     0.2000   -0.0015
##    440        0.6693             nan     0.2000   -0.0010
##    460        0.6634             nan     0.2000   -0.0022
##    480        0.6534             nan     0.2000   -0.0013
##    500        0.6492             nan     0.2000   -0.0031
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2271             nan     0.2000    0.0264
##      2        1.1800             nan     0.2000    0.0168
##      3        1.1493             nan     0.2000    0.0124
##      4        1.1215             nan     0.2000    0.0112
##      5        1.0966             nan     0.2000    0.0100
##      6        1.0823             nan     0.2000    0.0061
##      7        1.0639             nan     0.2000    0.0060
##      8        1.0421             nan     0.2000    0.0072
##      9        1.0301             nan     0.2000    0.0033
##     10        1.0146             nan     0.2000    0.0017
##     20        0.9449             nan     0.2000   -0.0003
##     40        0.8839             nan     0.2000   -0.0016
##     60        0.8509             nan     0.2000   -0.0030
##     80        0.8308             nan     0.2000   -0.0032
##    100        0.8147             nan     0.2000   -0.0034
##    120        0.7998             nan     0.2000   -0.0009
##    140        0.7835             nan     0.2000   -0.0008
##    160        0.7711             nan     0.2000   -0.0021
##    180        0.7642             nan     0.2000   -0.0011
##    200        0.7508             nan     0.2000   -0.0018
##    220        0.7415             nan     0.2000   -0.0027
##    240        0.7287             nan     0.2000   -0.0008
##    260        0.7191             nan     0.2000   -0.0026
##    280        0.7135             nan     0.2000   -0.0024
##    300        0.7040             nan     0.2000   -0.0003
##    320        0.6963             nan     0.2000   -0.0038
##    340        0.6888             nan     0.2000   -0.0018
##    360        0.6775             nan     0.2000   -0.0019
##    380        0.6713             nan     0.2000   -0.0027
##    400        0.6658             nan     0.2000   -0.0009
##    420        0.6617             nan     0.2000   -0.0018
##    440        0.6530             nan     0.2000   -0.0022
##    460        0.6461             nan     0.2000   -0.0011
##    480        0.6399             nan     0.2000   -0.0023
##    500        0.6350             nan     0.2000   -0.0032
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2225             nan     0.2000    0.0279
##      2        1.1850             nan     0.2000    0.0191
##      3        1.1548             nan     0.2000    0.0129
##      4        1.1246             nan     0.2000    0.0121
##      5        1.0981             nan     0.2000    0.0106
##      6        1.0751             nan     0.2000    0.0115
##      7        1.0607             nan     0.2000    0.0045
##      8        1.0490             nan     0.2000    0.0008
##      9        1.0318             nan     0.2000    0.0041
##     10        1.0176             nan     0.2000    0.0067
##     20        0.9390             nan     0.2000   -0.0004
##     40        0.8749             nan     0.2000   -0.0015
##     60        0.8401             nan     0.2000   -0.0028
##     80        0.8176             nan     0.2000   -0.0023
##    100        0.8015             nan     0.2000   -0.0018
##    120        0.7869             nan     0.2000   -0.0016
##    140        0.7790             nan     0.2000   -0.0006
##    160        0.7674             nan     0.2000   -0.0016
##    180        0.7539             nan     0.2000   -0.0019
##    200        0.7440             nan     0.2000   -0.0023
##    220        0.7307             nan     0.2000   -0.0012
##    240        0.7245             nan     0.2000   -0.0030
##    260        0.7115             nan     0.2000   -0.0013
##    280        0.7087             nan     0.2000   -0.0017
##    300        0.6980             nan     0.2000   -0.0023
##    320        0.6915             nan     0.2000   -0.0034
##    340        0.6838             nan     0.2000   -0.0018
##    360        0.6786             nan     0.2000   -0.0005
##    380        0.6762             nan     0.2000   -0.0020
##    400        0.6671             nan     0.2000   -0.0007
##    420        0.6604             nan     0.2000   -0.0029
##    440        0.6553             nan     0.2000   -0.0027
##    460        0.6461             nan     0.2000   -0.0002
##    480        0.6425             nan     0.2000   -0.0010
##    500        0.6392             nan     0.2000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2110             nan     0.2000    0.0370
##      2        1.1509             nan     0.2000    0.0283
##      3        1.1072             nan     0.2000    0.0177
##      4        1.0727             nan     0.2000    0.0101
##      5        1.0491             nan     0.2000    0.0080
##      6        1.0197             nan     0.2000    0.0098
##      7        0.9978             nan     0.2000    0.0080
##      8        0.9784             nan     0.2000    0.0068
##      9        0.9644             nan     0.2000    0.0022
##     10        0.9533             nan     0.2000    0.0015
##     20        0.8675             nan     0.2000   -0.0005
##     40        0.7883             nan     0.2000    0.0008
##     60        0.7286             nan     0.2000   -0.0046
##     80        0.6842             nan     0.2000   -0.0042
##    100        0.6519             nan     0.2000   -0.0011
##    120        2.2376             nan     0.2000   -0.0007
##    140           inf             nan     0.2000       nan
##    160           inf             nan     0.2000       nan
##    180           inf             nan     0.2000       nan
##    200           inf             nan     0.2000       nan
##    220           inf             nan     0.2000       nan
##    240           inf             nan     0.2000       nan
##    260           inf             nan     0.2000       nan
##    280           inf             nan     0.2000       nan
##    300           inf             nan     0.2000       nan
##    320           inf             nan     0.2000   -0.0005
##    340           inf             nan     0.2000   -0.0004
##    360           inf             nan     0.2000       nan
##    380           inf             nan     0.2000       nan
##    400           inf             nan     0.2000       nan
##    420           inf             nan     0.2000       nan
##    440           inf             nan     0.2000       nan
##    460           inf             nan     0.2000       nan
##    480           inf             nan     0.2000       nan
##    500           inf             nan     0.2000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2145             nan     0.2000    0.0338
##      2        1.1542             nan     0.2000    0.0281
##      3        1.1095             nan     0.2000    0.0177
##      4        1.0762             nan     0.2000    0.0119
##      5        1.0518             nan     0.2000    0.0064
##      6        1.0321             nan     0.2000    0.0043
##      7        1.0101             nan     0.2000    0.0075
##      8        0.9886             nan     0.2000    0.0048
##      9        0.9721             nan     0.2000    0.0011
##     10        0.9630             nan     0.2000    0.0007
##     20        0.8714             nan     0.2000   -0.0030
##     40        0.7912             nan     0.2000   -0.0016
##     60        0.7377             nan     0.2000   -0.0020
##     80        0.6977             nan     0.2000   -0.0020
##    100        0.6627             nan     0.2000   -0.0003
##    120        0.6277             nan     0.2000   -0.0018
##    140        0.5965             nan     0.2000   -0.0045
##    160        0.5649             nan     0.2000   -0.0002
##    180        0.5344             nan     0.2000   -0.0021
##    200        0.5145             nan     0.2000   -0.0023
##    220        0.4886             nan     0.2000   -0.0005
##    240        0.4671             nan     0.2000   -0.0011
##    260        0.4509             nan     0.2000   -0.0004
##    280        0.4315             nan     0.2000   -0.0017
##    300        0.4223             nan     0.2000   -0.0031
##    320        0.4066             nan     0.2000   -0.0023
##    340        0.3911             nan     0.2000   -0.0011
##    360        0.3785             nan     0.2000   -0.0018
##    380        0.3616             nan     0.2000   -0.0003
##    400        0.3469             nan     0.2000   -0.0023
##    420        0.3355             nan     0.2000   -0.0018
##    440        0.3203             nan     0.2000   -0.0010
##    460        0.3079             nan     0.2000   -0.0016
##    480        0.2940             nan     0.2000   -0.0011
##    500        0.2836             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2069             nan     0.2000    0.0367
##      2        1.1587             nan     0.2000    0.0168
##      3        1.1067             nan     0.2000    0.0196
##      4        1.0728             nan     0.2000    0.0118
##      5        1.0484             nan     0.2000    0.0046
##      6        1.0268             nan     0.2000    0.0086
##      7        1.0016             nan     0.2000    0.0074
##      8        0.9851             nan     0.2000    0.0019
##      9        0.9732             nan     0.2000    0.0040
##     10        0.9580             nan     0.2000    0.0025
##     20        0.8712             nan     0.2000   -0.0029
##     40        0.7900             nan     0.2000   -0.0019
##     60        0.7329             nan     0.2000   -0.0016
##     80        0.6884             nan     0.2000   -0.0002
##    100        0.6551             nan     0.2000   -0.0015
##    120        0.6167             nan     0.2000   -0.0016
##    140        0.5833             nan     0.2000   -0.0031
##    160        0.5552             nan     0.2000   -0.0028
##    180        0.5275             nan     0.2000   -0.0031
##    200        0.5005             nan     0.2000   -0.0014
##    220        0.4767             nan     0.2000   -0.0014
##    240        0.4517             nan     0.2000   -0.0023
##    260        0.4300             nan     0.2000   -0.0016
##    280        0.4112             nan     0.2000   -0.0003
##    300        0.3947             nan     0.2000   -0.0015
##    320        0.3798             nan     0.2000   -0.0009
##    340        0.3663             nan     0.2000   -0.0009
##    360        0.3533             nan     0.2000   -0.0020
##    380        0.3350             nan     0.2000   -0.0011
##    400        0.3220             nan     0.2000   -0.0010
##    420        0.3129             nan     0.2000   -0.0021
##    440        0.2980             nan     0.2000   -0.0007
##    460        0.2895             nan     0.2000   -0.0017
##    480        0.2821             nan     0.2000   -0.0033
##    500        0.2739             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1930             nan     0.2000    0.0524
##      2        1.1217             nan     0.2000    0.0270
##      3        1.0692             nan     0.2000    0.0212
##      4        1.0309             nan     0.2000    0.0162
##      5        1.0014             nan     0.2000    0.0116
##      6        0.9734             nan     0.2000    0.0083
##      7        0.9503             nan     0.2000    0.0062
##      8        0.9346             nan     0.2000   -0.0005
##      9        0.9232             nan     0.2000   -0.0006
##     10        0.9093             nan     0.2000    0.0000
##     20        0.8269             nan     0.2000   -0.0007
##     40        0.7188             nan     0.2000   -0.0026
##     60        0.6575             nan     0.2000   -0.0042
##     80        0.6027             nan     0.2000   -0.0014
##    100        0.5479             nan     0.2000   -0.0020
##    120        0.5059             nan     0.2000   -0.0072
##    140           inf             nan     0.2000   -0.0013
##    160           inf             nan     0.2000       nan
##    180           inf             nan     0.2000       nan
##    200           inf             nan     0.2000       nan
##    220           inf             nan     0.2000       nan
##    240           inf             nan     0.2000       nan
##    260           inf             nan     0.2000       nan
##    280           inf             nan     0.2000       nan
##    300           inf             nan     0.2000       nan
##    320           inf             nan     0.2000       nan
##    340           inf             nan     0.2000   -0.0010
##    360           inf             nan     0.2000       nan
##    380           inf             nan     0.2000       nan
##    400           inf             nan     0.2000       nan
##    420           inf             nan     0.2000       nan
##    440           inf             nan     0.2000   -0.0004
##    460           inf             nan     0.2000       nan
##    480           inf             nan     0.2000       nan
##    500           inf             nan     0.2000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2083             nan     0.2000    0.0336
##      2        1.1297             nan     0.2000    0.0352
##      3        1.0651             nan     0.2000    0.0273
##      4        1.0330             nan     0.2000    0.0108
##      5        1.0063             nan     0.2000    0.0049
##      6        0.9817             nan     0.2000    0.0096
##      7        0.9582             nan     0.2000    0.0066
##      8        0.9370             nan     0.2000    0.0044
##      9        0.9217             nan     0.2000    0.0033
##     10        0.9075             nan     0.2000    0.0045
##     20        0.8255             nan     0.2000   -0.0047
##     40        0.7216             nan     0.2000   -0.0044
##     60        0.6426             nan     0.2000   -0.0020
##     80        0.5914             nan     0.2000   -0.0024
##    100        0.5356             nan     0.2000   -0.0014
##    120        0.4881             nan     0.2000   -0.0017
##    140        0.4527             nan     0.2000   -0.0028
##    160        0.4156             nan     0.2000   -0.0028
##    180        0.3877             nan     0.2000   -0.0031
##    200        0.3598             nan     0.2000   -0.0003
##    220        0.3307             nan     0.2000   -0.0012
##    240        0.3078             nan     0.2000   -0.0020
##    260        0.2873             nan     0.2000   -0.0017
##    280        0.2699             nan     0.2000   -0.0012
##    300        0.2490             nan     0.2000   -0.0012
##    320        0.2325             nan     0.2000   -0.0008
##    340        0.2195             nan     0.2000   -0.0012
##    360        0.2056             nan     0.2000   -0.0008
##    380        0.1935             nan     0.2000   -0.0010
##    400        0.1822             nan     0.2000   -0.0010
##    420        0.1694             nan     0.2000   -0.0004
##    440        0.1616             nan     0.2000   -0.0006
##    460        0.1524             nan     0.2000   -0.0004
##    480        0.1433             nan     0.2000   -0.0003
##    500        0.1348             nan     0.2000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2003             nan     0.2000    0.0466
##      2        1.1374             nan     0.2000    0.0228
##      3        1.0788             nan     0.2000    0.0249
##      4        1.0515             nan     0.2000    0.0030
##      5        1.0126             nan     0.2000    0.0108
##      6        0.9831             nan     0.2000    0.0101
##      7        0.9647             nan     0.2000    0.0056
##      8        0.9476             nan     0.2000    0.0031
##      9        0.9248             nan     0.2000    0.0048
##     10        0.9121             nan     0.2000   -0.0012
##     20        0.8174             nan     0.2000   -0.0008
##     40        0.7100             nan     0.2000   -0.0026
##     60        0.6384             nan     0.2000   -0.0027
##     80        0.5734             nan     0.2000   -0.0014
##    100        0.5254             nan     0.2000   -0.0015
##    120        0.4970             nan     0.2000   -0.0030
##    140        0.4554             nan     0.2000   -0.0034
##    160        0.4153             nan     0.2000   -0.0010
##    180        0.3813             nan     0.2000    0.0003
##    200        0.3521             nan     0.2000   -0.0027
##    220        0.3267             nan     0.2000   -0.0026
##    240        0.3023             nan     0.2000   -0.0007
##    260        0.2782             nan     0.2000   -0.0018
##    280        0.2613             nan     0.2000   -0.0017
##    300        0.2408             nan     0.2000   -0.0016
##    320        0.2230             nan     0.2000   -0.0004
##    340        0.2086             nan     0.2000   -0.0020
##    360        0.1950             nan     0.2000   -0.0012
##    380        0.1839             nan     0.2000   -0.0010
##    400        0.1730             nan     0.2000   -0.0006
##    420        0.1623             nan     0.2000   -0.0002
##    440        0.1537             nan     0.2000   -0.0007
##    460        0.1457             nan     0.2000   -0.0007
##    480        0.1375             nan     0.2000   -0.0010
##    500        0.1305             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2017             nan     0.3000    0.0415
##      2        1.1517             nan     0.3000    0.0212
##      3        1.1136             nan     0.3000    0.0130
##      4        1.0838             nan     0.3000    0.0120
##      5        1.0518             nan     0.3000    0.0164
##      6        1.0375             nan     0.3000    0.0003
##      7        1.0213             nan     0.3000    0.0046
##      8        1.0004             nan     0.3000    0.0081
##      9        0.9847             nan     0.3000    0.0048
##     10        0.9748             nan     0.3000    0.0042
##     20        0.9104             nan     0.3000    0.0040
##     40        0.8551             nan     0.3000   -0.0039
##     60        0.8315             nan     0.3000   -0.0011
##     80        0.8039             nan     0.3000   -0.0001
##    100        0.7787             nan     0.3000   -0.0023
##    120        0.7578             nan     0.3000   -0.0016
##    140        0.7472             nan     0.3000   -0.0030
##    160        0.7309             nan     0.3000   -0.0016
##    180        0.7152             nan     0.3000   -0.0026
##    200        0.7053             nan     0.3000   -0.0021
##    220        0.6951             nan     0.3000   -0.0015
##    240        0.6814             nan     0.3000   -0.0008
##    260        0.6764             nan     0.3000   -0.0002
##    280        0.6665             nan     0.3000   -0.0012
##    300        0.6555             nan     0.3000   -0.0040
##    320        0.6473             nan     0.3000   -0.0014
##    340        0.6386             nan     0.3000   -0.0040
##    360        0.6273             nan     0.3000   -0.0011
##    380        0.6222             nan     0.3000   -0.0015
##    400        0.6139             nan     0.3000   -0.0032
##    420        0.6070             nan     0.3000   -0.0024
##    440        0.5983             nan     0.3000   -0.0020
##    460        0.5891             nan     0.3000   -0.0020
##    480        0.5795             nan     0.3000   -0.0010
##    500        0.5759             nan     0.3000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2152             nan     0.3000    0.0296
##      2        1.1567             nan     0.3000    0.0246
##      3        1.1197             nan     0.3000    0.0143
##      4        1.0796             nan     0.3000    0.0161
##      5        1.0443             nan     0.3000    0.0156
##      6        1.0264             nan     0.3000    0.0081
##      7        1.0106             nan     0.3000    0.0034
##      8        0.9959             nan     0.3000    0.0016
##      9        0.9846             nan     0.3000    0.0007
##     10        0.9773             nan     0.3000   -0.0010
##     20        0.9033             nan     0.3000   -0.0026
##     40        0.8586             nan     0.3000   -0.0050
##     60        0.8228             nan     0.3000   -0.0037
##     80        0.7921             nan     0.3000   -0.0022
##    100        0.7696             nan     0.3000   -0.0024
##    120        0.7541             nan     0.3000   -0.0018
##    140        0.7390             nan     0.3000   -0.0027
##    160        0.7217             nan     0.3000    0.0000
##    180        0.7159             nan     0.3000   -0.0014
##    200        0.7019             nan     0.3000   -0.0039
##    220        0.6891             nan     0.3000   -0.0002
##    240        0.6771             nan     0.3000   -0.0028
##    260        0.6676             nan     0.3000   -0.0025
##    280        0.6599             nan     0.3000   -0.0025
##    300        0.6513             nan     0.3000   -0.0030
##    320        0.6378             nan     0.3000   -0.0011
##    340        0.6321             nan     0.3000   -0.0024
##    360        0.6248             nan     0.3000   -0.0019
##    380        0.6133             nan     0.3000   -0.0033
##    400        0.6030             nan     0.3000   -0.0021
##    420        0.6002             nan     0.3000   -0.0020
##    440        0.5983             nan     0.3000   -0.0013
##    460        0.5859             nan     0.3000   -0.0031
##    480        0.5774             nan     0.3000   -0.0011
##    500        0.5733             nan     0.3000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2062             nan     0.3000    0.0361
##      2        1.1528             nan     0.3000    0.0259
##      3        1.1159             nan     0.3000    0.0155
##      4        1.0869             nan     0.3000    0.0089
##      5        1.0526             nan     0.3000    0.0122
##      6        1.0279             nan     0.3000    0.0066
##      7        1.0062             nan     0.3000    0.0038
##      8        0.9938             nan     0.3000    0.0029
##      9        0.9867             nan     0.3000    0.0006
##     10        0.9762             nan     0.3000    0.0015
##     20        0.9098             nan     0.3000   -0.0016
##     40        0.8490             nan     0.3000   -0.0042
##     60        0.8164             nan     0.3000    0.0014
##     80        0.7907             nan     0.3000   -0.0026
##    100        0.7781             nan     0.3000   -0.0040
##    120        0.7618             nan     0.3000   -0.0023
##    140        0.7465             nan     0.3000   -0.0046
##    160        0.7308             nan     0.3000   -0.0049
##    180        0.7183             nan     0.3000   -0.0021
##    200        0.7064             nan     0.3000   -0.0023
##    220        0.6967             nan     0.3000   -0.0017
##    240        0.6837             nan     0.3000   -0.0027
##    260        0.6706             nan     0.3000   -0.0029
##    280        0.6693             nan     0.3000   -0.0047
##    300        0.6579             nan     0.3000   -0.0014
##    320        0.6457             nan     0.3000   -0.0017
##    340        0.6387             nan     0.3000   -0.0026
##    360        0.6306             nan     0.3000   -0.0024
##    380        0.6214             nan     0.3000   -0.0014
##    400        0.6181             nan     0.3000   -0.0041
##    420        0.6134             nan     0.3000   -0.0039
##    440        0.6078             nan     0.3000   -0.0028
##    460        0.6005             nan     0.3000   -0.0030
##    480        0.5923             nan     0.3000   -0.0026
##    500        0.5862             nan     0.3000   -0.0042
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1792             nan     0.3000    0.0544
##      2        1.1260             nan     0.3000    0.0157
##      3        1.0694             nan     0.3000    0.0231
##      4        1.0294             nan     0.3000    0.0140
##      5        1.0037             nan     0.3000    0.0065
##      6        0.9830             nan     0.3000    0.0035
##      7        0.9638             nan     0.3000    0.0019
##      8        0.9443             nan     0.3000    0.0075
##      9        0.9359             nan     0.3000   -0.0048
##     10        0.9252             nan     0.3000   -0.0009
##     20        0.8417             nan     0.3000   -0.0023
##     40        0.7601             nan     0.3000   -0.0027
##     60        0.6967             nan     0.3000   -0.0007
##     80        0.6386             nan     0.3000   -0.0035
##    100        0.5953             nan     0.3000   -0.0030
##    120        0.5535             nan     0.3000   -0.0050
##    140        0.5066             nan     0.3000   -0.0040
##    160        0.4761             nan     0.3000   -0.0013
##    180        0.4414             nan     0.3000    0.0023
##    200        0.4110             nan     0.3000   -0.0038
##    220        0.3853             nan     0.3000   -0.0023
##    240        0.3655             nan     0.3000   -0.0036
##    260        0.3475             nan     0.3000   -0.0037
##    280        0.3212             nan     0.3000   -0.0003
##    300        0.3052             nan     0.3000   -0.0042
##    320        0.2892             nan     0.3000   -0.0026
##    340        0.2785             nan     0.3000   -0.0032
##    360        0.2649             nan     0.3000   -0.0018
##    380        0.2465             nan     0.3000   -0.0010
##    400        0.2382             nan     0.3000   -0.0011
##    420        0.2269             nan     0.3000   -0.0029
##    440        0.2182             nan     0.3000   -0.0015
##    460        0.2063             nan     0.3000   -0.0015
##    480        0.1942             nan     0.3000   -0.0007
##    500        0.1866             nan     0.3000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1616             nan     0.3000    0.0558
##      2        1.0906             nan     0.3000    0.0234
##      3        1.0400             nan     0.3000    0.0243
##      4        1.0083             nan     0.3000    0.0127
##      5        0.9836             nan     0.3000    0.0066
##      6        0.9709             nan     0.3000   -0.0013
##      7        0.9486             nan     0.3000    0.0027
##      8        0.9304             nan     0.3000    0.0023
##      9        0.9140             nan     0.3000    0.0035
##     10        0.9070             nan     0.3000   -0.0003
##     20        0.8451             nan     0.3000   -0.0058
##     40        0.7637             nan     0.3000   -0.0024
##     60        0.7056             nan     0.3000   -0.0034
##     80        0.6450             nan     0.3000    0.0000
##    100        0.5931             nan     0.3000   -0.0018
##    120        0.5538             nan     0.3000   -0.0016
##    140        0.5117             nan     0.3000   -0.0036
##    160        0.4674             nan     0.3000   -0.0053
##    180        0.4302             nan     0.3000   -0.0051
##    200        0.4040             nan     0.3000   -0.0027
##    220        0.3751             nan     0.3000   -0.0052
##    240        0.3542             nan     0.3000   -0.0018
##    260        0.3307             nan     0.3000   -0.0009
##    280        0.3075             nan     0.3000   -0.0024
##    300        0.2927             nan     0.3000   -0.0019
##    320        0.2756             nan     0.3000   -0.0024
##    340        0.2595             nan     0.3000   -0.0009
##    360        0.2463             nan     0.3000   -0.0016
##    380        0.2326             nan     0.3000   -0.0010
##    400        0.2210             nan     0.3000   -0.0011
##    420        0.2080             nan     0.3000   -0.0002
##    440        0.1967             nan     0.3000   -0.0011
##    460        0.1842             nan     0.3000   -0.0025
##    480        0.1767             nan     0.3000   -0.0030
##    500        0.1704             nan     0.3000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1801             nan     0.3000    0.0586
##      2        1.0998             nan     0.3000    0.0265
##      3        1.0555             nan     0.3000    0.0164
##      4        1.0097             nan     0.3000    0.0133
##      5        0.9775             nan     0.3000    0.0095
##      6        0.9562             nan     0.3000    0.0010
##      7        0.9400             nan     0.3000    0.0013
##      8        0.9186             nan     0.3000    0.0048
##      9        0.9090             nan     0.3000    0.0026
##     10        0.9020             nan     0.3000   -0.0053
##     20        0.8245             nan     0.3000   -0.0004
##     40        0.7543             nan     0.3000   -0.0078
##     60        0.6947             nan     0.3000   -0.0061
##     80        0.6348             nan     0.3000   -0.0035
##    100        0.5908             nan     0.3000   -0.0055
##    120        0.5593             nan     0.3000   -0.0065
##    140        0.5172             nan     0.3000   -0.0016
##    160        0.4806             nan     0.3000   -0.0024
##    180        0.4445             nan     0.3000   -0.0026
##    200        0.4165             nan     0.3000   -0.0015
##    220        0.3918             nan     0.3000   -0.0011
##    240        0.3719             nan     0.3000   -0.0021
##    260        0.3588             nan     0.3000   -0.0007
##    280        0.3403             nan     0.3000   -0.0009
##    300        0.3221             nan     0.3000   -0.0005
##    320        0.3028             nan     0.3000   -0.0009
##    340        0.2875             nan     0.3000   -0.0026
##    360        0.2688             nan     0.3000   -0.0035
##    380        0.2576             nan     0.3000   -0.0023
##    400        0.2469             nan     0.3000   -0.0024
##    420        0.2396             nan     0.3000   -0.0021
##    440        0.2269             nan     0.3000   -0.0023
##    460        0.2126             nan     0.3000   -0.0016
##    480        0.2014             nan     0.3000   -0.0017
##    500        0.1918             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1600             nan     0.3000    0.0531
##      2        1.0852             nan     0.3000    0.0206
##      3        1.0433             nan     0.3000    0.0115
##      4        0.9968             nan     0.3000    0.0151
##      5        0.9636             nan     0.3000    0.0104
##      6        0.9391             nan     0.3000   -0.0018
##      7        0.9142             nan     0.3000   -0.0017
##      8        0.9070             nan     0.3000   -0.0106
##      9        0.8926             nan     0.3000   -0.0029
##     10        0.8757             nan     0.3000    0.0049
##     20        0.8055             nan     0.3000   -0.0048
##     40        0.6897             nan     0.3000   -0.0103
##     60        0.5890             nan     0.3000   -0.0024
##     80        0.4915             nan     0.3000   -0.0034
##    100        0.4459             nan     0.3000   -0.0061
##    120        0.4052             nan     0.3000   -0.0048
##    140        0.3584             nan     0.3000   -0.0019
##    160        0.3210             nan     0.3000   -0.0018
##    180        0.2828             nan     0.3000   -0.0016
##    200        0.2487             nan     0.3000   -0.0023
##    220        0.2254             nan     0.3000   -0.0012
##    240        0.2027             nan     0.3000   -0.0020
##    260        0.1870             nan     0.3000   -0.0015
##    280        0.1701             nan     0.3000   -0.0016
##    300        0.1557             nan     0.3000   -0.0008
##    320        0.1433             nan     0.3000   -0.0012
##    340        0.1300             nan     0.3000   -0.0014
##    360        0.1181             nan     0.3000   -0.0003
##    380        0.1100             nan     0.3000   -0.0013
##    400        0.1012             nan     0.3000   -0.0004
##    420        0.0929             nan     0.3000   -0.0006
##    440        0.0842             nan     0.3000   -0.0006
##    460        0.0787             nan     0.3000   -0.0006
##    480        0.0728             nan     0.3000   -0.0009
##    500        0.0665             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1625             nan     0.3000    0.0537
##      2        1.0814             nan     0.3000    0.0324
##      3        1.0348             nan     0.3000    0.0107
##      4        0.9882             nan     0.3000    0.0132
##      5        0.9545             nan     0.3000    0.0089
##      6        0.9304             nan     0.3000   -0.0010
##      7        0.9118             nan     0.3000   -0.0010
##      8        0.8972             nan     0.3000    0.0009
##      9        0.8811             nan     0.3000   -0.0009
##     10        0.8663             nan     0.3000    0.0012
##     20        0.7816             nan     0.3000   -0.0027
##     40        0.6581             nan     0.3000   -0.0072
##     60        0.5724             nan     0.3000   -0.0043
##     80        0.5060             nan     0.3000   -0.0044
##    100        0.4510             nan     0.3000   -0.0059
##    120        0.4032             nan     0.3000   -0.0020
##    140        0.3567             nan     0.3000   -0.0029
##    160        0.3220             nan     0.3000   -0.0027
##    180        0.2850             nan     0.3000   -0.0045
##    200        0.2617             nan     0.3000   -0.0008
##    220        0.2324             nan     0.3000   -0.0009
##    240        0.2120             nan     0.3000   -0.0015
##    260        0.1953             nan     0.3000   -0.0015
##    280        0.1783             nan     0.3000   -0.0023
##    300        0.1619             nan     0.3000   -0.0006
##    320        0.1481             nan     0.3000   -0.0009
##    340        0.1352             nan     0.3000   -0.0021
##    360        0.1234             nan     0.3000   -0.0015
##    380        0.1141             nan     0.3000   -0.0002
##    400        0.1060             nan     0.3000   -0.0021
##    420        0.0995             nan     0.3000   -0.0015
##    440        0.0907             nan     0.3000   -0.0003
##    460        0.0836             nan     0.3000   -0.0002
##    480        0.0781             nan     0.3000   -0.0002
##    500        0.0711             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1621             nan     0.3000    0.0512
##      2        1.0648             nan     0.3000    0.0449
##      3        1.0208             nan     0.3000    0.0157
##      4        0.9868             nan     0.3000    0.0008
##      5        0.9551             nan     0.3000    0.0056
##      6        0.9201             nan     0.3000    0.0126
##      7        0.9032             nan     0.3000    0.0004
##      8        0.8882             nan     0.3000   -0.0013
##      9        0.8809             nan     0.3000   -0.0044
##     10        0.8650             nan     0.3000   -0.0037
##     20        0.7718             nan     0.3000   -0.0071
##     40        0.6563             nan     0.3000   -0.0041
##     60        0.5745             nan     0.3000   -0.0013
##     80        0.5182             nan     0.3000   -0.0042
##    100        0.4472             nan     0.3000   -0.0015
##    120        0.4504             nan     0.3000   -0.0503
##    140        0.3420             nan     0.3000   -0.0027
##    160        0.3017             nan     0.3000   -0.0015
##    180        0.2660             nan     0.3000   -0.0015
##    200        0.2340             nan     0.3000   -0.0019
##    220        0.2126             nan     0.3000   -0.0026
##    240        0.1920             nan     0.3000   -0.0013
##    260        0.1764             nan     0.3000   -0.0010
##    280        0.1590             nan     0.3000   -0.0015
##    300        0.1463             nan     0.3000   -0.0023
##    320        0.1316             nan     0.3000   -0.0008
##    340        0.1212             nan     0.3000   -0.0005
##    360        0.1122             nan     0.3000   -0.0009
##    380        0.1001             nan     0.3000   -0.0004
##    400        0.0913             nan     0.3000   -0.0007
##    420        0.0842             nan     0.3000   -0.0011
##    440        0.0763             nan     0.3000   -0.0005
##    460        0.0715             nan     0.3000   -0.0005
##    480        0.0661             nan     0.3000   -0.0005
##    500        0.0607             nan     0.3000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1618             nan     0.5000    0.0541
##      2        1.0952             nan     0.5000    0.0229
##      3        1.0391             nan     0.5000    0.0233
##      4        1.0171             nan     0.5000    0.0044
##      5        0.9780             nan     0.5000    0.0146
##      6        0.9635             nan     0.5000    0.0037
##      7        0.9534             nan     0.5000   -0.0003
##      8        0.9381             nan     0.5000   -0.0016
##      9        0.9306             nan     0.5000   -0.0004
##     10        0.9194             nan     0.5000    0.0027
##     20        0.8641             nan     0.5000   -0.0088
##     40        0.8212             nan     0.5000   -0.0147
##     60        0.9354             nan     0.5000   -0.0059
##     80        0.9268             nan     0.5000   -0.0007
##    100        0.9192             nan     0.5000   -0.0007
##    120        0.9051             nan     0.5000   -0.0069
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000   -0.0003
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000    0.0000
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000    0.0000
##    460           inf             nan     0.5000   -0.0016
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1649             nan     0.5000    0.0478
##      2        1.1018             nan     0.5000    0.0195
##      3        1.0494             nan     0.5000    0.0220
##      4        1.0060             nan     0.5000    0.0141
##      5        0.9844             nan     0.5000    0.0088
##      6        0.9749             nan     0.5000   -0.0075
##      7        0.9674             nan     0.5000   -0.0022
##      8        0.9520             nan     0.5000   -0.0009
##      9        0.9361             nan     0.5000    0.0023
##     10        0.9310             nan     0.5000   -0.0013
##     20        0.8942             nan     0.5000   -0.0143
##     40        0.8178             nan     0.5000   -0.0061
##     60        0.7901             nan     0.5000   -0.0040
##     80        0.7571             nan     0.5000   -0.0044
##    100        0.7327             nan     0.5000   -0.0027
##    120        0.7198             nan     0.5000   -0.0038
##    140        0.6879             nan     0.5000   -0.0092
##    160        0.6660             nan     0.5000   -0.0019
##    180        0.6595             nan     0.5000   -0.0028
##    200        0.6408             nan     0.5000   -0.0036
##    220        0.6406             nan     0.5000   -0.0014
##    240        0.6276             nan     0.5000   -0.0054
##    260        0.6173             nan     0.5000   -0.0015
##    280        0.5992             nan     0.5000   -0.0029
##    300        0.5940             nan     0.5000   -0.0053
##    320        0.5866             nan     0.5000   -0.0017
##    340        0.5740             nan     0.5000   -0.0012
##    360        0.5606             nan     0.5000   -0.0028
##    380        0.5573             nan     0.5000   -0.0011
##    400        0.5442             nan     0.5000   -0.0028
##    420        0.5387             nan     0.5000   -0.0075
##    440        0.5268             nan     0.5000   -0.0025
##    460        0.5212             nan     0.5000   -0.0048
##    480        0.5034             nan     0.5000   -0.0005
##    500        0.5009             nan     0.5000   -0.0054
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1617             nan     0.5000    0.0603
##      2        1.1046             nan     0.5000    0.0180
##      3        1.0566             nan     0.5000    0.0134
##      4        1.0398             nan     0.5000    0.0007
##      5        0.9933             nan     0.5000    0.0144
##      6        0.9843             nan     0.5000   -0.0007
##      7        0.9614             nan     0.5000    0.0061
##      8        0.9463             nan     0.5000    0.0017
##      9        0.9347             nan     0.5000    0.0041
##     10        0.9277             nan     0.5000    0.0012
##     20        0.8705             nan     0.5000   -0.0009
##     40        0.8224             nan     0.5000   -0.0007
##     60        0.7808             nan     0.5000   -0.0003
##     80        0.7667             nan     0.5000   -0.0068
##    100        0.7440             nan     0.5000   -0.0050
##    120        0.7152             nan     0.5000   -0.0042
##    140        0.7077             nan     0.5000   -0.0074
##    160        0.6850             nan     0.5000   -0.0034
##    180        0.6734             nan     0.5000   -0.0021
##    200        0.6636             nan     0.5000   -0.0019
##    220        0.6492             nan     0.5000   -0.0055
##    240        0.6392             nan     0.5000   -0.0052
##    260        0.6271             nan     0.5000   -0.0063
##    280        0.6182             nan     0.5000   -0.0003
##    300        0.6111             nan     0.5000   -0.0103
##    320        0.5966             nan     0.5000   -0.0060
##    340        0.5906             nan     0.5000   -0.0064
##    360        0.5783             nan     0.5000   -0.0038
##    380        0.5601             nan     0.5000   -0.0019
##    400        0.5476             nan     0.5000   -0.0035
##    420        0.5338             nan     0.5000   -0.0035
##    440        0.5268             nan     0.5000   -0.0049
##    460        0.5242             nan     0.5000   -0.0022
##    480        0.5148             nan     0.5000   -0.0027
##    500        0.5094             nan     0.5000   -0.0056
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1207             nan     0.5000    0.0815
##      2        1.0391             nan     0.5000    0.0311
##      3        0.9924             nan     0.5000    0.0150
##      4        0.9626             nan     0.5000    0.0042
##      5        0.9322             nan     0.5000    0.0057
##      6        0.9024             nan     0.5000   -0.0001
##      7        0.9048             nan     0.5000   -0.0158
##      8        0.8881             nan     0.5000   -0.0038
##      9        0.8796             nan     0.5000   -0.0051
##     10        0.8718             nan     0.5000   -0.0055
##     20           inf             nan     0.5000      -inf
##     40           inf             nan     0.5000       nan
##     60           inf             nan     0.5000   -0.0068
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000   -0.0047
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000   -0.0077
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1281             nan     0.5000    0.0703
##      2        1.0456             nan     0.5000    0.0234
##      3        0.9818             nan     0.5000    0.0206
##      4        0.9578             nan     0.5000   -0.0000
##      5        0.9333             nan     0.5000   -0.0000
##      6        0.9227             nan     0.5000   -0.0042
##      7        0.9032             nan     0.5000   -0.0013
##      8        0.8929             nan     0.5000   -0.0082
##      9        0.8765             nan     0.5000    0.0053
##     10        0.8536             nan     0.5000    0.0022
##     20        0.7827             nan     0.5000   -0.0150
##     40        0.6791             nan     0.5000   -0.0125
##     60        0.6049             nan     0.5000   -0.0066
##     80        0.5661             nan     0.5000   -0.0096
##    100        0.4978             nan     0.5000   -0.0054
##    120        0.4302             nan     0.5000   -0.0030
##    140        0.4052             nan     0.5000   -0.0052
##    160        0.3682             nan     0.5000   -0.0065
##    180        0.3363             nan     0.5000   -0.0123
##    200        0.3132             nan     0.5000   -0.0016
##    220        0.2924             nan     0.5000   -0.0026
##    240        0.2618             nan     0.5000   -0.0013
##    260        0.2419             nan     0.5000   -0.0046
##    280        0.2260             nan     0.5000   -0.0050
##    300        0.2058             nan     0.5000   -0.0014
##    320        0.1875             nan     0.5000   -0.0020
##    340        0.1660             nan     0.5000   -0.0021
##    360        0.1538             nan     0.5000   -0.0019
##    380        0.1421             nan     0.5000   -0.0020
##    400        0.1302             nan     0.5000    0.0001
##    420        0.1223             nan     0.5000   -0.0036
##    440        0.1129             nan     0.5000   -0.0009
##    460        0.1019             nan     0.5000   -0.0014
##    480        0.0978             nan     0.5000   -0.0006
##    500        0.0920             nan     0.5000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1394             nan     0.5000    0.0679
##      2        1.0278             nan     0.5000    0.0398
##      3        0.9900             nan     0.5000    0.0056
##      4        0.9689             nan     0.5000   -0.0042
##      5        0.9431             nan     0.5000    0.0040
##      6        0.9331             nan     0.5000   -0.0085
##      7        0.9068             nan     0.5000    0.0011
##      8        0.8909             nan     0.5000    0.0020
##      9        0.8838             nan     0.5000   -0.0109
##     10        0.8743             nan     0.5000    0.0001
##     20        0.7809             nan     0.5000   -0.0042
##     40        0.6763             nan     0.5000   -0.0095
##     60        0.6236             nan     0.5000   -0.0077
##     80        0.5463             nan     0.5000   -0.0096
##    100        0.5093             nan     0.5000   -0.0066
##    120        0.4812             nan     0.5000   -0.0054
##    140        0.4423             nan     0.5000   -0.0069
##    160        0.3984             nan     0.5000   -0.0022
##    180        0.3547             nan     0.5000   -0.0021
##    200        0.3198             nan     0.5000   -0.0033
##    220        0.2877             nan     0.5000   -0.0000
##    240        0.2576             nan     0.5000    0.0001
##    260        0.2391             nan     0.5000   -0.0041
##    280        0.2165             nan     0.5000   -0.0028
##    300        0.2010             nan     0.5000   -0.0005
##    320        0.1884             nan     0.5000   -0.0020
##    340        0.1774             nan     0.5000   -0.0019
##    360        0.1618             nan     0.5000   -0.0012
##    380        0.1508             nan     0.5000   -0.0031
##    400        0.1396             nan     0.5000   -0.0013
##    420        0.1310             nan     0.5000   -0.0011
##    440        0.1235             nan     0.5000   -0.0010
##    460        0.1120             nan     0.5000   -0.0006
##    480        0.1054             nan     0.5000   -0.0048
##    500        0.0961             nan     0.5000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0830             nan     0.5000    0.0868
##      2        1.0067             nan     0.5000    0.0231
##      3        0.9628             nan     0.5000    0.0072
##      4        0.9328             nan     0.5000   -0.0096
##      5        0.9005             nan     0.5000   -0.0018
##      6        0.8772             nan     0.5000   -0.0038
##      7        0.8523             nan     0.5000   -0.0014
##      8        0.8445             nan     0.5000   -0.0082
##      9        0.8250             nan     0.5000   -0.0067
##     10        0.8223             nan     0.5000   -0.0094
##     20        0.7310             nan     0.5000   -0.0124
##     40        0.6137             nan     0.5000   -0.0135
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1104             nan     0.5000    0.0813
##      2        1.0105             nan     0.5000    0.0396
##      3        0.9653             nan     0.5000    0.0094
##      4        0.9375             nan     0.5000    0.0042
##      5        0.9179             nan     0.5000   -0.0121
##      6        0.8992             nan     0.5000   -0.0077
##      7        0.8805             nan     0.5000   -0.0062
##      8        0.8593             nan     0.5000   -0.0091
##      9        0.8471             nan     0.5000   -0.0073
##     10        0.8449             nan     0.5000   -0.0158
##     20        0.7767             nan     0.5000   -0.0384
##     40 598317428663792278808224688444.0000             nan     0.5000   -0.0152
##     60 598317428663792278808224688444.0000             nan     0.5000   -0.0096
##     80 598317428663792278808224688444.0000             nan     0.5000   -0.0172
##    100 598317428663792278808224688444.0000             nan     0.5000   -0.0039
##    120 598317428663792278808224688444.0000             nan     0.5000    0.0000
##    140 598317428663792278808224688444.0000             nan     0.5000   -0.0026
##    160 598317428663792278808224688444.0000             nan     0.5000   -0.0064
##    180 598317428663792278808224688444.0000             nan     0.5000   -0.0051
##    200 598317428663792278808224688444.0000             nan     0.5000   -0.0013
##    220 598317428663792278808224688444.0000             nan     0.5000   -0.0058
##    240 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000   -0.0003
##    260 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000    0.0001
##    280 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000   -0.0052
##    300 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000   -0.0007
##    320 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000    0.0001
##    340 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000   -0.0006
##    360 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000   -0.0031
##    380 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000    0.0002
##    400 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000    0.0001
##    420 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000   -0.0025
##    440 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000   -0.0000
##    460 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000   -0.0006
##    480 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000   -0.0034
##    500 37684657820914872976028820888800062420824862680266446046460.0000             nan     0.5000   -0.0039
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0829             nan     0.5000    0.0869
##      2        0.9967             nan     0.5000    0.0387
##      3        0.9470             nan     0.5000    0.0120
##      4        0.9146             nan     0.5000   -0.0070
##      5        0.8973             nan     0.5000   -0.0094
##      6        0.8754             nan     0.5000   -0.0083
##      7        0.8661             nan     0.5000   -0.0123
##      8        0.8478             nan     0.5000   -0.0050
##      9        0.8264             nan     0.5000   -0.0053
##     10        0.8269             nan     0.5000   -0.0179
##     20        0.7646             nan     0.5000   -0.0075
##     40        0.6251             nan     0.5000   -0.0088
##     60        0.4927             nan     0.5000   -0.0041
##     80        0.4018             nan     0.5000   -0.0057
##    100        0.3346             nan     0.5000   -0.0057
##    120        0.2794             nan     0.5000   -0.0022
##    140        0.2261             nan     0.5000   -0.0049
##    160        0.1854             nan     0.5000   -0.0059
##    180        0.1610             nan     0.5000   -0.0033
##    200        0.1316             nan     0.5000   -0.0010
##    220        0.1100             nan     0.5000   -0.0010
##    240        0.0914             nan     0.5000   -0.0006
##    260        0.0814             nan     0.5000   -0.0010
##    280        0.0719             nan     0.5000   -0.0009
##    300        0.0606             nan     0.5000   -0.0009
##    320        0.0538             nan     0.5000   -0.0010
##    340        0.0459             nan     0.5000   -0.0003
##    360        0.0405             nan     0.5000   -0.0006
##    380        0.0366             nan     0.5000   -0.0007
##    400        0.0316             nan     0.5000   -0.0006
##    420        0.0274             nan     0.5000   -0.0003
##    440        0.0244             nan     0.5000   -0.0003
##    460        0.0212             nan     0.5000   -0.0004
##    480        0.0184             nan     0.5000   -0.0001
##    500        0.0166             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1207             nan     1.0000    0.0872
##      2        1.0557             nan     1.0000    0.0316
##      3        1.0205             nan     1.0000   -0.0017
##      4        0.9848             nan     1.0000    0.0093
##      5        0.9823             nan     1.0000   -0.0170
##      6        0.9689             nan     1.0000   -0.0018
##      7        0.9796             nan     1.0000   -0.0368
##      8        0.9606             nan     1.0000   -0.0157
##      9        0.9654             nan     1.0000   -0.0216
##     10        0.9620             nan     1.0000   -0.0149
##     20        0.9422             nan     1.0000   -0.0126
##     40     1061.9910             nan     1.0000   -0.0095
##     60     1061.9738             nan     1.0000   -0.0006
##     80     1132.0075             nan     1.0000   -0.0030
##    100     1131.9758             nan     1.0000   -0.0034
##    120     1131.7719             nan     1.0000    0.0043
##    140     1131.7721             nan     1.0000    0.0024
##    160     1131.7498             nan     1.0000   -0.0008
##    180     1131.7411             nan     1.0000   -0.0185
##    200     1131.7481             nan     1.0000   -0.0106
##    220     1131.7596             nan     1.0000   -0.0036
##    240     1131.7978             nan     1.0000    0.0005
##    260     1132.5797             nan     1.0000   -0.0034
##    280     1132.5530             nan     1.0000    0.0008
##    300     1132.5443             nan     1.0000   -0.0047
##    320     1132.5287             nan     1.0000   -0.0000
##    340     1132.5292             nan     1.0000    0.0021
##    360     1132.5243             nan     1.0000    0.0004
##    380     1132.5254             nan     1.0000   -0.0018
##    400     1991.6770             nan     1.0000   -0.0232
##    420     1991.6520             nan     1.0000   -0.0018
##    440     1991.6467             nan     1.0000    0.0002
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1289             nan     1.0000    0.0678
##      2        1.0692             nan     1.0000    0.0046
##      3        1.0020             nan     1.0000    0.0221
##      4        0.9637             nan     1.0000   -0.0022
##      5        0.9463             nan     1.0000    0.0001
##      6        0.9390             nan     1.0000   -0.0035
##      7        0.9458             nan     1.0000   -0.0175
##      8        0.9676             nan     1.0000   -0.0336
##      9        0.9568             nan     1.0000   -0.0033
##     10        0.9573             nan     1.0000   -0.0315
##     20        0.9370             nan     1.0000   -0.0102
##     40        0.8657             nan     1.0000   -0.0277
##     60        0.7966             nan     1.0000   -0.0149
##     80        0.7640             nan     1.0000   -0.0105
##    100        0.7898             nan     1.0000   -0.0308
##    120        1.2617             nan     1.0000   -0.3252
##    140  2882499.8476             nan     1.0000   -0.0005
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1331             nan     1.0000    0.0664
##      2        1.0707             nan     1.0000    0.0202
##      3        1.0179             nan     1.0000    0.0292
##      4        0.9980             nan     1.0000    0.0029
##      5        1.0037             nan     1.0000   -0.0156
##      6        0.9980             nan     1.0000   -0.0102
##      7        0.9968             nan     1.0000   -0.0165
##      8        0.9838             nan     1.0000   -0.0049
##      9        0.9921             nan     1.0000   -0.0237
##     10        0.9913             nan     1.0000   -0.0158
##     20        0.9319             nan     1.0000   -0.0036
##     40  3160436.0324             nan     1.0000   -0.0100
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0514             nan     1.0000    0.1096
##      2        0.9985             nan     1.0000   -0.0183
##      3        0.9581             nan     1.0000    0.0094
##      4        0.9412             nan     1.0000   -0.0133
##      5        0.9333             nan     1.0000   -0.0204
##      6        0.9369             nan     1.0000   -0.0255
##      7        0.9344             nan     1.0000   -0.0320
##      8        0.9224             nan     1.0000   -0.0140
##      9        0.9170             nan     1.0000   -0.0129
##     10        0.9195             nan     1.0000   -0.0387
##     20           inf             nan     1.0000      -inf
##     40           inf             nan     1.0000   -0.0026
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0975             nan     1.0000    0.0956
##      2        1.0195             nan     1.0000    0.0000
##      3        0.9683             nan     1.0000    0.0180
##      4        0.9891             nan     1.0000   -0.0526
##      5        0.9622             nan     1.0000    0.0005
##      6        0.9556             nan     1.0000   -0.0170
##      7        0.9450             nan     1.0000   -0.0161
##      8        0.9240             nan     1.0000   -0.0055
##      9        0.9118             nan     1.0000   -0.0318
##     10        0.8905             nan     1.0000   -0.0033
##     20        4.8194             nan     1.0000   -0.0604
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0872             nan     1.0000    0.0669
##      2        1.0172             nan     1.0000    0.0239
##      3        0.9791             nan     1.0000    0.0013
##      4        0.9504             nan     1.0000   -0.0001
##      5        0.9353             nan     1.0000   -0.0083
##      6        0.9300             nan     1.0000   -0.0312
##      7        0.9009             nan     1.0000   -0.0145
##      8        0.9220             nan     1.0000   -0.0529
##      9        0.8750             nan     1.0000    0.0065
##     10        0.8598             nan     1.0000   -0.0172
##     20        0.8000             nan     1.0000   -0.0206
##     40        0.7806             nan     1.0000    0.0050
##     60        0.9344             nan     1.0000   -0.0727
##     80        0.9053             nan     1.0000   -0.0236
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0965             nan     1.0000    0.0573
##      2        0.9804             nan     1.0000    0.0392
##      3        0.9676             nan     1.0000   -0.0211
##      4        0.9680             nan     1.0000   -0.0411
##      5        0.9726             nan     1.0000   -0.0412
##      6        0.9586             nan     1.0000   -0.0344
##      7        0.9706             nan     1.0000   -0.0538
##      8        0.9879             nan     1.0000   -0.0662
##      9        0.9451             nan     1.0000   -0.0275
##     10        0.9346             nan     1.0000   -0.0393
##     20           inf             nan     1.0000   -0.0645
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000   -0.0586
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0614             nan     1.0000    0.0753
##      2        0.9893             nan     1.0000   -0.0002
##      3        0.9612             nan     1.0000   -0.0157
##      4        0.9528             nan     1.0000   -0.0257
##      5        0.9489             nan     1.0000   -0.0367
##      6        0.9428             nan     1.0000   -0.0349
##      7        0.9263             nan     1.0000   -0.0119
##      8        0.9103             nan     1.0000   -0.0278
##      9        1.4521             nan     1.0000   -0.5902
##     10           inf             nan     1.0000      -inf
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0180             nan     1.0000    0.1013
##      2        0.9809             nan     1.0000   -0.0314
##      3        0.9734             nan     1.0000   -0.0316
##      4        0.9442             nan     1.0000   -0.0184
##      5        0.9003             nan     1.0000    0.0129
##      6        0.8804             nan     1.0000   -0.0305
##      7        0.8996             nan     1.0000   -0.0368
##      8        0.8972             nan     1.0000   -0.0229
##      9        0.9071             nan     1.0000   -0.0435
##     10        0.9334             nan     1.0000   -0.0640
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0001
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0001
##     10        1.2898             nan     0.0010    0.0001
##     20        1.2862             nan     0.0010    0.0002
##     40        1.2794             nan     0.0010    0.0001
##     60        1.2728             nan     0.0010    0.0002
##     80        1.2662             nan     0.0010    0.0001
##    100        1.2601             nan     0.0010    0.0001
##    120        1.2541             nan     0.0010    0.0001
##    140        1.2483             nan     0.0010    0.0001
##    160        1.2430             nan     0.0010    0.0001
##    180        1.2377             nan     0.0010    0.0001
##    200        1.2325             nan     0.0010    0.0001
##    220        1.2274             nan     0.0010    0.0001
##    240        1.2225             nan     0.0010    0.0001
##    260        1.2176             nan     0.0010    0.0001
##    280        1.2129             nan     0.0010    0.0001
##    300        1.2082             nan     0.0010    0.0001
##    320        1.2036             nan     0.0010    0.0001
##    340        1.1994             nan     0.0010    0.0001
##    360        1.1952             nan     0.0010    0.0001
##    380        1.1912             nan     0.0010    0.0001
##    400        1.1870             nan     0.0010    0.0001
##    420        1.1831             nan     0.0010    0.0001
##    440        1.1793             nan     0.0010    0.0001
##    460        1.1756             nan     0.0010    0.0001
##    480        1.1720             nan     0.0010    0.0001
##    500        1.1684             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0001
##      4        1.2920             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0001
##      6        1.2913             nan     0.0010    0.0002
##      7        1.2910             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2899             nan     0.0010    0.0002
##     20        1.2863             nan     0.0010    0.0002
##     40        1.2793             nan     0.0010    0.0002
##     60        1.2728             nan     0.0010    0.0002
##     80        1.2662             nan     0.0010    0.0001
##    100        1.2600             nan     0.0010    0.0001
##    120        1.2542             nan     0.0010    0.0001
##    140        1.2483             nan     0.0010    0.0001
##    160        1.2430             nan     0.0010    0.0001
##    180        1.2374             nan     0.0010    0.0001
##    200        1.2320             nan     0.0010    0.0001
##    220        1.2270             nan     0.0010    0.0001
##    240        1.2220             nan     0.0010    0.0001
##    260        1.2172             nan     0.0010    0.0001
##    280        1.2125             nan     0.0010    0.0001
##    300        1.2078             nan     0.0010    0.0001
##    320        1.2035             nan     0.0010    0.0001
##    340        1.1991             nan     0.0010    0.0001
##    360        1.1949             nan     0.0010    0.0001
##    380        1.1908             nan     0.0010    0.0001
##    400        1.1868             nan     0.0010    0.0001
##    420        1.1830             nan     0.0010    0.0001
##    440        1.1791             nan     0.0010    0.0001
##    460        1.1754             nan     0.0010    0.0001
##    480        1.1717             nan     0.0010    0.0001
##    500        1.1681             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0001
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0001
##     10        1.2899             nan     0.0010    0.0002
##     20        1.2865             nan     0.0010    0.0002
##     40        1.2795             nan     0.0010    0.0002
##     60        1.2727             nan     0.0010    0.0001
##     80        1.2664             nan     0.0010    0.0001
##    100        1.2601             nan     0.0010    0.0001
##    120        1.2543             nan     0.0010    0.0001
##    140        1.2483             nan     0.0010    0.0001
##    160        1.2428             nan     0.0010    0.0001
##    180        1.2374             nan     0.0010    0.0001
##    200        1.2319             nan     0.0010    0.0001
##    220        1.2267             nan     0.0010    0.0001
##    240        1.2217             nan     0.0010    0.0001
##    260        1.2169             nan     0.0010    0.0001
##    280        1.2123             nan     0.0010    0.0001
##    300        1.2076             nan     0.0010    0.0001
##    320        1.2031             nan     0.0010    0.0001
##    340        1.1988             nan     0.0010    0.0001
##    360        1.1945             nan     0.0010    0.0001
##    380        1.1903             nan     0.0010    0.0001
##    400        1.1862             nan     0.0010    0.0001
##    420        1.1823             nan     0.0010    0.0001
##    440        1.1784             nan     0.0010    0.0001
##    460        1.1746             nan     0.0010    0.0001
##    480        1.1709             nan     0.0010    0.0001
##    500        1.1672             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2902             nan     0.0010    0.0002
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2843             nan     0.0010    0.0002
##     40        1.2754             nan     0.0010    0.0002
##     60        1.2667             nan     0.0010    0.0002
##     80        1.2584             nan     0.0010    0.0002
##    100        1.2502             nan     0.0010    0.0002
##    120        1.2426             nan     0.0010    0.0002
##    140        1.2352             nan     0.0010    0.0002
##    160        1.2277             nan     0.0010    0.0002
##    180        1.2207             nan     0.0010    0.0002
##    200        1.2137             nan     0.0010    0.0001
##    220        1.2070             nan     0.0010    0.0001
##    240        1.2005             nan     0.0010    0.0002
##    260        1.1942             nan     0.0010    0.0001
##    280        1.1881             nan     0.0010    0.0001
##    300        1.1819             nan     0.0010    0.0001
##    320        1.1758             nan     0.0010    0.0001
##    340        1.1700             nan     0.0010    0.0001
##    360        1.1645             nan     0.0010    0.0001
##    380        1.1590             nan     0.0010    0.0001
##    400        1.1535             nan     0.0010    0.0001
##    420        1.1483             nan     0.0010    0.0001
##    440        1.1432             nan     0.0010    0.0001
##    460        1.1383             nan     0.0010    0.0001
##    480        1.1336             nan     0.0010    0.0001
##    500        1.1289             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2842             nan     0.0010    0.0002
##     40        1.2754             nan     0.0010    0.0002
##     60        1.2669             nan     0.0010    0.0002
##     80        1.2585             nan     0.0010    0.0002
##    100        1.2503             nan     0.0010    0.0002
##    120        1.2424             nan     0.0010    0.0001
##    140        1.2349             nan     0.0010    0.0002
##    160        1.2274             nan     0.0010    0.0002
##    180        1.2203             nan     0.0010    0.0002
##    200        1.2134             nan     0.0010    0.0001
##    220        1.2066             nan     0.0010    0.0001
##    240        1.2001             nan     0.0010    0.0001
##    260        1.1938             nan     0.0010    0.0001
##    280        1.1878             nan     0.0010    0.0001
##    300        1.1819             nan     0.0010    0.0001
##    320        1.1761             nan     0.0010    0.0001
##    340        1.1703             nan     0.0010    0.0001
##    360        1.1647             nan     0.0010    0.0001
##    380        1.1591             nan     0.0010    0.0001
##    400        1.1538             nan     0.0010    0.0001
##    420        1.1488             nan     0.0010    0.0001
##    440        1.1436             nan     0.0010    0.0001
##    460        1.1386             nan     0.0010    0.0001
##    480        1.1338             nan     0.0010    0.0001
##    500        1.1293             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2921             nan     0.0010    0.0002
##      4        1.2916             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2907             nan     0.0010    0.0002
##      7        1.2902             nan     0.0010    0.0002
##      8        1.2898             nan     0.0010    0.0002
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2889             nan     0.0010    0.0002
##     20        1.2842             nan     0.0010    0.0002
##     40        1.2754             nan     0.0010    0.0002
##     60        1.2669             nan     0.0010    0.0002
##     80        1.2585             nan     0.0010    0.0002
##    100        1.2505             nan     0.0010    0.0002
##    120        1.2429             nan     0.0010    0.0001
##    140        1.2355             nan     0.0010    0.0002
##    160        1.2281             nan     0.0010    0.0002
##    180        1.2211             nan     0.0010    0.0002
##    200        1.2144             nan     0.0010    0.0001
##    220        1.2076             nan     0.0010    0.0001
##    240        1.2010             nan     0.0010    0.0001
##    260        1.1948             nan     0.0010    0.0001
##    280        1.1886             nan     0.0010    0.0001
##    300        1.1826             nan     0.0010    0.0001
##    320        1.1768             nan     0.0010    0.0001
##    340        1.1709             nan     0.0010    0.0001
##    360        1.1652             nan     0.0010    0.0001
##    380        1.1597             nan     0.0010    0.0001
##    400        1.1543             nan     0.0010    0.0001
##    420        1.1491             nan     0.0010    0.0001
##    440        1.1441             nan     0.0010    0.0001
##    460        1.1392             nan     0.0010    0.0001
##    480        1.1341             nan     0.0010    0.0001
##    500        1.1295             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2905             nan     0.0010    0.0003
##      6        1.2900             nan     0.0010    0.0002
##      7        1.2894             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0002
##      9        1.2883             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2827             nan     0.0010    0.0002
##     40        1.2726             nan     0.0010    0.0002
##     60        1.2626             nan     0.0010    0.0002
##     80        1.2530             nan     0.0010    0.0002
##    100        1.2437             nan     0.0010    0.0002
##    120        1.2348             nan     0.0010    0.0002
##    140        1.2260             nan     0.0010    0.0002
##    160        1.2177             nan     0.0010    0.0001
##    180        1.2094             nan     0.0010    0.0002
##    200        1.2014             nan     0.0010    0.0001
##    220        1.1937             nan     0.0010    0.0001
##    240        1.1860             nan     0.0010    0.0002
##    260        1.1788             nan     0.0010    0.0002
##    280        1.1718             nan     0.0010    0.0002
##    300        1.1647             nan     0.0010    0.0001
##    320        1.1579             nan     0.0010    0.0001
##    340        1.1514             nan     0.0010    0.0001
##    360        1.1449             nan     0.0010    0.0002
##    380        1.1387             nan     0.0010    0.0001
##    400        1.1324             nan     0.0010    0.0001
##    420        1.1265             nan     0.0010    0.0001
##    440        1.1208             nan     0.0010    0.0001
##    460        1.1151             nan     0.0010    0.0001
##    480        1.1095             nan     0.0010    0.0001
##    500        1.1043             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2927             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2916             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2905             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2895             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0003
##     10        1.2879             nan     0.0010    0.0002
##     20        1.2825             nan     0.0010    0.0003
##     40        1.2725             nan     0.0010    0.0002
##     60        1.2626             nan     0.0010    0.0002
##     80        1.2531             nan     0.0010    0.0002
##    100        1.2441             nan     0.0010    0.0002
##    120        1.2352             nan     0.0010    0.0002
##    140        1.2266             nan     0.0010    0.0001
##    160        1.2181             nan     0.0010    0.0002
##    180        1.2099             nan     0.0010    0.0002
##    200        1.2022             nan     0.0010    0.0002
##    220        1.1943             nan     0.0010    0.0002
##    240        1.1866             nan     0.0010    0.0002
##    260        1.1792             nan     0.0010    0.0001
##    280        1.1723             nan     0.0010    0.0002
##    300        1.1652             nan     0.0010    0.0001
##    320        1.1584             nan     0.0010    0.0001
##    340        1.1519             nan     0.0010    0.0001
##    360        1.1457             nan     0.0010    0.0002
##    380        1.1393             nan     0.0010    0.0001
##    400        1.1333             nan     0.0010    0.0001
##    420        1.1274             nan     0.0010    0.0001
##    440        1.1216             nan     0.0010    0.0001
##    460        1.1163             nan     0.0010    0.0001
##    480        1.1108             nan     0.0010    0.0001
##    500        1.1055             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0003
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0002
##     20        1.2827             nan     0.0010    0.0002
##     40        1.2723             nan     0.0010    0.0002
##     60        1.2627             nan     0.0010    0.0002
##     80        1.2527             nan     0.0010    0.0002
##    100        1.2437             nan     0.0010    0.0002
##    120        1.2350             nan     0.0010    0.0002
##    140        1.2264             nan     0.0010    0.0001
##    160        1.2180             nan     0.0010    0.0002
##    180        1.2096             nan     0.0010    0.0002
##    200        1.2017             nan     0.0010    0.0001
##    220        1.1939             nan     0.0010    0.0002
##    240        1.1863             nan     0.0010    0.0001
##    260        1.1789             nan     0.0010    0.0001
##    280        1.1718             nan     0.0010    0.0001
##    300        1.1646             nan     0.0010    0.0002
##    320        1.1580             nan     0.0010    0.0001
##    340        1.1513             nan     0.0010    0.0001
##    360        1.1448             nan     0.0010    0.0001
##    380        1.1386             nan     0.0010    0.0001
##    400        1.1327             nan     0.0010    0.0001
##    420        1.1266             nan     0.0010    0.0001
##    440        1.1208             nan     0.0010    0.0001
##    460        1.1152             nan     0.0010    0.0001
##    480        1.1097             nan     0.0010    0.0001
##    500        1.1044             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2592             nan     0.1000    0.0170
##      2        1.2257             nan     0.1000    0.0118
##      3        1.1995             nan     0.1000    0.0095
##      4        1.1788             nan     0.1000    0.0097
##      5        1.1607             nan     0.1000    0.0056
##      6        1.1466             nan     0.1000    0.0063
##      7        1.1292             nan     0.1000    0.0078
##      8        1.1138             nan     0.1000    0.0064
##      9        1.1022             nan     0.1000    0.0047
##     10        1.0895             nan     0.1000    0.0049
##     20        1.0042             nan     0.1000    0.0012
##     40        0.9223             nan     0.1000    0.0010
##     60        0.8827             nan     0.1000   -0.0013
##     80        0.8545             nan     0.1000   -0.0018
##    100        0.8404             nan     0.1000   -0.0007
##    120        0.8275             nan     0.1000   -0.0009
##    140        0.8130             nan     0.1000   -0.0006
##    160        0.8034             nan     0.1000   -0.0003
##    180        0.7935             nan     0.1000   -0.0008
##    200        0.7867             nan     0.1000   -0.0016
##    220        0.7778             nan     0.1000   -0.0011
##    240        0.7730             nan     0.1000   -0.0013
##    260        0.7663             nan     0.1000   -0.0008
##    280        0.7615             nan     0.1000   -0.0007
##    300        0.7568             nan     0.1000   -0.0006
##    320        0.7535             nan     0.1000   -0.0012
##    340        0.7469             nan     0.1000   -0.0008
##    360        0.7420             nan     0.1000   -0.0009
##    380        0.7379             nan     0.1000   -0.0016
##    400        0.7317             nan     0.1000   -0.0010
##    420        0.7277             nan     0.1000   -0.0016
##    440        0.7241             nan     0.1000   -0.0002
##    460        0.7184             nan     0.1000   -0.0007
##    480        0.7155             nan     0.1000   -0.0010
##    500        0.7109             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2646             nan     0.1000    0.0136
##      2        1.2319             nan     0.1000    0.0143
##      3        1.2022             nan     0.1000    0.0102
##      4        1.1806             nan     0.1000    0.0100
##      5        1.1646             nan     0.1000    0.0084
##      6        1.1490             nan     0.1000    0.0070
##      7        1.1347             nan     0.1000    0.0058
##      8        1.1194             nan     0.1000    0.0054
##      9        1.1091             nan     0.1000    0.0039
##     10        1.0992             nan     0.1000    0.0029
##     20        1.0092             nan     0.1000    0.0013
##     40        0.9237             nan     0.1000    0.0001
##     60        0.8824             nan     0.1000   -0.0007
##     80        0.8566             nan     0.1000    0.0002
##    100        0.8401             nan     0.1000   -0.0007
##    120        0.8251             nan     0.1000   -0.0016
##    140        0.8190             nan     0.1000   -0.0011
##    160        0.8096             nan     0.1000   -0.0025
##    180        0.8008             nan     0.1000   -0.0002
##    200        0.7926             nan     0.1000   -0.0010
##    220        0.7860             nan     0.1000   -0.0014
##    240        0.7792             nan     0.1000   -0.0006
##    260        0.7736             nan     0.1000   -0.0005
##    280        0.7670             nan     0.1000   -0.0006
##    300        0.7623             nan     0.1000   -0.0006
##    320        0.7579             nan     0.1000   -0.0009
##    340        0.7529             nan     0.1000   -0.0012
##    360        0.7486             nan     0.1000   -0.0009
##    380        0.7423             nan     0.1000   -0.0011
##    400        0.7376             nan     0.1000   -0.0004
##    420        0.7323             nan     0.1000   -0.0005
##    440        0.7271             nan     0.1000   -0.0009
##    460        0.7226             nan     0.1000   -0.0003
##    480        0.7181             nan     0.1000   -0.0003
##    500        0.7131             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2628             nan     0.1000    0.0168
##      2        1.2312             nan     0.1000    0.0146
##      3        1.2063             nan     0.1000    0.0118
##      4        1.1844             nan     0.1000    0.0090
##      5        1.1695             nan     0.1000    0.0070
##      6        1.1530             nan     0.1000    0.0072
##      7        1.1371             nan     0.1000    0.0039
##      8        1.1215             nan     0.1000    0.0077
##      9        1.1063             nan     0.1000    0.0039
##     10        1.0952             nan     0.1000    0.0036
##     20        1.0011             nan     0.1000    0.0019
##     40        0.9215             nan     0.1000   -0.0003
##     60        0.8826             nan     0.1000   -0.0009
##     80        0.8588             nan     0.1000    0.0001
##    100        0.8377             nan     0.1000   -0.0010
##    120        0.8241             nan     0.1000   -0.0007
##    140        0.8136             nan     0.1000   -0.0006
##    160        0.8033             nan     0.1000   -0.0008
##    180        0.7982             nan     0.1000   -0.0014
##    200        0.7915             nan     0.1000   -0.0007
##    220        0.7850             nan     0.1000   -0.0005
##    240        0.7765             nan     0.1000   -0.0006
##    260        0.7687             nan     0.1000   -0.0009
##    280        0.7630             nan     0.1000   -0.0013
##    300        0.7567             nan     0.1000   -0.0008
##    320        0.7517             nan     0.1000   -0.0008
##    340        0.7445             nan     0.1000   -0.0009
##    360        0.7393             nan     0.1000   -0.0014
##    380        0.7359             nan     0.1000   -0.0011
##    400        0.7308             nan     0.1000   -0.0008
##    420        0.7271             nan     0.1000   -0.0008
##    440        0.7236             nan     0.1000   -0.0005
##    460        0.7186             nan     0.1000   -0.0015
##    480        0.7141             nan     0.1000   -0.0005
##    500        0.7102             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2479             nan     0.1000    0.0235
##      2        1.2137             nan     0.1000    0.0152
##      3        1.1820             nan     0.1000    0.0150
##      4        1.1558             nan     0.1000    0.0126
##      5        1.1292             nan     0.1000    0.0112
##      6        1.1063             nan     0.1000    0.0079
##      7        1.0865             nan     0.1000    0.0069
##      8        1.0688             nan     0.1000    0.0065
##      9        1.0526             nan     0.1000    0.0078
##     10        1.0376             nan     0.1000    0.0047
##     20        0.9410             nan     0.1000    0.0016
##     40        0.8586             nan     0.1000   -0.0010
##     60        0.8106             nan     0.1000   -0.0017
##     80        0.7799             nan     0.1000   -0.0014
##    100        0.7558             nan     0.1000   -0.0007
##    120        0.7309             nan     0.1000    0.0005
##    140        0.7049             nan     0.1000   -0.0009
##    160        0.6823             nan     0.1000   -0.0018
##    180        0.6626             nan     0.1000   -0.0007
##    200        0.6467             nan     0.1000   -0.0006
##    220        0.6277             nan     0.1000   -0.0004
##    240        0.6135             nan     0.1000   -0.0008
##    260        0.5943             nan     0.1000   -0.0012
##    280        0.5809             nan     0.1000   -0.0011
##    300        0.5653             nan     0.1000   -0.0012
##    320        0.5495             nan     0.1000   -0.0010
##    340        0.5330             nan     0.1000   -0.0006
##    360        0.5190             nan     0.1000   -0.0009
##    380        0.5080             nan     0.1000   -0.0014
##    400        0.4949             nan     0.1000   -0.0012
##    420        0.4843             nan     0.1000   -0.0003
##    440        0.4735             nan     0.1000   -0.0003
##    460        0.4635             nan     0.1000   -0.0011
##    480        0.4563             nan     0.1000   -0.0010
##    500        0.4461             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2481             nan     0.1000    0.0189
##      2        1.2092             nan     0.1000    0.0181
##      3        1.1765             nan     0.1000    0.0134
##      4        1.1485             nan     0.1000    0.0133
##      5        1.1248             nan     0.1000    0.0076
##      6        1.1025             nan     0.1000    0.0095
##      7        1.0852             nan     0.1000    0.0056
##      8        1.0678             nan     0.1000    0.0075
##      9        1.0571             nan     0.1000    0.0030
##     10        1.0404             nan     0.1000    0.0069
##     20        0.9429             nan     0.1000    0.0019
##     40        0.8558             nan     0.1000   -0.0001
##     60        0.8131             nan     0.1000    0.0001
##     80        0.7805             nan     0.1000   -0.0027
##    100        0.7525             nan     0.1000   -0.0013
##    120        0.7270             nan     0.1000   -0.0005
##    140        0.7025             nan     0.1000   -0.0006
##    160        0.6819             nan     0.1000   -0.0002
##    180        0.6631             nan     0.1000   -0.0018
##    200        0.6426             nan     0.1000   -0.0012
##    220        0.6277             nan     0.1000   -0.0008
##    240        0.6135             nan     0.1000   -0.0012
##    260        0.5992             nan     0.1000   -0.0009
##    280        0.5833             nan     0.1000   -0.0007
##    300        0.5684             nan     0.1000   -0.0007
##    320        0.5549             nan     0.1000   -0.0005
##    340        0.5423             nan     0.1000   -0.0017
##    360        0.5277             nan     0.1000   -0.0007
##    380        0.5118             nan     0.1000   -0.0006
##    400        0.5031             nan     0.1000   -0.0006
##    420        0.4900             nan     0.1000   -0.0006
##    440        0.4787             nan     0.1000   -0.0005
##    460        0.4686             nan     0.1000   -0.0007
##    480        0.4556             nan     0.1000   -0.0017
##    500        0.4459             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2533             nan     0.1000    0.0215
##      2        1.2143             nan     0.1000    0.0161
##      3        1.1830             nan     0.1000    0.0152
##      4        1.1571             nan     0.1000    0.0097
##      5        1.1300             nan     0.1000    0.0124
##      6        1.1079             nan     0.1000    0.0057
##      7        1.0862             nan     0.1000    0.0071
##      8        1.0688             nan     0.1000    0.0066
##      9        1.0533             nan     0.1000    0.0047
##     10        1.0384             nan     0.1000    0.0070
##     20        0.9409             nan     0.1000    0.0001
##     40        0.8529             nan     0.1000   -0.0009
##     60        0.8105             nan     0.1000   -0.0011
##     80        0.7784             nan     0.1000   -0.0013
##    100        0.7536             nan     0.1000   -0.0010
##    120        0.7288             nan     0.1000   -0.0017
##    140        0.7076             nan     0.1000   -0.0009
##    160        0.6871             nan     0.1000   -0.0012
##    180        0.6678             nan     0.1000   -0.0009
##    200        0.6515             nan     0.1000   -0.0015
##    220        0.6356             nan     0.1000   -0.0006
##    240        0.6139             nan     0.1000   -0.0007
##    260        0.5984             nan     0.1000   -0.0005
##    280        0.5835             nan     0.1000   -0.0006
##    300        0.5708             nan     0.1000   -0.0008
##    320        0.5576             nan     0.1000   -0.0008
##    340        0.5438             nan     0.1000   -0.0013
##    360        0.5321             nan     0.1000   -0.0014
##    380        0.5216             nan     0.1000   -0.0013
##    400        0.5079             nan     0.1000   -0.0009
##    420        0.4949             nan     0.1000   -0.0012
##    440        0.4843             nan     0.1000   -0.0003
##    460        0.4745             nan     0.1000   -0.0010
##    480        0.4623             nan     0.1000   -0.0010
##    500        0.4543             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2435             nan     0.1000    0.0228
##      2        1.2016             nan     0.1000    0.0188
##      3        1.1634             nan     0.1000    0.0179
##      4        1.1285             nan     0.1000    0.0117
##      5        1.1000             nan     0.1000    0.0067
##      6        1.0762             nan     0.1000    0.0091
##      7        1.0541             nan     0.1000    0.0093
##      8        1.0338             nan     0.1000    0.0073
##      9        1.0146             nan     0.1000    0.0082
##     10        1.0014             nan     0.1000    0.0051
##     20        0.9013             nan     0.1000    0.0001
##     40        0.7978             nan     0.1000   -0.0030
##     60        0.7419             nan     0.1000   -0.0014
##     80        0.6975             nan     0.1000   -0.0009
##    100        0.6622             nan     0.1000   -0.0009
##    120        0.6343             nan     0.1000   -0.0013
##    140        0.5969             nan     0.1000   -0.0011
##    160        0.5737             nan     0.1000   -0.0009
##    180        0.5474             nan     0.1000   -0.0008
##    200        0.5192             nan     0.1000   -0.0017
##    220        0.4975             nan     0.1000   -0.0010
##    240        0.4756             nan     0.1000   -0.0009
##    260        0.4565             nan     0.1000   -0.0020
##    280        0.4399             nan     0.1000   -0.0010
##    300        0.4229             nan     0.1000   -0.0014
##    320        0.4036             nan     0.1000   -0.0014
##    340        0.3883             nan     0.1000   -0.0008
##    360        0.3732             nan     0.1000   -0.0007
##    380        0.3572             nan     0.1000   -0.0011
##    400        0.3431             nan     0.1000   -0.0004
##    420        0.3290             nan     0.1000   -0.0005
##    440        0.3157             nan     0.1000   -0.0004
##    460        0.3045             nan     0.1000   -0.0007
##    480        0.2955             nan     0.1000   -0.0012
##    500        0.2835             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2412             nan     0.1000    0.0216
##      2        1.1993             nan     0.1000    0.0167
##      3        1.1635             nan     0.1000    0.0141
##      4        1.1311             nan     0.1000    0.0136
##      5        1.1017             nan     0.1000    0.0112
##      6        1.0784             nan     0.1000    0.0093
##      7        1.0559             nan     0.1000    0.0090
##      8        1.0319             nan     0.1000    0.0087
##      9        1.0168             nan     0.1000    0.0041
##     10        1.0007             nan     0.1000    0.0064
##     20        0.9002             nan     0.1000   -0.0008
##     40        0.7985             nan     0.1000    0.0005
##     60        0.7461             nan     0.1000   -0.0024
##     80        0.7026             nan     0.1000   -0.0024
##    100        0.6697             nan     0.1000   -0.0017
##    120        0.6385             nan     0.1000   -0.0001
##    140        0.6049             nan     0.1000   -0.0006
##    160        0.5780             nan     0.1000   -0.0007
##    180        0.5490             nan     0.1000   -0.0010
##    200        0.5249             nan     0.1000   -0.0007
##    220        0.5002             nan     0.1000   -0.0007
##    240        0.4826             nan     0.1000   -0.0008
##    260        0.4663             nan     0.1000   -0.0019
##    280        0.4438             nan     0.1000   -0.0010
##    300        0.4257             nan     0.1000   -0.0012
##    320        0.4061             nan     0.1000   -0.0008
##    340        0.3899             nan     0.1000   -0.0018
##    360        0.3707             nan     0.1000   -0.0003
##    380        0.3593             nan     0.1000   -0.0017
##    400        0.3465             nan     0.1000   -0.0019
##    420        0.3347             nan     0.1000   -0.0016
##    440        0.3208             nan     0.1000   -0.0010
##    460        0.3083             nan     0.1000   -0.0007
##    480        0.2982             nan     0.1000   -0.0007
##    500        0.2878             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2522             nan     0.1000    0.0166
##      2        1.2083             nan     0.1000    0.0162
##      3        1.1738             nan     0.1000    0.0159
##      4        1.1416             nan     0.1000    0.0146
##      5        1.1143             nan     0.1000    0.0097
##      6        1.0890             nan     0.1000    0.0090
##      7        1.0675             nan     0.1000    0.0093
##      8        1.0503             nan     0.1000    0.0061
##      9        1.0271             nan     0.1000    0.0088
##     10        1.0062             nan     0.1000    0.0054
##     20        0.8960             nan     0.1000    0.0019
##     40        0.8050             nan     0.1000   -0.0011
##     60        0.7520             nan     0.1000   -0.0018
##     80        0.7085             nan     0.1000   -0.0006
##    100        0.6707             nan     0.1000   -0.0012
##    120        0.6372             nan     0.1000   -0.0008
##    140        0.6031             nan     0.1000   -0.0011
##    160        0.5764             nan     0.1000   -0.0005
##    180        0.5489             nan     0.1000   -0.0006
##    200        0.5262             nan     0.1000   -0.0009
##    220        0.5042             nan     0.1000   -0.0007
##    240        0.4861             nan     0.1000   -0.0013
##    260        0.4679             nan     0.1000   -0.0011
##    280        0.4460             nan     0.1000   -0.0012
##    300        0.4281             nan     0.1000   -0.0009
##    320        0.4115             nan     0.1000   -0.0010
##    340        0.3942             nan     0.1000   -0.0011
##    360        0.3775             nan     0.1000   -0.0014
##    380        0.3632             nan     0.1000   -0.0015
##    400        0.3490             nan     0.1000   -0.0013
##    420        0.3341             nan     0.1000   -0.0010
##    440        0.3213             nan     0.1000   -0.0005
##    460        0.3076             nan     0.1000   -0.0001
##    480        0.2971             nan     0.1000   -0.0012
##    500        0.2865             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2437             nan     0.2000    0.0183
##      2        1.1816             nan     0.2000    0.0244
##      3        1.1452             nan     0.2000    0.0163
##      4        1.1159             nan     0.2000    0.0117
##      5        1.0885             nan     0.2000    0.0120
##      6        1.0637             nan     0.2000    0.0121
##      7        1.0468             nan     0.2000    0.0076
##      8        1.0333             nan     0.2000    0.0048
##      9        1.0181             nan     0.2000    0.0037
##     10        1.0046             nan     0.2000    0.0054
##     20        0.9248             nan     0.2000   -0.0038
##     40        0.8596             nan     0.2000    0.0002
##     60        0.8390             nan     0.2000   -0.0030
##     80        0.8112             nan     0.2000   -0.0027
##    100        0.7947             nan     0.2000   -0.0011
##    120        0.7807             nan     0.2000   -0.0033
##    140        0.7718             nan     0.2000   -0.0010
##    160        0.7562             nan     0.2000   -0.0015
##    180        0.7460             nan     0.2000   -0.0019
##    200        0.7339             nan     0.2000   -0.0008
##    220        0.7265             nan     0.2000   -0.0024
##    240        0.7175             nan     0.2000   -0.0018
##    260        0.7070             nan     0.2000   -0.0022
##    280        0.6999             nan     0.2000   -0.0010
##    300        0.6948             nan     0.2000   -0.0017
##    320        0.6897             nan     0.2000   -0.0004
##    340        0.6799             nan     0.2000   -0.0021
##    360        0.6749             nan     0.2000   -0.0007
##    380        0.6673             nan     0.2000   -0.0015
##    400        0.6634             nan     0.2000   -0.0018
##    420        0.6559             nan     0.2000   -0.0007
##    440        0.6545             nan     0.2000   -0.0024
##    460        0.6470             nan     0.2000   -0.0011
##    480        0.6413             nan     0.2000   -0.0019
##    500        0.6353             nan     0.2000   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2362             nan     0.2000    0.0231
##      2        1.1906             nan     0.2000    0.0156
##      3        1.1527             nan     0.2000    0.0170
##      4        1.1238             nan     0.2000    0.0117
##      5        1.1015             nan     0.2000    0.0082
##      6        1.0820             nan     0.2000    0.0074
##      7        1.0663             nan     0.2000    0.0046
##      8        1.0489             nan     0.2000    0.0076
##      9        1.0389             nan     0.2000    0.0028
##     10        1.0197             nan     0.2000    0.0075
##     20        0.9330             nan     0.2000   -0.0011
##     40        0.8725             nan     0.2000   -0.0013
##     60        0.8302             nan     0.2000   -0.0007
##     80        0.8103             nan     0.2000   -0.0034
##    100        0.7964             nan     0.2000   -0.0018
##    120        0.7836             nan     0.2000   -0.0016
##    140        0.7686             nan     0.2000   -0.0013
##    160        0.7581             nan     0.2000   -0.0012
##    180        0.7513             nan     0.2000   -0.0042
##    200        0.7427             nan     0.2000   -0.0023
##    220        0.7357             nan     0.2000   -0.0029
##    240        0.7254             nan     0.2000   -0.0005
##    260        0.7162             nan     0.2000   -0.0019
##    280        0.7148             nan     0.2000   -0.0042
##    300        0.7055             nan     0.2000   -0.0028
##    320        0.6955             nan     0.2000   -0.0021
##    340        0.6860             nan     0.2000   -0.0020
##    360        0.6829             nan     0.2000   -0.0017
##    380        0.6708             nan     0.2000   -0.0013
##    400        0.6653             nan     0.2000   -0.0009
##    420        0.6595             nan     0.2000   -0.0025
##    440        0.6530             nan     0.2000   -0.0005
##    460        0.6455             nan     0.2000   -0.0004
##    480        0.6441             nan     0.2000   -0.0018
##    500        0.6376             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2315             nan     0.2000    0.0266
##      2        1.1838             nan     0.2000    0.0232
##      3        1.1487             nan     0.2000    0.0184
##      4        1.1208             nan     0.2000    0.0117
##      5        1.0963             nan     0.2000    0.0067
##      6        1.0669             nan     0.2000    0.0089
##      7        1.0445             nan     0.2000    0.0074
##      8        1.0310             nan     0.2000    0.0026
##      9        1.0177             nan     0.2000    0.0027
##     10        1.0004             nan     0.2000    0.0068
##     20        0.9255             nan     0.2000    0.0001
##     40        0.8614             nan     0.2000   -0.0008
##     60        0.8267             nan     0.2000   -0.0026
##     80        0.8043             nan     0.2000   -0.0020
##    100        0.7846             nan     0.2000   -0.0021
##    120        0.7756             nan     0.2000   -0.0018
##    140        0.7614             nan     0.2000   -0.0001
##    160        0.7546             nan     0.2000   -0.0019
##    180        0.7435             nan     0.2000   -0.0015
##    200        0.7297             nan     0.2000   -0.0010
##    220        0.7211             nan     0.2000   -0.0011
##    240        0.7093             nan     0.2000   -0.0016
##    260        0.7021             nan     0.2000   -0.0020
##    280        0.6936             nan     0.2000   -0.0003
##    300        0.6879             nan     0.2000   -0.0004
##    320        0.6833             nan     0.2000   -0.0019
##    340        0.6779             nan     0.2000   -0.0036
##    360        0.6717             nan     0.2000   -0.0020
##    380        0.6638             nan     0.2000   -0.0010
##    400        0.6592             nan     0.2000   -0.0006
##    420        0.6514             nan     0.2000   -0.0014
##    440        0.6457             nan     0.2000   -0.0005
##    460        0.6405             nan     0.2000   -0.0017
##    480        0.6335             nan     0.2000   -0.0014
##    500        0.6288             nan     0.2000   -0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2183             nan     0.2000    0.0311
##      2        1.1611             nan     0.2000    0.0213
##      3        1.1054             nan     0.2000    0.0236
##      4        1.0710             nan     0.2000    0.0116
##      5        1.0435             nan     0.2000    0.0094
##      6        1.0110             nan     0.2000    0.0111
##      7        0.9849             nan     0.2000    0.0068
##      8        0.9708             nan     0.2000    0.0038
##      9        0.9575             nan     0.2000    0.0049
##     10        0.9486             nan     0.2000   -0.0049
##     20        0.8601             nan     0.2000   -0.0001
##     40        0.7809             nan     0.2000   -0.0034
##     60        0.7232             nan     0.2000   -0.0037
##     80        0.6810             nan     0.2000   -0.0017
##    100        0.6443             nan     0.2000   -0.0037
##    120        0.6165             nan     0.2000   -0.0035
##    140        0.5836             nan     0.2000   -0.0030
##    160        0.5600             nan     0.2000   -0.0016
##    180        0.5351             nan     0.2000   -0.0018
##    200        0.5093             nan     0.2000   -0.0016
##    220        0.4871             nan     0.2000   -0.0028
##    240        0.4647             nan     0.2000   -0.0011
##    260        0.4465             nan     0.2000   -0.0021
##    280        0.4309             nan     0.2000   -0.0016
##    300        0.4104             nan     0.2000   -0.0037
##    320        0.3926             nan     0.2000   -0.0029
##    340        0.3770             nan     0.2000   -0.0006
##    360        0.3628             nan     0.2000   -0.0022
##    380        0.3462             nan     0.2000   -0.0018
##    400        0.3316             nan     0.2000   -0.0019
##    420        0.3155             nan     0.2000   -0.0009
##    440        0.3022             nan     0.2000   -0.0016
##    460        0.2911             nan     0.2000   -0.0010
##    480        0.2803             nan     0.2000   -0.0005
##    500        0.2727             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2158             nan     0.2000    0.0348
##      2        1.1459             nan     0.2000    0.0293
##      3        1.1093             nan     0.2000    0.0147
##      4        1.0637             nan     0.2000    0.0196
##      5        1.0272             nan     0.2000    0.0141
##      6        1.0011             nan     0.2000    0.0107
##      7        0.9893             nan     0.2000    0.0011
##      8        0.9694             nan     0.2000    0.0058
##      9        0.9553             nan     0.2000    0.0014
##     10        0.9480             nan     0.2000   -0.0018
##     20        0.8623             nan     0.2000    0.0002
##     40        0.7823             nan     0.2000   -0.0025
##     60        0.7179             nan     0.2000   -0.0015
##     80        0.6835             nan     0.2000   -0.0015
##    100        0.6472             nan     0.2000   -0.0006
##    120        0.6225             nan     0.2000   -0.0022
##    140        0.5996             nan     0.2000   -0.0012
##    160        0.5740             nan     0.2000   -0.0025
##    180        0.5514             nan     0.2000   -0.0036
##    200        0.5247             nan     0.2000   -0.0022
##    220        0.5043             nan     0.2000   -0.0011
##    240        0.4820             nan     0.2000   -0.0000
##    260        0.4654             nan     0.2000   -0.0010
##    280        0.4502             nan     0.2000   -0.0022
##    300        0.4314             nan     0.2000   -0.0011
##    320        0.4173             nan     0.2000   -0.0024
##    340        0.4009             nan     0.2000   -0.0007
##    360        0.3828             nan     0.2000   -0.0018
##    380        0.3673             nan     0.2000    0.0003
##    400        0.3558             nan     0.2000   -0.0009
##    420        0.3444             nan     0.2000   -0.0019
##    440        0.3303             nan     0.2000   -0.0015
##    460        0.3124             nan     0.2000   -0.0010
##    480        0.3023             nan     0.2000   -0.0009
##    500        0.2919             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2165             nan     0.2000    0.0367
##      2        1.1613             nan     0.2000    0.0178
##      3        1.1182             nan     0.2000    0.0193
##      4        1.0732             nan     0.2000    0.0152
##      5        1.0357             nan     0.2000    0.0100
##      6        1.0128             nan     0.2000    0.0079
##      7        0.9844             nan     0.2000    0.0113
##      8        0.9649             nan     0.2000    0.0064
##      9        0.9511             nan     0.2000    0.0023
##     10        0.9390             nan     0.2000    0.0001
##     20        0.8568             nan     0.2000   -0.0008
##     40        0.7802             nan     0.2000   -0.0020
##     60        0.7325             nan     0.2000   -0.0007
##     80        0.6931             nan     0.2000   -0.0038
##    100        0.6536             nan     0.2000   -0.0029
##    120        0.6266             nan     0.2000   -0.0007
##    140        0.5899             nan     0.2000   -0.0031
##    160        0.5621             nan     0.2000   -0.0025
##    180        0.5359             nan     0.2000   -0.0003
##    200        0.5124             nan     0.2000   -0.0040
##    220        0.4912             nan     0.2000   -0.0022
##    240        0.4693             nan     0.2000   -0.0022
##    260        0.4498             nan     0.2000   -0.0001
##    280        0.4297             nan     0.2000   -0.0023
##    300        0.4109             nan     0.2000   -0.0037
##    320        0.3905             nan     0.2000   -0.0015
##    340        0.3731             nan     0.2000   -0.0017
##    360        0.3614             nan     0.2000   -0.0035
##    380        0.3514             nan     0.2000   -0.0016
##    400        0.3388             nan     0.2000   -0.0013
##    420        0.3264             nan     0.2000   -0.0018
##    440        0.3141             nan     0.2000   -0.0005
##    460        0.3017             nan     0.2000   -0.0006
##    480        0.2958             nan     0.2000   -0.0026
##    500        0.2822             nan     0.2000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1978             nan     0.2000    0.0407
##      2        1.1329             nan     0.2000    0.0258
##      3        1.0870             nan     0.2000    0.0180
##      4        1.0381             nan     0.2000    0.0188
##      5        1.0093             nan     0.2000    0.0027
##      6        0.9865             nan     0.2000    0.0030
##      7        0.9596             nan     0.2000    0.0095
##      8        0.9365             nan     0.2000    0.0057
##      9        0.9175             nan     0.2000    0.0024
##     10        0.9054             nan     0.2000   -0.0027
##     20        0.8100             nan     0.2000    0.0000
##     40        0.7185             nan     0.2000   -0.0025
##     60        0.6423             nan     0.2000    0.0001
##     80        0.5868             nan     0.2000   -0.0037
##    100        0.5245             nan     0.2000   -0.0005
##    120        0.4812             nan     0.2000   -0.0028
##    140        0.4385             nan     0.2000   -0.0023
##    160        0.4001             nan     0.2000   -0.0025
##    180        0.3669             nan     0.2000   -0.0002
##    200        0.3355             nan     0.2000   -0.0015
##    220        0.3085             nan     0.2000   -0.0015
##    240        0.2878             nan     0.2000   -0.0032
##    260        0.2731             nan     0.2000   -0.0010
##    280        0.2546             nan     0.2000   -0.0019
##    300        0.2395             nan     0.2000   -0.0014
##    320        0.2265             nan     0.2000    0.0002
##    340        0.2144             nan     0.2000   -0.0010
##    360        0.2032             nan     0.2000   -0.0011
##    380        0.1913             nan     0.2000   -0.0009
##    400        0.1805             nan     0.2000   -0.0015
##    420        0.1730             nan     0.2000   -0.0011
##    440        0.1634             nan     0.2000   -0.0014
##    460        0.1544             nan     0.2000   -0.0011
##    480        0.1457             nan     0.2000   -0.0005
##    500        0.1368             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2008             nan     0.2000    0.0358
##      2        1.1351             nan     0.2000    0.0300
##      3        1.0872             nan     0.2000    0.0142
##      4        1.0417             nan     0.2000    0.0135
##      5        1.0109             nan     0.2000    0.0099
##      6        0.9925             nan     0.2000    0.0021
##      7        0.9648             nan     0.2000    0.0103
##      8        0.9479             nan     0.2000    0.0043
##      9        0.9332             nan     0.2000   -0.0005
##     10        0.9191             nan     0.2000    0.0016
##     20        0.8154             nan     0.2000    0.0002
##     40        0.6935             nan     0.2000   -0.0012
##     60        0.6311             nan     0.2000   -0.0024
##     80        0.5703             nan     0.2000   -0.0049
##    100        0.5223             nan     0.2000    0.0002
##    120        0.4864             nan     0.2000   -0.0039
##    140        0.4525             nan     0.2000   -0.0021
##    160        0.4199             nan     0.2000   -0.0014
##    180        0.3863             nan     0.2000   -0.0012
##    200        0.3582             nan     0.2000   -0.0010
##    220        0.3302             nan     0.2000   -0.0014
##    240        0.3059             nan     0.2000   -0.0014
##    260        0.2843             nan     0.2000   -0.0013
##    280        0.2659             nan     0.2000   -0.0023
##    300        0.2442             nan     0.2000   -0.0006
##    320        0.2308             nan     0.2000   -0.0013
##    340        0.2165             nan     0.2000   -0.0012
##    360        0.2023             nan     0.2000   -0.0006
##    380        0.1904             nan     0.2000   -0.0012
##    400        0.1788             nan     0.2000   -0.0009
##    420        0.1694             nan     0.2000   -0.0010
##    440        0.1594             nan     0.2000   -0.0008
##    460        0.1513             nan     0.2000   -0.0010
##    480        0.1419             nan     0.2000   -0.0005
##    500        0.1317             nan     0.2000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2053             nan     0.2000    0.0318
##      2        1.1315             nan     0.2000    0.0257
##      3        1.0693             nan     0.2000    0.0290
##      4        1.0306             nan     0.2000    0.0102
##      5        0.9964             nan     0.2000    0.0096
##      6        0.9648             nan     0.2000    0.0111
##      7        0.9432             nan     0.2000    0.0060
##      8        0.9241             nan     0.2000    0.0069
##      9        0.9128             nan     0.2000   -0.0005
##     10        0.8958             nan     0.2000    0.0028
##     20        0.8127             nan     0.2000   -0.0030
##     40        0.7147             nan     0.2000   -0.0035
##     60        0.6476             nan     0.2000   -0.0028
##     80        0.5852             nan     0.2000   -0.0005
##    100        0.5313             nan     0.2000   -0.0042
##    120        0.4890             nan     0.2000   -0.0029
##    140        0.4602             nan     0.2000   -0.0020
##    160        0.4230             nan     0.2000   -0.0015
##    180        0.3900             nan     0.2000   -0.0028
##    200        0.3699             nan     0.2000   -0.0014
##    220        0.3473             nan     0.2000   -0.0022
##    240        0.3178             nan     0.2000   -0.0033
##    260        0.2978             nan     0.2000   -0.0009
##    280        0.2715             nan     0.2000   -0.0015
##    300        0.2561             nan     0.2000   -0.0015
##    320        0.2381             nan     0.2000   -0.0016
##    340        0.2235             nan     0.2000    0.0000
##    360        0.2115             nan     0.2000   -0.0019
##    380        0.1973             nan     0.2000   -0.0010
##    400        0.1893             nan     0.2000   -0.0017
##    420        0.1755             nan     0.2000   -0.0010
##    440        0.1624             nan     0.2000   -0.0006
##    460        0.1527             nan     0.2000   -0.0012
##    480        0.1445             nan     0.2000   -0.0017
##    500        0.1354             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2062             nan     0.3000    0.0435
##      2        1.1527             nan     0.3000    0.0231
##      3        1.1109             nan     0.3000    0.0172
##      4        1.0745             nan     0.3000    0.0143
##      5        1.0444             nan     0.3000    0.0123
##      6        1.0175             nan     0.3000    0.0118
##      7        1.0050             nan     0.3000   -0.0012
##      8        0.9867             nan     0.3000    0.0038
##      9        0.9776             nan     0.3000    0.0022
##     10        0.9612             nan     0.3000    0.0058
##     20        0.8930             nan     0.3000   -0.0030
##     40        0.8393             nan     0.3000   -0.0030
##     60        0.8142             nan     0.3000   -0.0012
##     80        0.7858             nan     0.3000    0.0001
##    100        0.7706             nan     0.3000   -0.0032
##    120        0.7533             nan     0.3000   -0.0031
##    140        0.7431             nan     0.3000   -0.0055
##    160        0.7264             nan     0.3000   -0.0017
##    180        0.7156             nan     0.3000   -0.0012
##    200        0.7098             nan     0.3000   -0.0007
##    220        0.6995             nan     0.3000   -0.0025
##    240        0.6869             nan     0.3000   -0.0050
##    260        0.6741             nan     0.3000   -0.0018
##    280        0.6630             nan     0.3000   -0.0038
##    300        0.6503             nan     0.3000   -0.0011
##    320        0.6370             nan     0.3000   -0.0054
##    340        0.6340             nan     0.3000   -0.0005
##    360        0.6240             nan     0.3000   -0.0019
##    380        0.6157             nan     0.3000   -0.0034
##    400        0.6092             nan     0.3000   -0.0035
##    420        0.5983             nan     0.3000   -0.0018
##    440        0.5919             nan     0.3000   -0.0023
##    460        0.5883             nan     0.3000   -0.0032
##    480        0.5801             nan     0.3000   -0.0001
##    500        0.5771             nan     0.3000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1961             nan     0.3000    0.0425
##      2        1.1333             nan     0.3000    0.0236
##      3        1.0986             nan     0.3000    0.0147
##      4        1.0657             nan     0.3000    0.0154
##      5        1.0341             nan     0.3000    0.0114
##      6        1.0211             nan     0.3000    0.0023
##      7        1.0046             nan     0.3000    0.0045
##      8        0.9794             nan     0.3000    0.0091
##      9        0.9666             nan     0.3000    0.0011
##     10        0.9505             nan     0.3000    0.0066
##     20        0.8719             nan     0.3000   -0.0001
##     40        0.8243             nan     0.3000   -0.0007
##     60        0.8042             nan     0.3000   -0.0021
##     80        0.7811             nan     0.3000   -0.0055
##    100        0.7612             nan     0.3000   -0.0033
##    120        0.7508             nan     0.3000   -0.0027
##    140        0.7344             nan     0.3000   -0.0059
##    160        0.7205             nan     0.3000   -0.0038
##    180        0.7054             nan     0.3000   -0.0063
##    200        0.6914             nan     0.3000   -0.0045
##    220        0.6770             nan     0.3000   -0.0016
##    240        0.6673             nan     0.3000   -0.0016
##    260        0.6619             nan     0.3000   -0.0015
##    280        0.6503             nan     0.3000   -0.0018
##    300        0.6407             nan     0.3000   -0.0071
##    320        0.6251             nan     0.3000   -0.0032
##    340        0.6196             nan     0.3000   -0.0004
##    360        0.6120             nan     0.3000   -0.0033
##    380        0.6019             nan     0.3000   -0.0003
##    400        0.5932             nan     0.3000   -0.0014
##    420        0.5858             nan     0.3000   -0.0012
##    440        0.5805             nan     0.3000   -0.0011
##    460        0.5730             nan     0.3000   -0.0047
##    480        0.5678             nan     0.3000   -0.0019
##    500        0.5625             nan     0.3000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2087             nan     0.3000    0.0361
##      2        1.1622             nan     0.3000    0.0195
##      3        1.0986             nan     0.3000    0.0280
##      4        1.0635             nan     0.3000    0.0142
##      5        1.0294             nan     0.3000    0.0133
##      6        1.0112             nan     0.3000    0.0045
##      7        0.9975             nan     0.3000   -0.0034
##      8        0.9810             nan     0.3000    0.0053
##      9        0.9616             nan     0.3000    0.0043
##     10        0.9549             nan     0.3000   -0.0011
##     20        0.8815             nan     0.3000   -0.0027
##     40        0.8408             nan     0.3000   -0.0021
##     60        0.8091             nan     0.3000   -0.0008
##     80        0.7886             nan     0.3000   -0.0012
##    100        0.7747             nan     0.3000   -0.0044
##    120        0.7587             nan     0.3000   -0.0043
##    140        0.7488             nan     0.3000   -0.0063
##    160        0.7415             nan     0.3000   -0.0002
##    180        0.7210             nan     0.3000   -0.0014
##    200        0.7112             nan     0.3000   -0.0020
##    220        0.6996             nan     0.3000   -0.0003
##    240        0.6872             nan     0.3000   -0.0036
##    260        0.6794             nan     0.3000   -0.0023
##    280        0.6674             nan     0.3000   -0.0030
##    300        0.6634             nan     0.3000   -0.0031
##    320        0.6501             nan     0.3000   -0.0039
##    340        0.6436             nan     0.3000   -0.0017
##    360        0.6341             nan     0.3000   -0.0023
##    380        0.6236             nan     0.3000   -0.0053
##    400        0.6201             nan     0.3000   -0.0013
##    420        0.6089             nan     0.3000   -0.0012
##    440        0.6012             nan     0.3000   -0.0037
##    460        0.6015             nan     0.3000   -0.0012
##    480        0.5927             nan     0.3000   -0.0012
##    500        0.5881             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1822             nan     0.3000    0.0544
##      2        1.1064             nan     0.3000    0.0340
##      3        1.0535             nan     0.3000    0.0169
##      4        1.0127             nan     0.3000    0.0106
##      5        0.9815             nan     0.3000    0.0102
##      6        0.9562             nan     0.3000    0.0090
##      7        0.9473             nan     0.3000   -0.0018
##      8        0.9288             nan     0.3000    0.0038
##      9        0.9187             nan     0.3000   -0.0056
##     10        0.9015             nan     0.3000    0.0023
##     20        0.8268             nan     0.3000   -0.0027
##     40        0.7413             nan     0.3000   -0.0027
##     60        0.6833             nan     0.3000   -0.0036
##     80        0.6356             nan     0.3000   -0.0034
##    100        0.5925             nan     0.3000   -0.0031
##    120        0.5475             nan     0.3000   -0.0028
##    140        0.5026             nan     0.3000   -0.0061
##    160        0.4797             nan     0.3000   -0.0061
##    180        0.4520             nan     0.3000   -0.0039
##    200        0.4092             nan     0.3000   -0.0029
##    220        0.3815             nan     0.3000   -0.0016
##    240        0.3501             nan     0.3000   -0.0023
##    260        0.3317             nan     0.3000   -0.0056
##    280        0.3106             nan     0.3000   -0.0005
##    300        0.2944             nan     0.3000   -0.0020
##    320        0.2805             nan     0.3000   -0.0036
##    340        0.2621             nan     0.3000   -0.0032
##    360        0.2480             nan     0.3000   -0.0008
##    380        0.2338             nan     0.3000   -0.0005
##    400        0.2184             nan     0.3000   -0.0011
##    420        0.2083             nan     0.3000   -0.0017
##    440        0.2007             nan     0.3000   -0.0012
##    460        0.1868             nan     0.3000   -0.0006
##    480        0.1745             nan     0.3000   -0.0005
##    500        0.1673             nan     0.3000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1776             nan     0.3000    0.0431
##      2        1.1056             nan     0.3000    0.0309
##      3        1.0518             nan     0.3000    0.0204
##      4        1.0122             nan     0.3000    0.0137
##      5        0.9877             nan     0.3000    0.0048
##      6        0.9628             nan     0.3000    0.0024
##      7        0.9434             nan     0.3000    0.0070
##      8        0.9219             nan     0.3000    0.0029
##      9        0.9083             nan     0.3000    0.0026
##     10        0.8974             nan     0.3000   -0.0003
##     20        0.8238             nan     0.3000   -0.0035
##     40        0.7272             nan     0.3000   -0.0031
##     60        0.6668             nan     0.3000   -0.0060
##     80        0.6235             nan     0.3000   -0.0053
##    100        0.5835             nan     0.3000   -0.0077
##    120        0.5380             nan     0.3000   -0.0004
##    140        0.5019             nan     0.3000   -0.0036
##    160        0.4734             nan     0.3000   -0.0002
##    180        0.4466             nan     0.3000   -0.0031
##    200        0.4178             nan     0.3000   -0.0002
##    220        0.3917             nan     0.3000   -0.0033
##    240        0.3683             nan     0.3000   -0.0037
##    260        0.3394             nan     0.3000   -0.0010
##    280        0.3148             nan     0.3000   -0.0025
##    300        0.2939             nan     0.3000   -0.0023
##    320        0.2790             nan     0.3000   -0.0010
##    340        0.2654             nan     0.3000   -0.0016
##    360        0.2493             nan     0.3000   -0.0002
##    380        0.2347             nan     0.3000   -0.0016
##    400        0.2261             nan     0.3000   -0.0027
##    420        0.2172             nan     0.3000   -0.0014
##    440        0.2044             nan     0.3000   -0.0008
##    460        0.1964             nan     0.3000   -0.0015
##    480        0.1889             nan     0.3000   -0.0022
##    500        0.1802             nan     0.3000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1878             nan     0.3000    0.0384
##      2        1.1245             nan     0.3000    0.0225
##      3        1.0616             nan     0.3000    0.0269
##      4        1.0229             nan     0.3000    0.0190
##      5        0.9828             nan     0.3000    0.0134
##      6        0.9529             nan     0.3000    0.0113
##      7        0.9322             nan     0.3000    0.0060
##      8        0.9206             nan     0.3000    0.0009
##      9        0.9096             nan     0.3000   -0.0028
##     10        0.8922             nan     0.3000    0.0036
##     20        0.8228             nan     0.3000   -0.0026
##     40        0.7218             nan     0.3000   -0.0023
##     60        0.6614             nan     0.3000   -0.0064
##     80        0.5994             nan     0.3000   -0.0038
##    100        0.5683             nan     0.3000   -0.0030
##    120        0.5248             nan     0.3000    0.0006
##    140        0.4873             nan     0.3000   -0.0047
##    160        0.4478             nan     0.3000   -0.0020
##    180        0.4217             nan     0.3000   -0.0011
##    200        0.4025             nan     0.3000   -0.0003
##    220        0.3773             nan     0.3000   -0.0028
##    240        0.3599             nan     0.3000   -0.0030
##    260        0.3415             nan     0.3000   -0.0010
##    280        0.3159             nan     0.3000   -0.0012
##    300        0.2987             nan     0.3000   -0.0016
##    320        0.2870             nan     0.3000   -0.0036
##    340        0.2689             nan     0.3000   -0.0022
##    360        0.2526             nan     0.3000   -0.0014
##    380        0.2343             nan     0.3000   -0.0018
##    400        0.2254             nan     0.3000   -0.0031
##    420        0.2128             nan     0.3000   -0.0014
##    440        0.2015             nan     0.3000   -0.0006
##    460        0.1936             nan     0.3000   -0.0015
##    480        0.1838             nan     0.3000   -0.0006
##    500        0.1748             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1557             nan     0.3000    0.0547
##      2        1.0569             nan     0.3000    0.0420
##      3        1.0004             nan     0.3000    0.0234
##      4        0.9587             nan     0.3000    0.0103
##      5        0.9416             nan     0.3000   -0.0043
##      6        0.9186             nan     0.3000   -0.0029
##      7        0.8947             nan     0.3000    0.0057
##      8        0.8757             nan     0.3000   -0.0008
##      9        0.8600             nan     0.3000   -0.0001
##     10        0.8522             nan     0.3000   -0.0015
##     20        0.7712             nan     0.3000   -0.0078
##     40        0.6651             nan     0.3000   -0.0029
##     60        0.6255             nan     0.3000   -0.0029
##     80        0.5304             nan     0.3000   -0.0055
##    100        0.4788             nan     0.3000   -0.0005
##    120        0.3892             nan     0.3000   -0.0022
##    140        0.3337             nan     0.3000   -0.0029
##    160        0.2943             nan     0.3000   -0.0043
##    180        0.2593             nan     0.3000   -0.0010
##    200        0.2366             nan     0.3000   -0.0014
##    220        0.2160             nan     0.3000   -0.0015
##    240        0.1984             nan     0.3000   -0.0008
##    260        0.1830             nan     0.3000   -0.0010
##    280        0.1651             nan     0.3000   -0.0005
##    300        0.1525             nan     0.3000   -0.0010
##    320        0.1372             nan     0.3000   -0.0013
##    340        0.1256             nan     0.3000   -0.0003
##    360        0.1179             nan     0.3000   -0.0005
##    380        0.1086             nan     0.3000   -0.0004
##    400        0.1012             nan     0.3000   -0.0016
##    420        0.0934             nan     0.3000   -0.0004
##    440        0.0868             nan     0.3000   -0.0011
##    460        0.0799             nan     0.3000   -0.0006
##    480        0.0727             nan     0.3000   -0.0008
##    500        0.0665             nan     0.3000   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1522             nan     0.3000    0.0593
##      2        1.0709             nan     0.3000    0.0330
##      3        1.0236             nan     0.3000    0.0124
##      4        0.9786             nan     0.3000    0.0118
##      5        0.9471             nan     0.3000    0.0122
##      6        0.9242             nan     0.3000    0.0041
##      7        0.9054             nan     0.3000   -0.0012
##      8        0.8822             nan     0.3000    0.0033
##      9        0.8672             nan     0.3000   -0.0015
##     10        0.8514             nan     0.3000   -0.0063
##     20        0.7694             nan     0.3000   -0.0080
##     40        0.6616             nan     0.3000   -0.0071
##     60        0.5787             nan     0.3000   -0.0053
##     80        0.5022             nan     0.3000   -0.0040
##    100        0.4510             nan     0.3000   -0.0083
##    120        0.4080             nan     0.3000   -0.0055
##    140        0.3698             nan     0.3000   -0.0054
##    160        0.3255             nan     0.3000   -0.0039
##    180        0.2948             nan     0.3000   -0.0036
##    200        0.2608             nan     0.3000   -0.0009
##    220        0.2374             nan     0.3000   -0.0035
##    240        0.2157             nan     0.3000   -0.0017
##    260        0.1924             nan     0.3000   -0.0003
##    280        0.1742             nan     0.3000   -0.0003
##    300        0.1583             nan     0.3000   -0.0024
##    320        0.1429             nan     0.3000   -0.0011
##    340        0.1325             nan     0.3000   -0.0002
##    360        0.1204             nan     0.3000   -0.0024
##    380        0.1091             nan     0.3000   -0.0004
##    400        0.1000             nan     0.3000   -0.0008
##    420        0.0935             nan     0.3000   -0.0013
##    440        0.0861             nan     0.3000   -0.0010
##    460        0.0784             nan     0.3000   -0.0005
##    480        0.0725             nan     0.3000   -0.0011
##    500        0.0676             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1599             nan     0.3000    0.0553
##      2        1.0772             nan     0.3000    0.0392
##      3        1.0190             nan     0.3000    0.0153
##      4        0.9822             nan     0.3000    0.0072
##      5        0.9506             nan     0.3000    0.0039
##      6        0.9227             nan     0.3000    0.0080
##      7        0.9090             nan     0.3000   -0.0051
##      8        0.9051             nan     0.3000   -0.0093
##      9        0.8940             nan     0.3000   -0.0055
##     10        0.8730             nan     0.3000    0.0018
##     20        0.7776             nan     0.3000   -0.0025
##     40        0.6603             nan     0.3000   -0.0030
##     60        0.5770             nan     0.3000   -0.0074
##     80        0.5087             nan     0.3000   -0.0028
##    100        0.4543             nan     0.3000   -0.0022
##    120        0.3977             nan     0.3000   -0.0050
##    140        0.3506             nan     0.3000   -0.0051
##    160        0.3123             nan     0.3000   -0.0025
##    180        0.2813             nan     0.3000   -0.0050
##    200        0.2553             nan     0.3000   -0.0043
##    220        0.2287             nan     0.3000   -0.0022
##    240        0.2076             nan     0.3000   -0.0009
##    260        0.1863             nan     0.3000   -0.0019
##    280        0.1679             nan     0.3000   -0.0001
##    300        0.1537             nan     0.3000   -0.0007
##    320        0.1430             nan     0.3000   -0.0015
##    340        0.1318             nan     0.3000   -0.0014
##    360        0.1228             nan     0.3000   -0.0012
##    380        0.1101             nan     0.3000   -0.0004
##    400        0.1015             nan     0.3000   -0.0006
##    420        0.0955             nan     0.3000   -0.0012
##    440        0.0857             nan     0.3000   -0.0006
##    460        0.0788             nan     0.3000   -0.0011
##    480        0.0734             nan     0.3000   -0.0007
##    500        0.0669             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1685             nan     0.5000    0.0669
##      2        1.1006             nan     0.5000    0.0305
##      3        1.0405             nan     0.5000    0.0247
##      4        1.0009             nan     0.5000    0.0174
##      5        0.9833             nan     0.5000    0.0060
##      6        0.9563             nan     0.5000    0.0081
##      7        0.9418             nan     0.5000    0.0011
##      8        0.9303             nan     0.5000   -0.0003
##      9        0.9163             nan     0.5000    0.0011
##     10        0.9046             nan     0.5000   -0.0036
##     20        0.8573             nan     0.5000    0.0005
##     40        0.8314             nan     0.5000   -0.0065
##     60        1.1512             nan     0.5000   -0.0031
##     80        1.1361             nan     0.5000   -0.0082
##    100        1.1165             nan     0.5000   -0.0076
##    120        1.0943             nan     0.5000   -0.0003
##    140        1.0778             nan     0.5000   -0.0022
##    160        1.0679             nan     0.5000   -0.0075
##    180        1.0540             nan     0.5000   -0.0034
##    200        1.0527             nan     0.5000   -0.0007
##    220        1.0425             nan     0.5000    0.0000
##    240        1.0272             nan     0.5000   -0.0005
##    260        1.0185             nan     0.5000   -0.0015
##    280        1.0079             nan     0.5000    0.0004
##    300        0.9984             nan     0.5000   -0.0008
##    320        0.9975             nan     0.5000   -0.0001
##    340        0.9922             nan     0.5000   -0.0037
##    360        0.9888             nan     0.5000    0.0001
##    380        0.9807             nan     0.5000   -0.0015
##    400        0.9694             nan     0.5000   -0.0068
##    420        0.9708             nan     0.5000   -0.0026
##    440        0.9715             nan     0.5000   -0.0063
##    460        0.9652             nan     0.5000   -0.0068
##    480        0.9566             nan     0.5000   -0.0047
##    500        0.9453             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1782             nan     0.5000    0.0705
##      2        1.1086             nan     0.5000    0.0225
##      3        1.0870             nan     0.5000   -0.0057
##      4        1.0340             nan     0.5000    0.0248
##      5        1.0032             nan     0.5000    0.0131
##      6        0.9796             nan     0.5000    0.0054
##      7        0.9620             nan     0.5000   -0.0001
##      8        0.9557             nan     0.5000   -0.0040
##      9        0.9347             nan     0.5000    0.0058
##     10        0.9273             nan     0.5000    0.0023
##     20        0.8569             nan     0.5000   -0.0065
##     40        0.8144             nan     0.5000    0.0017
##     60        0.7916             nan     0.5000   -0.0060
##     80        0.7815             nan     0.5000   -0.0105
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1519             nan     0.5000    0.0594
##      2        1.0901             nan     0.5000    0.0246
##      3        1.0474             nan     0.5000    0.0213
##      4        0.9964             nan     0.5000    0.0217
##      5        0.9753             nan     0.5000    0.0053
##      6        0.9471             nan     0.5000    0.0105
##      7        0.9341             nan     0.5000   -0.0016
##      8        0.9220             nan     0.5000   -0.0002
##      9        0.9123             nan     0.5000    0.0027
##     10        0.9078             nan     0.5000   -0.0033
##     20        0.8466             nan     0.5000   -0.0021
##     40        0.8041             nan     0.5000   -0.0070
##     60        0.7924             nan     0.5000   -0.0047
##     80        0.7575             nan     0.5000   -0.0023
##    100        0.7320             nan     0.5000   -0.0029
##    120        0.7052             nan     0.5000   -0.0090
##    140        0.6923             nan     0.5000   -0.0026
##    160        0.6883             nan     0.5000   -0.0061
##    180        0.6720             nan     0.5000   -0.0059
##    200        0.6643             nan     0.5000   -0.0044
##    220        0.6560             nan     0.5000   -0.0080
##    240        0.6273             nan     0.5000   -0.0094
##    260        0.6120             nan     0.5000   -0.0044
##    280        0.6229             nan     0.5000   -0.0067
##    300        0.6130             nan     0.5000   -0.0022
##    320        0.6029             nan     0.5000   -0.0101
##    340        0.5899             nan     0.5000   -0.0063
##    360        0.5717             nan     0.5000   -0.0018
##    380        0.5679             nan     0.5000   -0.0058
##    400        0.5600             nan     0.5000   -0.0051
##    420        0.5546             nan     0.5000   -0.0015
##    440        0.5400             nan     0.5000   -0.0029
##    460        0.5270             nan     0.5000   -0.0033
##    480        0.5190             nan     0.5000   -0.0063
##    500        0.5060             nan     0.5000   -0.0046
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1164             nan     0.5000    0.0713
##      2        1.0214             nan     0.5000    0.0428
##      3        0.9733             nan     0.5000    0.0222
##      4        0.9387             nan     0.5000    0.0042
##      5        0.9184             nan     0.5000    0.0001
##      6        0.9050             nan     0.5000   -0.0050
##      7        0.8911             nan     0.5000   -0.0040
##      8        0.8718             nan     0.5000   -0.0047
##      9        0.8619             nan     0.5000    0.0020
##     10        0.8547             nan     0.5000   -0.0027
##     20        0.7708             nan     0.5000   -0.0092
##     40           inf             nan     0.5000       nan
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000   -0.0008
##    160           inf             nan     0.5000   -0.0001
##    180           inf             nan     0.5000   -0.0050
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1338             nan     0.5000    0.0733
##      2        1.0275             nan     0.5000    0.0281
##      3        0.9730             nan     0.5000    0.0213
##      4        0.9450             nan     0.5000    0.0022
##      5        0.9124             nan     0.5000    0.0066
##      6        0.9019             nan     0.5000   -0.0054
##      7        0.8797             nan     0.5000   -0.0150
##      8        0.8649             nan     0.5000    0.0043
##      9        0.8533             nan     0.5000   -0.0056
##     10        0.8371             nan     0.5000    0.0018
##     20        0.7768             nan     0.5000   -0.0069
##     40        0.6832             nan     0.5000   -0.0111
##     60        0.6154             nan     0.5000   -0.0074
##     80        0.5514             nan     0.5000   -0.0037
##    100        0.5073             nan     0.5000   -0.0068
##    120        0.4638             nan     0.5000   -0.0025
##    140        0.4134             nan     0.5000   -0.0059
##    160        0.3872             nan     0.5000   -0.0054
##    180        0.3365             nan     0.5000   -0.0080
##    200        0.3105             nan     0.5000   -0.0023
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1320             nan     0.5000    0.0793
##      2        1.0587             nan     0.5000    0.0281
##      3        1.0091             nan     0.5000    0.0185
##      4        0.9772             nan     0.5000    0.0077
##      5        0.9543             nan     0.5000   -0.0027
##      6        0.9161             nan     0.5000    0.0157
##      7        0.8988             nan     0.5000    0.0010
##      8        0.8862             nan     0.5000    0.0008
##      9        0.8750             nan     0.5000   -0.0046
##     10        0.8484             nan     0.5000    0.0024
##     20        0.7844             nan     0.5000   -0.0064
##     40        0.6894             nan     0.5000   -0.0062
##     60        0.6177             nan     0.5000   -0.0083
##     80        0.5480             nan     0.5000   -0.0048
##    100        0.5180             nan     0.5000   -0.0085
##    120        0.4527             nan     0.5000   -0.0110
##    140        0.4070             nan     0.5000   -0.0086
##    160        0.3783             nan     0.5000   -0.0038
##    180        0.3304             nan     0.5000   -0.0038
##    200        0.3095             nan     0.5000   -0.0048
##    220        0.2848             nan     0.5000   -0.0006
##    240        0.2577             nan     0.5000   -0.0050
##    260        0.2337             nan     0.5000   -0.0009
##    280        0.2157             nan     0.5000   -0.0045
##    300        0.1919             nan     0.5000   -0.0045
##    320        0.1747             nan     0.5000   -0.0016
##    340        0.1545             nan     0.5000   -0.0034
##    360        0.1436             nan     0.5000   -0.0005
##    380        0.1288             nan     0.5000   -0.0003
##    400        0.1179             nan     0.5000   -0.0019
##    420        0.1080             nan     0.5000   -0.0027
##    440        0.0976             nan     0.5000   -0.0001
##    460        0.0902             nan     0.5000   -0.0004
##    480        0.0859             nan     0.5000   -0.0005
##    500        0.0800             nan     0.5000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1081             nan     0.5000    0.0853
##      2        1.0239             nan     0.5000    0.0257
##      3        0.9520             nan     0.5000    0.0251
##      4        0.9409             nan     0.5000   -0.0087
##      5        0.9138             nan     0.5000   -0.0124
##      6        0.8848             nan     0.5000   -0.0042
##      7        0.8730             nan     0.5000   -0.0188
##      8        0.8568             nan     0.5000   -0.0094
##      9        0.8344             nan     0.5000    0.0010
##     10        0.8252             nan     0.5000   -0.0153
##     20        0.7252             nan     0.5000   -0.0183
##     40        0.5937             nan     0.5000   -0.0092
##     60        1.1033             nan     0.5000   -0.0034
##     80     1213.2023             nan     0.5000   -0.0011
##    100     1213.1596             nan     0.5000   -0.0023
##    120     1213.1129             nan     0.5000   -0.0057
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0963             nan     0.5000    0.0911
##      2        0.9914             nan     0.5000    0.0319
##      3        0.9354             nan     0.5000    0.0201
##      4        0.9167             nan     0.5000   -0.0074
##      5        0.8916             nan     0.5000   -0.0055
##      6        0.8810             nan     0.5000   -0.0087
##      7        0.8547             nan     0.5000    0.0011
##      8        0.8448             nan     0.5000   -0.0142
##      9        0.8311             nan     0.5000   -0.0084
##     10        0.8201             nan     0.5000   -0.0130
##     20        0.7064             nan     0.5000   -0.0053
##     40        0.5953             nan     0.5000   -0.0095
##     60        0.4990             nan     0.5000   -0.0071
##     80        0.4028             nan     0.5000   -0.0064
##    100        0.3441             nan     0.5000   -0.0020
##    120        0.2734             nan     0.5000    0.0002
##    140        0.2276             nan     0.5000   -0.0042
##    160        0.1961             nan     0.5000   -0.0042
##    180        0.1616             nan     0.5000   -0.0023
##    200        0.1461             nan     0.5000   -0.0021
##    220        0.1224             nan     0.5000   -0.0025
##    240        0.1040             nan     0.5000   -0.0012
##    260        0.0936             nan     0.5000   -0.0011
##    280        0.0818             nan     0.5000   -0.0007
##    300        0.0718             nan     0.5000   -0.0003
##    320        0.0640             nan     0.5000   -0.0016
##    340        0.0570             nan     0.5000   -0.0003
##    360        0.0490             nan     0.5000   -0.0008
##    380        0.0428             nan     0.5000   -0.0005
##    400        0.0379             nan     0.5000   -0.0005
##    420        0.0341             nan     0.5000   -0.0002
##    440        0.0316             nan     0.5000   -0.0002
##    460        0.0283             nan     0.5000   -0.0002
##    480        0.0259             nan     0.5000   -0.0007
##    500        0.0229             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1175             nan     0.5000    0.0708
##      2        1.0075             nan     0.5000    0.0456
##      3        0.9499             nan     0.5000    0.0238
##      4        0.9249             nan     0.5000   -0.0015
##      5        0.9051             nan     0.5000   -0.0061
##      6        0.8774             nan     0.5000    0.0010
##      7        0.8619             nan     0.5000   -0.0093
##      8        0.8372             nan     0.5000    0.0017
##      9        0.8235             nan     0.5000   -0.0103
##     10        0.8038             nan     0.5000   -0.0043
##     20        0.7243             nan     0.5000   -0.0150
##     40        0.5821             nan     0.5000   -0.0099
##     60        0.4815             nan     0.5000   -0.0117
##     80        0.4027             nan     0.5000   -0.0024
##    100        0.3356             nan     0.5000   -0.0098
##    120        0.2729             nan     0.5000   -0.0047
##    140        0.2339             nan     0.5000   -0.0024
##    160        0.1929             nan     0.5000   -0.0020
##    180        0.1646             nan     0.5000   -0.0019
##    200        0.1472             nan     0.5000   -0.0023
##    220        0.1264             nan     0.5000   -0.0018
##    240        0.1068             nan     0.5000   -0.0022
##    260        0.0944             nan     0.5000   -0.0004
##    280        0.0814             nan     0.5000   -0.0010
##    300        0.0715             nan     0.5000   -0.0011
##    320        0.0628             nan     0.5000   -0.0009
##    340        0.0543             nan     0.5000   -0.0003
##    360        0.0467             nan     0.5000   -0.0006
##    380        0.0412             nan     0.5000   -0.0010
##    400        0.0358             nan     0.5000   -0.0001
##    420        0.0312             nan     0.5000   -0.0001
##    440        0.0271             nan     0.5000   -0.0003
##    460        0.0238             nan     0.5000   -0.0002
##    480        0.0218             nan     0.5000   -0.0003
##    500        0.0194             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1316             nan     1.0000    0.0739
##      2        1.0630             nan     1.0000    0.0232
##      3        1.0307             nan     1.0000   -0.0110
##      4        1.0178             nan     1.0000   -0.0152
##      5        1.0002             nan     1.0000   -0.0061
##      6        0.9898             nan     1.0000   -0.0016
##      7        0.9749             nan     1.0000   -0.0040
##      8        0.9715             nan     1.0000   -0.0158
##      9        0.9630             nan     1.0000   -0.0071
##     10        0.9713             nan     1.0000   -0.0291
##     20        0.8712             nan     1.0000   -0.0044
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000   -0.0038
##    220           inf             nan     1.0000   -0.0148
##    240           inf             nan     1.0000   -0.0013
##    260           inf             nan     1.0000   -0.0114
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1195             nan     1.0000    0.0741
##      2        1.0637             nan     1.0000    0.0137
##      3        1.0555             nan     1.0000   -0.0182
##      4        1.0221             nan     1.0000    0.0070
##      5        0.9722             nan     1.0000    0.0008
##      6        0.9429             nan     1.0000    0.0059
##      7        0.9346             nan     1.0000   -0.0094
##      8        0.9069             nan     1.0000    0.0075
##      9        0.9002             nan     1.0000   -0.0051
##     10        0.8912             nan     1.0000   -0.0032
##     20        0.8758             nan     1.0000   -0.0190
##     40        0.9016             nan     1.0000   -0.0850
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000    0.0026
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1206             nan     1.0000    0.0785
##      2        1.0993             nan     1.0000   -0.0335
##      3        1.0241             nan     1.0000    0.0180
##      4        1.0130             nan     1.0000   -0.0061
##      5        0.9537             nan     1.0000    0.0273
##      6        0.9547             nan     1.0000   -0.0144
##      7        0.9633             nan     1.0000   -0.0386
##      8        0.9373             nan     1.0000   -0.0052
##      9        0.9249             nan     1.0000   -0.0156
##     10        0.9140             nan     1.0000   -0.0118
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0699             nan     1.0000    0.1288
##      2        0.9547             nan     1.0000    0.0499
##      3        0.9245             nan     1.0000    0.0004
##      4        0.9413             nan     1.0000   -0.0391
##      5        0.9196             nan     1.0000   -0.0118
##      6        0.9203             nan     1.0000   -0.0372
##      7        0.9024             nan     1.0000   -0.0047
##      8        0.9392             nan     1.0000   -0.0610
##      9        0.9181             nan     1.0000   -0.0221
##     10        0.9002             nan     1.0000    0.0002
##     20        0.8762             nan     1.0000   -0.0511
##     40 657504192971.6241             nan     1.0000   -0.0335
##     60 657504192971.5759             nan     1.0000   -0.0659
##     80 657504192971.4921             nan     1.0000   -0.0011
##    100 657504192971.4919             nan     1.0000   -0.0010
##    120 657504192971.4971             nan     1.0000   -0.0035
##    140 657504192971.5203             nan     1.0000    0.0011
##    160 657504192972.7058             nan     1.0000   -0.0070
##    180 657504192972.6395             nan     1.0000    0.0016
##    200 657504192972.6331             nan     1.0000    0.0000
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1109             nan     1.0000    0.0662
##      2        0.9968             nan     1.0000    0.0408
##      3        0.9737             nan     1.0000   -0.0171
##      4        0.9739             nan     1.0000   -0.0193
##      5        0.9617             nan     1.0000   -0.0130
##      6        0.9544             nan     1.0000   -0.0342
##      7        0.9162             nan     1.0000   -0.0033
##      8        0.9071             nan     1.0000   -0.0214
##      9        0.9509             nan     1.0000   -0.0731
##     10        0.9745             nan     1.0000   -0.0607
##     20        2.7832             nan     1.0000   -0.0825
##     40           inf             nan     1.0000      -inf
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0802             nan     1.0000    0.0778
##      2        1.0093             nan     1.0000    0.0155
##      3        0.9905             nan     1.0000   -0.0171
##      4        0.9506             nan     1.0000    0.0092
##      5        0.9284             nan     1.0000   -0.0047
##      6        0.9047             nan     1.0000   -0.0121
##      7        0.9060             nan     1.0000   -0.0247
##      8        0.9020             nan     1.0000   -0.0246
##      9        0.9058             nan     1.0000   -0.0368
##     10        0.9147             nan     1.0000   -0.0439
##     20        0.8679             nan     1.0000   -0.0438
##     40      248.2200             nan     1.0000    0.0030
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0430             nan     1.0000    0.1236
##      2        0.9401             nan     1.0000    0.0260
##      3        0.9286             nan     1.0000   -0.0389
##      4        0.9065             nan     1.0000   -0.0166
##      5        0.9973             nan     1.0000   -0.1371
##      6           inf             nan     1.0000   -0.0247
##      7           inf             nan     1.0000       nan
##      8           inf             nan     1.0000       nan
##      9           inf             nan     1.0000       nan
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    180 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    200 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    220 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    240 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    260 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    280 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    300 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    320 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    340 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    360 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    380 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    400 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    420 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    440 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    460 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    480 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
##    500 500419721315228714848466800400060862482086204828662480444680044682626264664882240684268068004240820424882884468002202404868888402204204682262686442628044286020226480262884228620268844068420864222888440440446442.0000             nan     1.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0018             nan     1.0000    0.1338
##      2        0.9210             nan     1.0000    0.0240
##      3        0.9158             nan     1.0000   -0.0243
##      4        0.9154             nan     1.0000   -0.0316
##      5        0.9760             nan     1.0000   -0.0927
##      6        0.9909             nan     1.0000   -0.0794
##      7        0.9679             nan     1.0000   -0.0122
##      8        0.9646             nan     1.0000   -0.0351
##      9        0.9504             nan     1.0000   -0.0269
##     10        0.9761             nan     1.0000   -0.0647
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0392             nan     1.0000    0.1204
##      2        0.9654             nan     1.0000    0.0141
##      3        0.9321             nan     1.0000   -0.0129
##      4        0.9048             nan     1.0000   -0.0114
##      5        0.9204             nan     1.0000   -0.0438
##      6        0.8950             nan     1.0000   -0.0116
##      7        0.8905             nan     1.0000   -0.0313
##      8        0.8570             nan     1.0000   -0.0287
##      9        0.8344             nan     1.0000   -0.0204
##     10        0.8398             nan     1.0000   -0.0296
##     20        0.7495             nan     1.0000   -0.0130
##     40        0.5875             nan     1.0000   -0.0500
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0002
##      3        1.2935             nan     0.0010    0.0001
##      4        1.2931             nan     0.0010    0.0002
##      5        1.2927             nan     0.0010    0.0002
##      6        1.2923             nan     0.0010    0.0002
##      7        1.2919             nan     0.0010    0.0002
##      8        1.2915             nan     0.0010    0.0002
##      9        1.2911             nan     0.0010    0.0002
##     10        1.2907             nan     0.0010    0.0002
##     20        1.2871             nan     0.0010    0.0002
##     40        1.2801             nan     0.0010    0.0002
##     60        1.2735             nan     0.0010    0.0002
##     80        1.2669             nan     0.0010    0.0001
##    100        1.2606             nan     0.0010    0.0001
##    120        1.2544             nan     0.0010    0.0001
##    140        1.2484             nan     0.0010    0.0001
##    160        1.2428             nan     0.0010    0.0001
##    180        1.2372             nan     0.0010    0.0001
##    200        1.2319             nan     0.0010    0.0001
##    220        1.2266             nan     0.0010    0.0001
##    240        1.2215             nan     0.0010    0.0001
##    260        1.2166             nan     0.0010    0.0001
##    280        1.2116             nan     0.0010    0.0001
##    300        1.2069             nan     0.0010    0.0001
##    320        1.2024             nan     0.0010    0.0001
##    340        1.1981             nan     0.0010    0.0001
##    360        1.1937             nan     0.0010    0.0001
##    380        1.1896             nan     0.0010    0.0001
##    400        1.1857             nan     0.0010    0.0001
##    420        1.1819             nan     0.0010    0.0001
##    440        1.1779             nan     0.0010    0.0001
##    460        1.1742             nan     0.0010    0.0001
##    480        1.1706             nan     0.0010    0.0001
##    500        1.1670             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0001
##      3        1.2934             nan     0.0010    0.0002
##      4        1.2930             nan     0.0010    0.0002
##      5        1.2926             nan     0.0010    0.0002
##      6        1.2922             nan     0.0010    0.0002
##      7        1.2918             nan     0.0010    0.0002
##      8        1.2915             nan     0.0010    0.0002
##      9        1.2911             nan     0.0010    0.0002
##     10        1.2907             nan     0.0010    0.0001
##     20        1.2871             nan     0.0010    0.0002
##     40        1.2798             nan     0.0010    0.0002
##     60        1.2733             nan     0.0010    0.0001
##     80        1.2667             nan     0.0010    0.0002
##    100        1.2602             nan     0.0010    0.0001
##    120        1.2540             nan     0.0010    0.0001
##    140        1.2479             nan     0.0010    0.0001
##    160        1.2421             nan     0.0010    0.0001
##    180        1.2366             nan     0.0010    0.0001
##    200        1.2312             nan     0.0010    0.0001
##    220        1.2259             nan     0.0010    0.0001
##    240        1.2209             nan     0.0010    0.0001
##    260        1.2160             nan     0.0010    0.0001
##    280        1.2113             nan     0.0010    0.0001
##    300        1.2069             nan     0.0010    0.0001
##    320        1.2024             nan     0.0010    0.0001
##    340        1.1980             nan     0.0010    0.0001
##    360        1.1938             nan     0.0010    0.0001
##    380        1.1898             nan     0.0010    0.0001
##    400        1.1857             nan     0.0010    0.0001
##    420        1.1819             nan     0.0010    0.0001
##    440        1.1779             nan     0.0010    0.0001
##    460        1.1740             nan     0.0010    0.0001
##    480        1.1703             nan     0.0010    0.0001
##    500        1.1667             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0002
##      3        1.2934             nan     0.0010    0.0002
##      4        1.2930             nan     0.0010    0.0001
##      5        1.2927             nan     0.0010    0.0002
##      6        1.2923             nan     0.0010    0.0001
##      7        1.2919             nan     0.0010    0.0002
##      8        1.2915             nan     0.0010    0.0002
##      9        1.2911             nan     0.0010    0.0002
##     10        1.2908             nan     0.0010    0.0001
##     20        1.2874             nan     0.0010    0.0002
##     40        1.2805             nan     0.0010    0.0002
##     60        1.2736             nan     0.0010    0.0001
##     80        1.2671             nan     0.0010    0.0002
##    100        1.2607             nan     0.0010    0.0002
##    120        1.2543             nan     0.0010    0.0001
##    140        1.2485             nan     0.0010    0.0001
##    160        1.2428             nan     0.0010    0.0001
##    180        1.2371             nan     0.0010    0.0001
##    200        1.2318             nan     0.0010    0.0001
##    220        1.2265             nan     0.0010    0.0001
##    240        1.2215             nan     0.0010    0.0001
##    260        1.2166             nan     0.0010    0.0001
##    280        1.2119             nan     0.0010    0.0001
##    300        1.2072             nan     0.0010    0.0001
##    320        1.2027             nan     0.0010    0.0001
##    340        1.1984             nan     0.0010    0.0001
##    360        1.1942             nan     0.0010    0.0001
##    380        1.1901             nan     0.0010    0.0001
##    400        1.1860             nan     0.0010    0.0001
##    420        1.1818             nan     0.0010    0.0001
##    440        1.1779             nan     0.0010    0.0001
##    460        1.1740             nan     0.0010    0.0001
##    480        1.1703             nan     0.0010    0.0001
##    500        1.1667             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2921             nan     0.0010    0.0002
##      6        1.2916             nan     0.0010    0.0002
##      7        1.2912             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2850             nan     0.0010    0.0002
##     40        1.2759             nan     0.0010    0.0002
##     60        1.2670             nan     0.0010    0.0002
##     80        1.2586             nan     0.0010    0.0002
##    100        1.2502             nan     0.0010    0.0002
##    120        1.2421             nan     0.0010    0.0002
##    140        1.2344             nan     0.0010    0.0002
##    160        1.2269             nan     0.0010    0.0002
##    180        1.2196             nan     0.0010    0.0002
##    200        1.2128             nan     0.0010    0.0001
##    220        1.2061             nan     0.0010    0.0001
##    240        1.1994             nan     0.0010    0.0001
##    260        1.1929             nan     0.0010    0.0001
##    280        1.1865             nan     0.0010    0.0001
##    300        1.1804             nan     0.0010    0.0001
##    320        1.1744             nan     0.0010    0.0001
##    340        1.1685             nan     0.0010    0.0001
##    360        1.1627             nan     0.0010    0.0001
##    380        1.1571             nan     0.0010    0.0001
##    400        1.1515             nan     0.0010    0.0001
##    420        1.1463             nan     0.0010    0.0001
##    440        1.1411             nan     0.0010    0.0001
##    460        1.1359             nan     0.0010    0.0001
##    480        1.1310             nan     0.0010    0.0001
##    500        1.1263             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2930             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2922             nan     0.0010    0.0002
##      6        1.2916             nan     0.0010    0.0002
##      7        1.2912             nan     0.0010    0.0002
##      8        1.2907             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2852             nan     0.0010    0.0002
##     40        1.2761             nan     0.0010    0.0002
##     60        1.2672             nan     0.0010    0.0002
##     80        1.2586             nan     0.0010    0.0002
##    100        1.2503             nan     0.0010    0.0002
##    120        1.2421             nan     0.0010    0.0001
##    140        1.2342             nan     0.0010    0.0002
##    160        1.2268             nan     0.0010    0.0001
##    180        1.2194             nan     0.0010    0.0002
##    200        1.2122             nan     0.0010    0.0002
##    220        1.2056             nan     0.0010    0.0001
##    240        1.1990             nan     0.0010    0.0001
##    260        1.1925             nan     0.0010    0.0001
##    280        1.1861             nan     0.0010    0.0001
##    300        1.1800             nan     0.0010    0.0001
##    320        1.1739             nan     0.0010    0.0001
##    340        1.1679             nan     0.0010    0.0001
##    360        1.1623             nan     0.0010    0.0001
##    380        1.1566             nan     0.0010    0.0001
##    400        1.1512             nan     0.0010    0.0001
##    420        1.1461             nan     0.0010    0.0001
##    440        1.1408             nan     0.0010    0.0001
##    460        1.1357             nan     0.0010    0.0001
##    480        1.1310             nan     0.0010    0.0001
##    500        1.1260             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2930             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2921             nan     0.0010    0.0002
##      6        1.2916             nan     0.0010    0.0002
##      7        1.2912             nan     0.0010    0.0002
##      8        1.2907             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2851             nan     0.0010    0.0002
##     40        1.2759             nan     0.0010    0.0002
##     60        1.2672             nan     0.0010    0.0002
##     80        1.2588             nan     0.0010    0.0002
##    100        1.2505             nan     0.0010    0.0002
##    120        1.2427             nan     0.0010    0.0001
##    140        1.2350             nan     0.0010    0.0002
##    160        1.2277             nan     0.0010    0.0002
##    180        1.2204             nan     0.0010    0.0002
##    200        1.2136             nan     0.0010    0.0002
##    220        1.2066             nan     0.0010    0.0001
##    240        1.2002             nan     0.0010    0.0001
##    260        1.1937             nan     0.0010    0.0002
##    280        1.1872             nan     0.0010    0.0001
##    300        1.1811             nan     0.0010    0.0001
##    320        1.1752             nan     0.0010    0.0001
##    340        1.1692             nan     0.0010    0.0001
##    360        1.1635             nan     0.0010    0.0001
##    380        1.1577             nan     0.0010    0.0001
##    400        1.1523             nan     0.0010    0.0001
##    420        1.1469             nan     0.0010    0.0001
##    440        1.1418             nan     0.0010    0.0001
##    460        1.1368             nan     0.0010    0.0001
##    480        1.1318             nan     0.0010    0.0001
##    500        1.1270             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2929             nan     0.0010    0.0002
##      4        1.2925             nan     0.0010    0.0002
##      5        1.2919             nan     0.0010    0.0003
##      6        1.2914             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0003
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2893             nan     0.0010    0.0003
##     20        1.2841             nan     0.0010    0.0002
##     40        1.2736             nan     0.0010    0.0002
##     60        1.2635             nan     0.0010    0.0002
##     80        1.2538             nan     0.0010    0.0002
##    100        1.2442             nan     0.0010    0.0002
##    120        1.2348             nan     0.0010    0.0002
##    140        1.2257             nan     0.0010    0.0002
##    160        1.2171             nan     0.0010    0.0002
##    180        1.2087             nan     0.0010    0.0002
##    200        1.2006             nan     0.0010    0.0002
##    220        1.1930             nan     0.0010    0.0001
##    240        1.1853             nan     0.0010    0.0002
##    260        1.1779             nan     0.0010    0.0002
##    280        1.1707             nan     0.0010    0.0002
##    300        1.1637             nan     0.0010    0.0002
##    320        1.1571             nan     0.0010    0.0001
##    340        1.1506             nan     0.0010    0.0001
##    360        1.1438             nan     0.0010    0.0001
##    380        1.1373             nan     0.0010    0.0001
##    400        1.1314             nan     0.0010    0.0001
##    420        1.1252             nan     0.0010    0.0002
##    440        1.1192             nan     0.0010    0.0001
##    460        1.1133             nan     0.0010    0.0001
##    480        1.1079             nan     0.0010    0.0001
##    500        1.1024             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0003
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2929             nan     0.0010    0.0003
##      4        1.2924             nan     0.0010    0.0002
##      5        1.2918             nan     0.0010    0.0002
##      6        1.2913             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2902             nan     0.0010    0.0003
##      9        1.2896             nan     0.0010    0.0003
##     10        1.2891             nan     0.0010    0.0003
##     20        1.2835             nan     0.0010    0.0003
##     40        1.2729             nan     0.0010    0.0002
##     60        1.2627             nan     0.0010    0.0002
##     80        1.2530             nan     0.0010    0.0002
##    100        1.2436             nan     0.0010    0.0002
##    120        1.2344             nan     0.0010    0.0002
##    140        1.2256             nan     0.0010    0.0002
##    160        1.2172             nan     0.0010    0.0002
##    180        1.2086             nan     0.0010    0.0002
##    200        1.2006             nan     0.0010    0.0002
##    220        1.1925             nan     0.0010    0.0002
##    240        1.1847             nan     0.0010    0.0002
##    260        1.1774             nan     0.0010    0.0002
##    280        1.1703             nan     0.0010    0.0001
##    300        1.1630             nan     0.0010    0.0002
##    320        1.1561             nan     0.0010    0.0001
##    340        1.1496             nan     0.0010    0.0002
##    360        1.1430             nan     0.0010    0.0002
##    380        1.1366             nan     0.0010    0.0001
##    400        1.1305             nan     0.0010    0.0001
##    420        1.1245             nan     0.0010    0.0001
##    440        1.1187             nan     0.0010    0.0001
##    460        1.1129             nan     0.0010    0.0001
##    480        1.1072             nan     0.0010    0.0001
##    500        1.1018             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0003
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2929             nan     0.0010    0.0002
##      4        1.2923             nan     0.0010    0.0003
##      5        1.2917             nan     0.0010    0.0003
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2901             nan     0.0010    0.0002
##      9        1.2895             nan     0.0010    0.0003
##     10        1.2889             nan     0.0010    0.0002
##     20        1.2835             nan     0.0010    0.0003
##     40        1.2732             nan     0.0010    0.0002
##     60        1.2630             nan     0.0010    0.0002
##     80        1.2533             nan     0.0010    0.0002
##    100        1.2437             nan     0.0010    0.0002
##    120        1.2348             nan     0.0010    0.0002
##    140        1.2260             nan     0.0010    0.0002
##    160        1.2173             nan     0.0010    0.0002
##    180        1.2090             nan     0.0010    0.0002
##    200        1.2008             nan     0.0010    0.0002
##    220        1.1929             nan     0.0010    0.0002
##    240        1.1853             nan     0.0010    0.0002
##    260        1.1777             nan     0.0010    0.0002
##    280        1.1708             nan     0.0010    0.0001
##    300        1.1638             nan     0.0010    0.0001
##    320        1.1569             nan     0.0010    0.0001
##    340        1.1502             nan     0.0010    0.0001
##    360        1.1437             nan     0.0010    0.0001
##    380        1.1373             nan     0.0010    0.0001
##    400        1.1311             nan     0.0010    0.0001
##    420        1.1249             nan     0.0010    0.0001
##    440        1.1190             nan     0.0010    0.0001
##    460        1.1131             nan     0.0010    0.0001
##    480        1.1074             nan     0.0010    0.0001
##    500        1.1019             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2567             nan     0.1000    0.0148
##      2        1.2296             nan     0.1000    0.0105
##      3        1.2035             nan     0.1000    0.0116
##      4        1.1839             nan     0.1000    0.0105
##      5        1.1671             nan     0.1000    0.0052
##      6        1.1491             nan     0.1000    0.0061
##      7        1.1320             nan     0.1000    0.0062
##      8        1.1171             nan     0.1000    0.0060
##      9        1.1014             nan     0.1000    0.0055
##     10        1.0897             nan     0.1000    0.0052
##     20        1.0023             nan     0.1000    0.0018
##     40        0.9192             nan     0.1000    0.0006
##     60        0.8817             nan     0.1000   -0.0005
##     80        0.8557             nan     0.1000    0.0000
##    100        0.8372             nan     0.1000    0.0001
##    120        0.8259             nan     0.1000   -0.0007
##    140        0.8143             nan     0.1000   -0.0011
##    160        0.8052             nan     0.1000   -0.0011
##    180        0.7968             nan     0.1000   -0.0009
##    200        0.7868             nan     0.1000   -0.0009
##    220        0.7788             nan     0.1000   -0.0008
##    240        0.7736             nan     0.1000   -0.0008
##    260        0.7663             nan     0.1000   -0.0006
##    280        0.7588             nan     0.1000   -0.0013
##    300        0.7522             nan     0.1000   -0.0014
##    320        0.7489             nan     0.1000   -0.0008
##    340        0.7415             nan     0.1000   -0.0016
##    360        0.7371             nan     0.1000   -0.0025
##    380        0.7313             nan     0.1000   -0.0003
##    400        0.7257             nan     0.1000   -0.0008
##    420        0.7211             nan     0.1000   -0.0008
##    440        0.7174             nan     0.1000   -0.0008
##    460        0.7130             nan     0.1000   -0.0005
##    480        0.7093             nan     0.1000   -0.0019
##    500        0.7030             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2569             nan     0.1000    0.0140
##      2        1.2282             nan     0.1000    0.0110
##      3        1.2038             nan     0.1000    0.0123
##      4        1.1800             nan     0.1000    0.0108
##      5        1.1638             nan     0.1000    0.0076
##      6        1.1490             nan     0.1000    0.0059
##      7        1.1315             nan     0.1000    0.0065
##      8        1.1209             nan     0.1000    0.0036
##      9        1.1061             nan     0.1000    0.0055
##     10        1.0927             nan     0.1000    0.0069
##     20        1.0033             nan     0.1000    0.0022
##     40        0.9220             nan     0.1000   -0.0004
##     60        0.8835             nan     0.1000   -0.0009
##     80        0.8570             nan     0.1000   -0.0004
##    100        0.8425             nan     0.1000   -0.0008
##    120        0.8264             nan     0.1000   -0.0002
##    140        0.8172             nan     0.1000   -0.0016
##    160        0.8083             nan     0.1000   -0.0010
##    180        0.8031             nan     0.1000   -0.0012
##    200        0.7940             nan     0.1000   -0.0017
##    220        0.7846             nan     0.1000   -0.0003
##    240        0.7764             nan     0.1000   -0.0012
##    260        0.7707             nan     0.1000   -0.0025
##    280        0.7633             nan     0.1000   -0.0003
##    300        0.7571             nan     0.1000   -0.0010
##    320        0.7500             nan     0.1000   -0.0007
##    340        0.7420             nan     0.1000   -0.0012
##    360        0.7372             nan     0.1000   -0.0007
##    380        0.7319             nan     0.1000   -0.0008
##    400        0.7282             nan     0.1000   -0.0007
##    420        0.7235             nan     0.1000   -0.0013
##    440        0.7203             nan     0.1000   -0.0011
##    460        0.7163             nan     0.1000   -0.0011
##    480        0.7098             nan     0.1000   -0.0010
##    500        0.7058             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2576             nan     0.1000    0.0183
##      2        1.2219             nan     0.1000    0.0144
##      3        1.2042             nan     0.1000    0.0080
##      4        1.1818             nan     0.1000    0.0108
##      5        1.1634             nan     0.1000    0.0075
##      6        1.1486             nan     0.1000    0.0070
##      7        1.1328             nan     0.1000    0.0060
##      8        1.1204             nan     0.1000    0.0061
##      9        1.1037             nan     0.1000    0.0061
##     10        1.0906             nan     0.1000    0.0064
##     20        1.0038             nan     0.1000    0.0018
##     40        0.9225             nan     0.1000   -0.0006
##     60        0.8785             nan     0.1000   -0.0001
##     80        0.8566             nan     0.1000   -0.0006
##    100        0.8380             nan     0.1000   -0.0000
##    120        0.8213             nan     0.1000   -0.0011
##    140        0.8107             nan     0.1000   -0.0007
##    160        0.8019             nan     0.1000   -0.0006
##    180        0.7913             nan     0.1000   -0.0014
##    200        0.7810             nan     0.1000   -0.0015
##    220        0.7727             nan     0.1000   -0.0014
##    240        0.7646             nan     0.1000   -0.0002
##    260        0.7570             nan     0.1000   -0.0000
##    280        0.7500             nan     0.1000   -0.0010
##    300        0.7452             nan     0.1000   -0.0011
##    320        0.7400             nan     0.1000   -0.0008
##    340        0.7351             nan     0.1000   -0.0005
##    360        0.7285             nan     0.1000   -0.0010
##    380        0.7228             nan     0.1000   -0.0007
##    400        0.7163             nan     0.1000   -0.0007
##    420        0.7133             nan     0.1000   -0.0015
##    440        0.7098             nan     0.1000   -0.0005
##    460        0.7044             nan     0.1000   -0.0011
##    480        0.7009             nan     0.1000   -0.0011
##    500        0.6966             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2453             nan     0.1000    0.0213
##      2        1.2101             nan     0.1000    0.0173
##      3        1.1750             nan     0.1000    0.0141
##      4        1.1484             nan     0.1000    0.0105
##      5        1.1252             nan     0.1000    0.0107
##      6        1.1004             nan     0.1000    0.0103
##      7        1.0799             nan     0.1000    0.0102
##      8        1.0640             nan     0.1000    0.0037
##      9        1.0457             nan     0.1000    0.0071
##     10        1.0370             nan     0.1000    0.0006
##     20        0.9392             nan     0.1000    0.0016
##     40        0.8532             nan     0.1000   -0.0002
##     60        0.8043             nan     0.1000   -0.0014
##     80        0.7706             nan     0.1000   -0.0020
##    100        0.7461             nan     0.1000   -0.0010
##    120        0.7166             nan     0.1000   -0.0005
##    140        0.6971             nan     0.1000   -0.0006
##    160        0.6778             nan     0.1000   -0.0014
##    180        0.6596             nan     0.1000   -0.0009
##    200        0.6386             nan     0.1000   -0.0005
##    220        0.6248             nan     0.1000   -0.0010
##    240        0.6079             nan     0.1000   -0.0018
##    260        0.5930             nan     0.1000   -0.0023
##    280        0.5777             nan     0.1000   -0.0019
##    300        0.5601             nan     0.1000   -0.0007
##    320        0.5455             nan     0.1000   -0.0004
##    340        0.5305             nan     0.1000   -0.0007
##    360        0.5167             nan     0.1000   -0.0007
##    380        0.5027             nan     0.1000   -0.0012
##    400        0.4884             nan     0.1000   -0.0011
##    420        0.4781             nan     0.1000   -0.0025
##    440        0.4653             nan     0.1000   -0.0009
##    460        0.4572             nan     0.1000   -0.0007
##    480        0.4491             nan     0.1000   -0.0002
##    500        0.4386             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2544             nan     0.1000    0.0195
##      2        1.2170             nan     0.1000    0.0175
##      3        1.1833             nan     0.1000    0.0136
##      4        1.1496             nan     0.1000    0.0136
##      5        1.1223             nan     0.1000    0.0118
##      6        1.0990             nan     0.1000    0.0104
##      7        1.0760             nan     0.1000    0.0094
##      8        1.0592             nan     0.1000    0.0080
##      9        1.0424             nan     0.1000    0.0064
##     10        1.0259             nan     0.1000    0.0064
##     20        0.9265             nan     0.1000    0.0015
##     40        0.8439             nan     0.1000   -0.0025
##     60        0.8023             nan     0.1000   -0.0023
##     80        0.7635             nan     0.1000    0.0004
##    100        0.7306             nan     0.1000   -0.0009
##    120        0.7088             nan     0.1000   -0.0014
##    140        0.6885             nan     0.1000   -0.0007
##    160        0.6692             nan     0.1000   -0.0002
##    180        0.6538             nan     0.1000   -0.0017
##    200        0.6366             nan     0.1000   -0.0005
##    220        0.6168             nan     0.1000   -0.0007
##    240        0.5978             nan     0.1000   -0.0010
##    260        0.5802             nan     0.1000   -0.0014
##    280        0.5674             nan     0.1000   -0.0014
##    300        0.5522             nan     0.1000   -0.0024
##    320        0.5389             nan     0.1000   -0.0012
##    340        0.5256             nan     0.1000   -0.0003
##    360        0.5102             nan     0.1000   -0.0008
##    380        0.4951             nan     0.1000   -0.0011
##    400        0.4841             nan     0.1000   -0.0018
##    420        0.4730             nan     0.1000   -0.0007
##    440        0.4641             nan     0.1000   -0.0007
##    460        0.4560             nan     0.1000   -0.0001
##    480        0.4453             nan     0.1000   -0.0006
##    500        0.4374             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2488             nan     0.1000    0.0213
##      2        1.2125             nan     0.1000    0.0161
##      3        1.1784             nan     0.1000    0.0125
##      4        1.1521             nan     0.1000    0.0128
##      5        1.1227             nan     0.1000    0.0114
##      6        1.1018             nan     0.1000    0.0089
##      7        1.0844             nan     0.1000    0.0055
##      8        1.0650             nan     0.1000    0.0072
##      9        1.0477             nan     0.1000    0.0074
##     10        1.0285             nan     0.1000    0.0068
##     20        0.9372             nan     0.1000    0.0009
##     40        0.8509             nan     0.1000   -0.0016
##     60        0.8079             nan     0.1000   -0.0011
##     80        0.7706             nan     0.1000   -0.0010
##    100        0.7422             nan     0.1000   -0.0019
##    120        0.7169             nan     0.1000   -0.0018
##    140        0.6903             nan     0.1000   -0.0005
##    160        0.6656             nan     0.1000   -0.0009
##    180        0.6496             nan     0.1000   -0.0015
##    200        0.6311             nan     0.1000   -0.0013
##    220        0.6093             nan     0.1000   -0.0006
##    240        0.5942             nan     0.1000   -0.0020
##    260        0.5781             nan     0.1000   -0.0010
##    280        0.5652             nan     0.1000   -0.0015
##    300        0.5518             nan     0.1000   -0.0009
##    320        0.5364             nan     0.1000   -0.0010
##    340        0.5227             nan     0.1000   -0.0007
##    360        0.5080             nan     0.1000   -0.0017
##    380        0.4963             nan     0.1000   -0.0007
##    400        0.4869             nan     0.1000   -0.0006
##    420        0.4792             nan     0.1000   -0.0009
##    440        0.4687             nan     0.1000   -0.0010
##    460        0.4576             nan     0.1000   -0.0010
##    480        0.4441             nan     0.1000   -0.0005
##    500        0.4332             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2408             nan     0.1000    0.0200
##      2        1.1950             nan     0.1000    0.0201
##      3        1.1563             nan     0.1000    0.0165
##      4        1.1237             nan     0.1000    0.0112
##      5        1.0964             nan     0.1000    0.0115
##      6        1.0719             nan     0.1000    0.0109
##      7        1.0510             nan     0.1000    0.0105
##      8        1.0323             nan     0.1000    0.0082
##      9        1.0147             nan     0.1000    0.0041
##     10        0.9956             nan     0.1000    0.0053
##     20        0.8902             nan     0.1000   -0.0001
##     40        0.7922             nan     0.1000    0.0003
##     60        0.7412             nan     0.1000   -0.0010
##     80        0.6933             nan     0.1000   -0.0010
##    100        0.6495             nan     0.1000   -0.0021
##    120        0.6153             nan     0.1000   -0.0007
##    140        0.5862             nan     0.1000   -0.0010
##    160        0.5566             nan     0.1000   -0.0001
##    180        0.5321             nan     0.1000   -0.0008
##    200        0.5095             nan     0.1000   -0.0005
##    220        0.4910             nan     0.1000   -0.0018
##    240        0.4705             nan     0.1000   -0.0012
##    260        0.4494             nan     0.1000   -0.0006
##    280        0.4297             nan     0.1000   -0.0004
##    300        0.4177             nan     0.1000   -0.0004
##    320        0.4019             nan     0.1000   -0.0010
##    340        0.3848             nan     0.1000   -0.0006
##    360        0.3702             nan     0.1000   -0.0009
##    380        0.3550             nan     0.1000   -0.0014
##    400        0.3430             nan     0.1000   -0.0019
##    420        0.3336             nan     0.1000   -0.0010
##    440        0.3225             nan     0.1000    0.0000
##    460        0.3128             nan     0.1000   -0.0005
##    480        0.3021             nan     0.1000   -0.0005
##    500        0.2908             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2400             nan     0.1000    0.0249
##      2        1.2022             nan     0.1000    0.0137
##      3        1.1621             nan     0.1000    0.0148
##      4        1.1245             nan     0.1000    0.0146
##      5        1.0968             nan     0.1000    0.0112
##      6        1.0749             nan     0.1000    0.0087
##      7        1.0562             nan     0.1000    0.0080
##      8        1.0357             nan     0.1000    0.0084
##      9        1.0191             nan     0.1000    0.0054
##     10        0.9964             nan     0.1000    0.0072
##     20        0.8960             nan     0.1000   -0.0001
##     40        0.8033             nan     0.1000   -0.0022
##     60        0.7462             nan     0.1000   -0.0005
##     80        0.6966             nan     0.1000   -0.0004
##    100        0.6574             nan     0.1000   -0.0011
##    120        0.6205             nan     0.1000   -0.0002
##    140        0.5862             nan     0.1000   -0.0004
##    160        0.5550             nan     0.1000   -0.0008
##    180        0.5241             nan     0.1000   -0.0008
##    200        0.5004             nan     0.1000   -0.0011
##    220        0.4798             nan     0.1000   -0.0022
##    240        0.4589             nan     0.1000   -0.0015
##    260        0.4389             nan     0.1000   -0.0005
##    280        0.4234             nan     0.1000   -0.0009
##    300        0.4046             nan     0.1000   -0.0008
##    320        0.3878             nan     0.1000   -0.0009
##    340        0.3743             nan     0.1000   -0.0008
##    360        0.3606             nan     0.1000   -0.0010
##    380        0.3453             nan     0.1000   -0.0008
##    400        0.3306             nan     0.1000   -0.0009
##    420        0.3187             nan     0.1000   -0.0010
##    440        0.3071             nan     0.1000   -0.0005
##    460        0.2943             nan     0.1000   -0.0010
##    480        0.2827             nan     0.1000   -0.0012
##    500        0.2722             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2455             nan     0.1000    0.0243
##      2        1.2056             nan     0.1000    0.0144
##      3        1.1664             nan     0.1000    0.0172
##      4        1.1351             nan     0.1000    0.0139
##      5        1.1040             nan     0.1000    0.0130
##      6        1.0787             nan     0.1000    0.0128
##      7        1.0577             nan     0.1000    0.0071
##      8        1.0342             nan     0.1000    0.0080
##      9        1.0182             nan     0.1000    0.0050
##     10        1.0040             nan     0.1000    0.0044
##     20        0.8968             nan     0.1000    0.0012
##     40        0.7932             nan     0.1000   -0.0009
##     60        0.7385             nan     0.1000   -0.0008
##     80        0.6945             nan     0.1000   -0.0013
##    100        0.6604             nan     0.1000   -0.0010
##    120        0.6297             nan     0.1000   -0.0010
##    140        0.5972             nan     0.1000   -0.0010
##    160        0.5670             nan     0.1000   -0.0014
##    180        0.5459             nan     0.1000   -0.0020
##    200        0.5144             nan     0.1000   -0.0012
##    220        0.4943             nan     0.1000   -0.0002
##    240        0.4717             nan     0.1000   -0.0003
##    260        0.4486             nan     0.1000   -0.0007
##    280        0.4320             nan     0.1000   -0.0019
##    300        0.4109             nan     0.1000   -0.0007
##    320        0.3918             nan     0.1000   -0.0011
##    340        0.3794             nan     0.1000   -0.0008
##    360        0.3643             nan     0.1000   -0.0010
##    380        0.3512             nan     0.1000   -0.0006
##    400        0.3370             nan     0.1000   -0.0014
##    420        0.3236             nan     0.1000   -0.0007
##    440        0.3117             nan     0.1000   -0.0006
##    460        0.2995             nan     0.1000    0.0001
##    480        0.2880             nan     0.1000   -0.0011
##    500        0.2751             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2268             nan     0.2000    0.0292
##      2        1.1887             nan     0.2000    0.0195
##      3        1.1450             nan     0.2000    0.0150
##      4        1.1144             nan     0.2000    0.0114
##      5        1.0929             nan     0.2000    0.0061
##      6        1.0655             nan     0.2000    0.0091
##      7        1.0520             nan     0.2000    0.0020
##      8        1.0311             nan     0.2000    0.0099
##      9        1.0145             nan     0.2000    0.0064
##     10        0.9990             nan     0.2000    0.0054
##     20        0.9212             nan     0.2000   -0.0008
##     40        0.8607             nan     0.2000   -0.0012
##     60        0.8286             nan     0.2000   -0.0020
##     80        0.8047             nan     0.2000   -0.0026
##    100        0.7896             nan     0.2000   -0.0045
##    120        0.7722             nan     0.2000   -0.0011
##    140        0.7584             nan     0.2000   -0.0023
##    160        0.7429             nan     0.2000   -0.0016
##    180        0.7302             nan     0.2000   -0.0007
##    200        0.7222             nan     0.2000   -0.0027
##    220        0.7166             nan     0.2000   -0.0035
##    240        0.7072             nan     0.2000   -0.0030
##    260        0.6928             nan     0.2000   -0.0009
##    280        0.6857             nan     0.2000   -0.0022
##    300        0.6758             nan     0.2000   -0.0016
##    320        0.6672             nan     0.2000   -0.0020
##    340        0.6600             nan     0.2000   -0.0031
##    360        0.6528             nan     0.2000   -0.0009
##    380        0.6486             nan     0.2000   -0.0009
##    400        0.6422             nan     0.2000   -0.0018
##    420        0.6397             nan     0.2000   -0.0006
##    440        0.6349             nan     0.2000   -0.0010
##    460        0.6307             nan     0.2000   -0.0011
##    480        0.6244             nan     0.2000   -0.0001
##    500        0.6182             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2250             nan     0.2000    0.0309
##      2        1.1873             nan     0.2000    0.0199
##      3        1.1461             nan     0.2000    0.0150
##      4        1.1154             nan     0.2000    0.0110
##      5        1.0874             nan     0.2000    0.0103
##      6        1.0664             nan     0.2000    0.0070
##      7        1.0521             nan     0.2000    0.0041
##      8        1.0300             nan     0.2000    0.0087
##      9        1.0140             nan     0.2000    0.0073
##     10        1.0060             nan     0.2000    0.0006
##     20        0.9148             nan     0.2000    0.0005
##     40        0.8636             nan     0.2000   -0.0027
##     60        0.8231             nan     0.2000   -0.0000
##     80        0.7998             nan     0.2000   -0.0020
##    100        0.7851             nan     0.2000   -0.0020
##    120        0.7717             nan     0.2000   -0.0036
##    140        0.7559             nan     0.2000   -0.0015
##    160        0.7479             nan     0.2000   -0.0036
##    180        0.7341             nan     0.2000   -0.0015
##    200        0.7258             nan     0.2000   -0.0025
##    220        0.7138             nan     0.2000   -0.0019
##    240        0.7036             nan     0.2000   -0.0012
##    260        0.6936             nan     0.2000   -0.0018
##    280        0.6850             nan     0.2000   -0.0025
##    300        0.6770             nan     0.2000   -0.0008
##    320        0.6703             nan     0.2000   -0.0010
##    340        0.6654             nan     0.2000   -0.0027
##    360        0.6592             nan     0.2000   -0.0008
##    380        0.6486             nan     0.2000   -0.0010
##    400        0.6444             nan     0.2000   -0.0012
##    420        0.6376             nan     0.2000   -0.0007
##    440        0.6355             nan     0.2000   -0.0024
##    460        0.6286             nan     0.2000   -0.0011
##    480        0.6224             nan     0.2000   -0.0015
##    500        0.6169             nan     0.2000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2325             nan     0.2000    0.0298
##      2        1.1902             nan     0.2000    0.0197
##      3        1.1517             nan     0.2000    0.0150
##      4        1.1192             nan     0.2000    0.0110
##      5        1.0991             nan     0.2000    0.0073
##      6        1.0755             nan     0.2000    0.0078
##      7        1.0523             nan     0.2000    0.0116
##      8        1.0348             nan     0.2000    0.0031
##      9        1.0195             nan     0.2000    0.0074
##     10        1.0016             nan     0.2000    0.0058
##     20        0.9199             nan     0.2000   -0.0016
##     40        0.8653             nan     0.2000   -0.0027
##     60        0.8387             nan     0.2000   -0.0026
##     80        0.8141             nan     0.2000   -0.0022
##    100        0.7979             nan     0.2000   -0.0014
##    120        0.7795             nan     0.2000   -0.0028
##    140        0.7684             nan     0.2000   -0.0025
##    160        0.7558             nan     0.2000   -0.0030
##    180        0.7485             nan     0.2000   -0.0022
##    200        0.7363             nan     0.2000   -0.0018
##    220        0.7232             nan     0.2000   -0.0013
##    240        0.7141             nan     0.2000   -0.0016
##    260        0.7047             nan     0.2000   -0.0029
##    280        0.6970             nan     0.2000   -0.0010
##    300        0.6919             nan     0.2000   -0.0016
##    320        0.6881             nan     0.2000   -0.0040
##    340        0.6794             nan     0.2000   -0.0018
##    360        0.6713             nan     0.2000   -0.0005
##    380        0.6664             nan     0.2000   -0.0009
##    400        0.6560             nan     0.2000   -0.0017
##    420        0.6498             nan     0.2000   -0.0016
##    440        0.6433             nan     0.2000   -0.0032
##    460        0.6380             nan     0.2000   -0.0007
##    480        0.6312             nan     0.2000   -0.0012
##    500        0.6260             nan     0.2000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2115             nan     0.2000    0.0301
##      2        1.1418             nan     0.2000    0.0277
##      3        1.0928             nan     0.2000    0.0233
##      4        1.0576             nan     0.2000    0.0135
##      5        1.0275             nan     0.2000    0.0130
##      6        1.0091             nan     0.2000    0.0067
##      7        0.9861             nan     0.2000    0.0075
##      8        0.9730             nan     0.2000    0.0034
##      9        0.9609             nan     0.2000    0.0028
##     10        0.9527             nan     0.2000   -0.0021
##     20        0.8576             nan     0.2000   -0.0034
##     40        0.7552             nan     0.2000   -0.0039
##     60        0.7074             nan     0.2000   -0.0026
##     80        0.6658             nan     0.2000   -0.0016
##    100        0.6326             nan     0.2000   -0.0006
##    120        0.6031             nan     0.2000   -0.0009
##    140        0.5798             nan     0.2000   -0.0038
##    160        0.5477             nan     0.2000   -0.0028
##    180        0.5136             nan     0.2000   -0.0016
##    200        0.4848             nan     0.2000   -0.0019
##    220        0.4584             nan     0.2000   -0.0028
##    240        0.4385             nan     0.2000   -0.0007
##    260        0.4164             nan     0.2000   -0.0014
##    280        0.3997             nan     0.2000   -0.0012
##    300        0.3790             nan     0.2000   -0.0026
##    320        0.3647             nan     0.2000   -0.0017
##    340        0.3463             nan     0.2000    0.0000
##    360        0.3308             nan     0.2000   -0.0015
##    380        0.3160             nan     0.2000   -0.0010
##    400        0.3046             nan     0.2000   -0.0020
##    420        0.2948             nan     0.2000   -0.0007
##    440        0.2827             nan     0.2000   -0.0007
##    460        0.2683             nan     0.2000   -0.0015
##    480        0.2597             nan     0.2000   -0.0004
##    500        0.2496             nan     0.2000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2092             nan     0.2000    0.0345
##      2        1.1468             nan     0.2000    0.0287
##      3        1.1004             nan     0.2000    0.0176
##      4        1.0654             nan     0.2000    0.0140
##      5        1.0381             nan     0.2000    0.0115
##      6        1.0136             nan     0.2000    0.0060
##      7        0.9814             nan     0.2000    0.0109
##      8        0.9606             nan     0.2000    0.0072
##      9        0.9466             nan     0.2000    0.0039
##     10        0.9391             nan     0.2000   -0.0003
##     20        0.8476             nan     0.2000   -0.0012
##     40        0.7753             nan     0.2000   -0.0006
##     60        0.7134             nan     0.2000   -0.0025
##     80        0.6770             nan     0.2000   -0.0013
##    100        0.6419             nan     0.2000   -0.0045
##    120        0.6102             nan     0.2000   -0.0030
##    140        0.5809             nan     0.2000   -0.0028
##    160        0.5582             nan     0.2000   -0.0017
##    180        0.5269             nan     0.2000   -0.0028
##    200        0.5019             nan     0.2000   -0.0011
##    220        0.4775             nan     0.2000   -0.0017
##    240        0.4587             nan     0.2000   -0.0017
##    260        0.4379             nan     0.2000   -0.0017
##    280        0.4174             nan     0.2000   -0.0022
##    300        0.4029             nan     0.2000   -0.0010
##    320        0.3879             nan     0.2000   -0.0017
##    340        0.3714             nan     0.2000   -0.0016
##    360        0.3529             nan     0.2000   -0.0017
##    380        0.3399             nan     0.2000   -0.0008
##    400        0.3255             nan     0.2000   -0.0009
##    420        0.3152             nan     0.2000   -0.0017
##    440        0.3061             nan     0.2000   -0.0010
##    460        0.2983             nan     0.2000   -0.0021
##    480        0.2866             nan     0.2000   -0.0016
##    500        0.2754             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2144             nan     0.2000    0.0365
##      2        1.1580             nan     0.2000    0.0250
##      3        1.1135             nan     0.2000    0.0127
##      4        1.0757             nan     0.2000    0.0163
##      5        1.0407             nan     0.2000    0.0138
##      6        1.0117             nan     0.2000    0.0105
##      7        0.9923             nan     0.2000    0.0062
##      8        0.9702             nan     0.2000    0.0070
##      9        0.9564             nan     0.2000    0.0047
##     10        0.9472             nan     0.2000   -0.0004
##     20        0.8591             nan     0.2000   -0.0013
##     40        0.8011             nan     0.2000   -0.0034
##     60        0.7571             nan     0.2000   -0.0061
##     80        0.7070             nan     0.2000   -0.0057
##    100        0.6597             nan     0.2000   -0.0029
##    120        0.6231             nan     0.2000   -0.0002
##    140        0.5962             nan     0.2000   -0.0038
##    160        0.5599             nan     0.2000   -0.0008
##    180        0.5331             nan     0.2000   -0.0023
##    200        0.5113             nan     0.2000   -0.0020
##    220        0.4969             nan     0.2000   -0.0036
##    240        0.4759             nan     0.2000   -0.0016
##    260        0.4524             nan     0.2000   -0.0020
##    280        0.4372             nan     0.2000   -0.0022
##    300        0.4149             nan     0.2000   -0.0014
##    320        0.3994             nan     0.2000   -0.0011
##    340        0.3860             nan     0.2000   -0.0037
##    360        0.3754             nan     0.2000   -0.0015
##    380        0.3597             nan     0.2000   -0.0025
##    400        0.3466             nan     0.2000   -0.0019
##    420        0.3311             nan     0.2000   -0.0010
##    440        0.3186             nan     0.2000   -0.0017
##    460        0.3061             nan     0.2000   -0.0009
##    480        0.2910             nan     0.2000   -0.0006
##    500        0.2804             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1971             nan     0.2000    0.0402
##      2        1.1222             nan     0.2000    0.0313
##      3        1.0753             nan     0.2000    0.0203
##      4        1.0324             nan     0.2000    0.0188
##      5        0.9983             nan     0.2000    0.0079
##      6        0.9662             nan     0.2000    0.0096
##      7        0.9426             nan     0.2000    0.0074
##      8        0.9244             nan     0.2000    0.0025
##      9        0.9066             nan     0.2000    0.0032
##     10        0.8924             nan     0.2000    0.0013
##     20        0.8138             nan     0.2000   -0.0025
##     40        0.7203             nan     0.2000   -0.0049
##     60        0.6482             nan     0.2000   -0.0049
##     80        0.5782             nan     0.2000   -0.0017
##    100        0.5305             nan     0.2000   -0.0030
##    120        0.4884             nan     0.2000   -0.0027
##    140        0.4574             nan     0.2000   -0.0131
##    160        0.4033             nan     0.2000   -0.0014
##    180        0.3723             nan     0.2000   -0.0022
##    200        0.3452             nan     0.2000   -0.0021
##    220        0.3131             nan     0.2000   -0.0009
##    240        0.2888             nan     0.2000   -0.0004
##    260        0.2690             nan     0.2000   -0.0012
##    280        0.2532             nan     0.2000   -0.0007
##    300        0.2347             nan     0.2000   -0.0017
##    320        0.2164             nan     0.2000   -0.0001
##    340        0.2031             nan     0.2000   -0.0015
##    360        0.1915             nan     0.2000   -0.0013
##    380        0.1817             nan     0.2000   -0.0007
##    400        0.1705             nan     0.2000   -0.0004
##    420        0.1599             nan     0.2000   -0.0005
##    440        0.1502             nan     0.2000   -0.0006
##    460        0.1405             nan     0.2000   -0.0003
##    480        0.1323             nan     0.2000   -0.0009
##    500        0.1251             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2111             nan     0.2000    0.0366
##      2        1.1384             nan     0.2000    0.0333
##      3        1.0835             nan     0.2000    0.0200
##      4        1.0390             nan     0.2000    0.0161
##      5        1.0025             nan     0.2000    0.0117
##      6        0.9668             nan     0.2000    0.0138
##      7        0.9451             nan     0.2000    0.0081
##      8        0.9249             nan     0.2000    0.0038
##      9        0.9050             nan     0.2000    0.0047
##     10        0.8899             nan     0.2000    0.0014
##     20        0.8020             nan     0.2000   -0.0017
##     40        0.6965             nan     0.2000   -0.0012
##     60        0.6327             nan     0.2000   -0.0038
##     80        0.5763             nan     0.2000   -0.0014
##    100        0.5161             nan     0.2000   -0.0022
##    120        0.4693             nan     0.2000   -0.0002
##    140        0.4274             nan     0.2000   -0.0013
##    160        0.3954             nan     0.2000   -0.0022
##    180        0.3680             nan     0.2000   -0.0006
##    200        0.3382             nan     0.2000   -0.0013
##    220        0.3098             nan     0.2000    0.0001
##    240        0.2861             nan     0.2000   -0.0016
##    260        0.2710             nan     0.2000   -0.0013
##    280        0.2498             nan     0.2000   -0.0014
##    300        0.2293             nan     0.2000   -0.0017
##    320        0.2125             nan     0.2000   -0.0014
##    340        0.1976             nan     0.2000   -0.0015
##    360        0.1830             nan     0.2000   -0.0007
##    380        0.1705             nan     0.2000   -0.0011
##    400        0.1574             nan     0.2000   -0.0007
##    420        0.1472             nan     0.2000   -0.0014
##    440        0.1383             nan     0.2000   -0.0005
##    460        0.1275             nan     0.2000   -0.0002
##    480        0.1210             nan     0.2000   -0.0005
##    500        0.1141             nan     0.2000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2042             nan     0.2000    0.0436
##      2        1.1328             nan     0.2000    0.0323
##      3        1.0745             nan     0.2000    0.0234
##      4        1.0329             nan     0.2000    0.0212
##      5        0.9929             nan     0.2000    0.0127
##      6        0.9632             nan     0.2000    0.0099
##      7        0.9373             nan     0.2000    0.0092
##      8        0.9135             nan     0.2000    0.0085
##      9        0.8963             nan     0.2000    0.0021
##     10        0.8829             nan     0.2000    0.0020
##     20        0.7956             nan     0.2000    0.0001
##     40        0.7064             nan     0.2000   -0.0024
##     60        0.6341             nan     0.2000    0.0001
##     80        0.5752             nan     0.2000   -0.0025
##    100        0.5193             nan     0.2000   -0.0010
##    120        0.4742             nan     0.2000   -0.0013
##    140        0.4360             nan     0.2000   -0.0034
##    160        0.4006             nan     0.2000   -0.0017
##    180        0.3692             nan     0.2000   -0.0013
##    200        0.3494             nan     0.2000   -0.0038
##    220        0.3189             nan     0.2000   -0.0012
##    240        0.2943             nan     0.2000   -0.0016
##    260        0.2750             nan     0.2000   -0.0029
##    280        0.2534             nan     0.2000   -0.0017
##    300        0.2339             nan     0.2000   -0.0012
##    320        0.2191             nan     0.2000   -0.0009
##    340        0.2033             nan     0.2000   -0.0017
##    360        0.1887             nan     0.2000   -0.0012
##    380        0.1756             nan     0.2000   -0.0007
##    400        0.1646             nan     0.2000   -0.0006
##    420        0.1548             nan     0.2000   -0.0005
##    440        0.1448             nan     0.2000   -0.0009
##    460        0.1354             nan     0.2000   -0.0005
##    480        0.1278             nan     0.2000   -0.0005
##    500        0.1220             nan     0.2000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2031             nan     0.3000    0.0360
##      2        1.1533             nan     0.3000    0.0244
##      3        1.1077             nan     0.3000    0.0202
##      4        1.0786             nan     0.3000    0.0092
##      5        1.0487             nan     0.3000    0.0089
##      6        1.0130             nan     0.3000    0.0126
##      7        1.0023             nan     0.3000    0.0009
##      8        0.9824             nan     0.3000    0.0060
##      9        0.9585             nan     0.3000    0.0071
##     10        0.9493             nan     0.3000   -0.0004
##     20        0.8863             nan     0.3000   -0.0016
##     40        0.8268             nan     0.3000   -0.0013
##     60        0.7955             nan     0.3000   -0.0033
##     80        0.7788             nan     0.3000   -0.0046
##    100        0.7603             nan     0.3000   -0.0013
##    120        0.7504             nan     0.3000   -0.0025
##    140        0.7327             nan     0.3000   -0.0043
##    160        0.7190             nan     0.3000   -0.0027
##    180        0.7054             nan     0.3000   -0.0019
##    200        0.6904             nan     0.3000   -0.0008
##    220        0.6774             nan     0.3000   -0.0020
##    240        0.6670             nan     0.3000   -0.0028
##    260        0.6559             nan     0.3000   -0.0029
##    280        0.6439             nan     0.3000   -0.0027
##    300        0.6335             nan     0.3000   -0.0018
##    320        0.6295             nan     0.3000   -0.0014
##    340        0.6197             nan     0.3000   -0.0031
##    360        0.6115             nan     0.3000   -0.0019
##    380        0.6041             nan     0.3000   -0.0045
##    400        0.5990             nan     0.3000   -0.0012
##    420        0.5914             nan     0.3000   -0.0019
##    440        0.5893             nan     0.3000   -0.0013
##    460        0.5835             nan     0.3000   -0.0016
##    480        0.5788             nan     0.3000   -0.0032
##    500        0.5712             nan     0.3000   -0.0042
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1957             nan     0.3000    0.0455
##      2        1.1451             nan     0.3000    0.0183
##      3        1.1053             nan     0.3000    0.0179
##      4        1.0699             nan     0.3000    0.0162
##      5        1.0408             nan     0.3000    0.0107
##      6        1.0187             nan     0.3000    0.0068
##      7        0.9984             nan     0.3000    0.0081
##      8        0.9812             nan     0.3000    0.0066
##      9        0.9681             nan     0.3000    0.0000
##     10        0.9527             nan     0.3000    0.0032
##     20        0.8972             nan     0.3000   -0.0009
##     40        0.8314             nan     0.3000   -0.0009
##     60        0.8013             nan     0.3000   -0.0040
##     80        0.7778             nan     0.3000   -0.0040
##    100        0.7613             nan     0.3000   -0.0026
##    120        0.7504             nan     0.3000   -0.0032
##    140        0.7315             nan     0.3000   -0.0010
##    160        0.7210             nan     0.3000   -0.0018
##    180        0.7159             nan     0.3000   -0.0084
##    200        0.7036             nan     0.3000   -0.0021
##    220        0.6978             nan     0.3000   -0.0023
##    240        0.6845             nan     0.3000   -0.0052
##    260        0.6751             nan     0.3000   -0.0062
##    280        0.6612             nan     0.3000   -0.0018
##    300        0.6533             nan     0.3000   -0.0032
##    320        0.6403             nan     0.3000   -0.0030
##    340        0.6330             nan     0.3000   -0.0023
##    360        0.6200             nan     0.3000   -0.0007
##    380        0.6161             nan     0.3000   -0.0015
##    400        0.6125             nan     0.3000   -0.0025
##    420        0.6020             nan     0.3000   -0.0036
##    440        0.5940             nan     0.3000   -0.0016
##    460        0.5801             nan     0.3000   -0.0018
##    480        0.5711             nan     0.3000   -0.0025
##    500        0.5695             nan     0.3000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1966             nan     0.3000    0.0419
##      2        1.1388             nan     0.3000    0.0282
##      3        1.1034             nan     0.3000    0.0102
##      4        1.0645             nan     0.3000    0.0141
##      5        1.0261             nan     0.3000    0.0137
##      6        0.9993             nan     0.3000    0.0086
##      7        0.9788             nan     0.3000    0.0025
##      8        0.9614             nan     0.3000    0.0068
##      9        0.9498             nan     0.3000    0.0019
##     10        0.9404             nan     0.3000    0.0005
##     20        0.8858             nan     0.3000   -0.0013
##     40        0.8277             nan     0.3000   -0.0017
##     60        0.8032             nan     0.3000   -0.0014
##     80        0.7803             nan     0.3000   -0.0027
##    100        0.7693             nan     0.3000   -0.0031
##    120        0.7489             nan     0.3000   -0.0021
##    140        0.7302             nan     0.3000    0.0000
##    160        0.7222             nan     0.3000   -0.0056
##    180        0.7061             nan     0.3000   -0.0043
##    200        0.6886             nan     0.3000   -0.0011
##    220        0.6822             nan     0.3000   -0.0008
##    240        0.6789             nan     0.3000   -0.0025
##    260        0.6689             nan     0.3000   -0.0023
##    280        0.6639             nan     0.3000   -0.0042
##    300        0.6445             nan     0.3000   -0.0017
##    320        0.6414             nan     0.3000   -0.0088
##    340        0.6298             nan     0.3000   -0.0015
##    360        0.6210             nan     0.3000   -0.0033
##    380        0.6144             nan     0.3000   -0.0016
##    400        0.6064             nan     0.3000   -0.0035
##    420        0.5961             nan     0.3000   -0.0013
##    440        0.5878             nan     0.3000   -0.0016
##    460        0.5807             nan     0.3000   -0.0024
##    480        0.5749             nan     0.3000   -0.0030
##    500        0.5706             nan     0.3000   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1728             nan     0.3000    0.0530
##      2        1.0990             nan     0.3000    0.0275
##      3        1.0480             nan     0.3000    0.0192
##      4        1.0044             nan     0.3000    0.0160
##      5        0.9700             nan     0.3000    0.0122
##      6        0.9435             nan     0.3000    0.0106
##      7        0.9304             nan     0.3000    0.0020
##      8        0.9195             nan     0.3000   -0.0031
##      9        0.9061             nan     0.3000    0.0015
##     10        0.8937             nan     0.3000   -0.0006
##     20        0.8362             nan     0.3000   -0.0048
##     40        0.7392             nan     0.3000   -0.0031
##     60        0.6675             nan     0.3000   -0.0032
##     80        0.6075             nan     0.3000   -0.0024
##    100        0.5483             nan     0.3000   -0.0012
##    120        0.5084             nan     0.3000   -0.0039
##    140        0.4749             nan     0.3000   -0.0024
##    160        0.4421             nan     0.3000   -0.0027
##    180        0.4213             nan     0.3000   -0.0016
##    200        0.3962             nan     0.3000   -0.0045
##    220        0.3697             nan     0.3000   -0.0007
##    240        0.3487             nan     0.3000   -0.0024
##    260        0.3255             nan     0.3000   -0.0027
##    280        0.3092             nan     0.3000   -0.0031
##    300        0.2870             nan     0.3000   -0.0002
##    320        0.2679             nan     0.3000   -0.0026
##    340        0.2503             nan     0.3000   -0.0012
##    360        0.2322             nan     0.3000   -0.0008
##    380        0.2160             nan     0.3000   -0.0016
##    400        0.2080             nan     0.3000   -0.0004
##    420        0.1961             nan     0.3000   -0.0006
##    440        0.1866             nan     0.3000   -0.0016
##    460        0.1790             nan     0.3000   -0.0019
##    480        0.1694             nan     0.3000    0.0002
##    500        0.1624             nan     0.3000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1792             nan     0.3000    0.0576
##      2        1.0946             nan     0.3000    0.0360
##      3        1.0454             nan     0.3000    0.0176
##      4        1.0080             nan     0.3000    0.0123
##      5        0.9743             nan     0.3000    0.0066
##      6        0.9485             nan     0.3000    0.0075
##      7        0.9286             nan     0.3000    0.0073
##      8        0.9139             nan     0.3000   -0.0001
##      9        0.8961             nan     0.3000    0.0027
##     10        0.8881             nan     0.3000   -0.0046
##     20        0.8128             nan     0.3000   -0.0028
##     40        0.7345             nan     0.3000   -0.0032
##     60        0.6617             nan     0.3000   -0.0014
##     80        0.6245             nan     0.3000   -0.0029
##    100        0.5834             nan     0.3000   -0.0031
##    120        0.5350             nan     0.3000   -0.0029
##    140        0.5005             nan     0.3000   -0.0031
##    160        0.4675             nan     0.3000   -0.0020
##    180        0.4436             nan     0.3000   -0.0057
##    200        0.4118             nan     0.3000   -0.0008
##    220        0.3878             nan     0.3000   -0.0008
##    240        0.3647             nan     0.3000   -0.0038
##    260        0.3413             nan     0.3000   -0.0012
##    280        0.3217             nan     0.3000   -0.0020
##    300        0.3049             nan     0.3000   -0.0026
##    320        0.2811             nan     0.3000   -0.0024
##    340        0.2674             nan     0.3000   -0.0016
##    360        0.2560             nan     0.3000   -0.0014
##    380        0.2395             nan     0.3000   -0.0017
##    400        0.2265             nan     0.3000   -0.0015
##    420        0.2137             nan     0.3000   -0.0004
##    440        0.2019             nan     0.3000   -0.0008
##    460        0.1942             nan     0.3000   -0.0003
##    480        0.1803             nan     0.3000   -0.0015
##    500        0.1727             nan     0.3000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1613             nan     0.3000    0.0512
##      2        1.0905             nan     0.3000    0.0202
##      3        1.0293             nan     0.3000    0.0202
##      4        0.9827             nan     0.3000    0.0204
##      5        0.9608             nan     0.3000    0.0049
##      6        0.9395             nan     0.3000    0.0023
##      7        0.9229             nan     0.3000    0.0037
##      8        0.9121             nan     0.3000   -0.0034
##      9        0.9029             nan     0.3000   -0.0015
##     10        0.8882             nan     0.3000    0.0053
##     20        0.8221             nan     0.3000   -0.0003
##     40        0.7462             nan     0.3000   -0.0049
##     60        0.6758             nan     0.3000   -0.0032
##     80        0.6186             nan     0.3000   -0.0018
##    100        0.5705             nan     0.3000   -0.0044
##    120        0.5314             nan     0.3000   -0.0001
##    140        0.5012             nan     0.3000   -0.0024
##    160        0.4748             nan     0.3000   -0.0044
##    180        0.4479             nan     0.3000   -0.0030
##    200        0.4213             nan     0.3000   -0.0008
##    220        0.3965             nan     0.3000   -0.0008
##    240        0.3765             nan     0.3000   -0.0024
##    260        0.3519             nan     0.3000   -0.0028
##    280        0.3299             nan     0.3000   -0.0056
##    300        0.3123             nan     0.3000   -0.0023
##    320        0.2970             nan     0.3000   -0.0024
##    340        0.2821             nan     0.3000   -0.0031
##    360        0.2750             nan     0.3000   -0.0011
##    380        0.2570             nan     0.3000   -0.0010
##    400        0.2423             nan     0.3000   -0.0033
##    420        0.2285             nan     0.3000   -0.0012
##    440        0.2123             nan     0.3000   -0.0026
##    460        0.2026             nan     0.3000   -0.0014
##    480        0.1921             nan     0.3000   -0.0014
##    500        0.1842             nan     0.3000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1604             nan     0.3000    0.0617
##      2        1.0731             nan     0.3000    0.0284
##      3        1.0231             nan     0.3000    0.0154
##      4        0.9839             nan     0.3000    0.0091
##      5        0.9446             nan     0.3000    0.0103
##      6        0.9203             nan     0.3000    0.0005
##      7        0.8958             nan     0.3000    0.0043
##      8        0.8781             nan     0.3000   -0.0010
##      9        0.8653             nan     0.3000   -0.0028
##     10        0.8427             nan     0.3000   -0.0003
##     20        0.7659             nan     0.3000   -0.0056
##     40        0.6436             nan     0.3000   -0.0074
##     60        0.5529             nan     0.3000   -0.0064
##     80        0.4838             nan     0.3000   -0.0048
##    100        0.4305             nan     0.3000   -0.0023
##    120        0.3726             nan     0.3000   -0.0016
##    140        0.3264             nan     0.3000   -0.0047
##    160        0.2834             nan     0.3000   -0.0015
##    180        0.2517             nan     0.3000   -0.0026
##    200        0.2281             nan     0.3000   -0.0020
##    220        0.2074             nan     0.3000   -0.0007
##    240        0.1888             nan     0.3000   -0.0022
##    260        0.1727             nan     0.3000   -0.0022
##    280        0.1568             nan     0.3000   -0.0005
##    300        0.1439             nan     0.3000   -0.0010
##    320        0.1341             nan     0.3000   -0.0006
##    340        0.1230             nan     0.3000   -0.0007
##    360        0.1110             nan     0.3000   -0.0015
##    380        0.1017             nan     0.3000   -0.0011
##    400        0.0943             nan     0.3000   -0.0003
##    420        0.0862             nan     0.3000   -0.0009
##    440        0.0799             nan     0.3000   -0.0003
##    460        0.0740             nan     0.3000   -0.0003
##    480        0.0705             nan     0.3000   -0.0008
##    500        0.0659             nan     0.3000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1597             nan     0.3000    0.0612
##      2        1.0729             nan     0.3000    0.0388
##      3        1.0107             nan     0.3000    0.0261
##      4        0.9777             nan     0.3000    0.0047
##      5        0.9394             nan     0.3000    0.0178
##      6        0.9174             nan     0.3000   -0.0005
##      7        0.8932             nan     0.3000    0.0040
##      8        0.8772             nan     0.3000    0.0002
##      9        0.8615             nan     0.3000    0.0009
##     10        0.8445             nan     0.3000    0.0011
##     20        0.7455             nan     0.3000   -0.0053
##     40        0.6286             nan     0.3000   -0.0015
##     60        0.5428             nan     0.3000   -0.0042
##     80        0.4650             nan     0.3000   -0.0010
##    100        0.4072             nan     0.3000   -0.0020
##    120        0.3835             nan     0.3000   -0.0037
##    140        0.3378             nan     0.3000   -0.0030
##    160        0.3070             nan     0.3000   -0.0004
##    180        0.2663             nan     0.3000   -0.0029
##    200        0.2363             nan     0.3000   -0.0023
##    220        0.2105             nan     0.3000   -0.0004
##    240        0.1896             nan     0.3000   -0.0017
##    260        0.1737             nan     0.3000   -0.0018
##    280        0.1560             nan     0.3000   -0.0019
##    300        0.1429             nan     0.3000   -0.0011
##    320        0.1305             nan     0.3000   -0.0011
##    340        0.1197             nan     0.3000   -0.0014
##    360        0.1101             nan     0.3000   -0.0013
##    380        0.0970             nan     0.3000   -0.0010
##    400        0.0876             nan     0.3000   -0.0006
##    420        0.0800             nan     0.3000   -0.0005
##    440        0.0747             nan     0.3000   -0.0007
##    460        0.0684             nan     0.3000   -0.0004
##    480        0.0638             nan     0.3000   -0.0005
##    500        0.0588             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1584             nan     0.3000    0.0493
##      2        1.0573             nan     0.3000    0.0384
##      3        1.0081             nan     0.3000    0.0184
##      4        0.9572             nan     0.3000    0.0118
##      5        0.9252             nan     0.3000    0.0103
##      6        0.9037             nan     0.3000    0.0019
##      7        0.8798             nan     0.3000    0.0048
##      8        0.8582             nan     0.3000    0.0029
##      9        0.8414             nan     0.3000    0.0041
##     10        0.8269             nan     0.3000    0.0006
##     20        0.7465             nan     0.3000    0.0002
##     40        0.6384             nan     0.3000   -0.0027
##     60        0.5559             nan     0.3000   -0.0017
##     80        0.4944             nan     0.3000   -0.0027
##    100        0.4173             nan     0.3000   -0.0053
##    120        0.3729             nan     0.3000   -0.0027
##    140        0.3326             nan     0.3000   -0.0019
##    160        0.2868             nan     0.3000   -0.0014
##    180        0.2571             nan     0.3000   -0.0011
##    200        0.2304             nan     0.3000   -0.0013
##    220        0.2084             nan     0.3000   -0.0017
##    240        0.1884             nan     0.3000   -0.0000
##    260        0.1664             nan     0.3000   -0.0015
##    280        0.1525             nan     0.3000   -0.0015
##    300        0.1363             nan     0.3000   -0.0010
##    320        0.1243             nan     0.3000   -0.0017
##    340        0.1139             nan     0.3000   -0.0009
##    360        0.1047             nan     0.3000   -0.0006
##    380        0.0954             nan     0.3000   -0.0015
##    400        0.0881             nan     0.3000   -0.0007
##    420        0.0816             nan     0.3000   -0.0006
##    440        0.0756             nan     0.3000   -0.0004
##    460        0.0701             nan     0.3000   -0.0011
##    480        0.0635             nan     0.3000   -0.0002
##    500        0.0594             nan     0.3000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1405             nan     0.5000    0.0276
##      2        1.0905             nan     0.5000    0.0232
##      3        1.0697             nan     0.5000   -0.0068
##      4        1.0277             nan     0.5000    0.0151
##      5        1.0050             nan     0.5000    0.0061
##      6        0.9807             nan     0.5000    0.0049
##      7        0.9683             nan     0.5000   -0.0078
##      8        0.9551             nan     0.5000    0.0038
##      9        0.9519             nan     0.5000   -0.0035
##     10        0.9428             nan     0.5000   -0.0041
##     20        0.8720             nan     0.5000   -0.0042
##     40        0.8208             nan     0.5000   -0.0041
##     60        0.7806             nan     0.5000   -0.0018
##     80        0.8032             nan     0.5000   -0.0027
##    100        0.7139             nan     0.5000    0.0003
##    120        0.6843             nan     0.5000   -0.0064
##    140        0.6699             nan     0.5000   -0.0049
##    160        0.6546             nan     0.5000   -0.0002
##    180        0.6341             nan     0.5000   -0.0065
##    200        0.6292             nan     0.5000   -0.0053
##    220        0.6159             nan     0.5000   -0.0030
##    240        0.6007             nan     0.5000   -0.0040
##    260        0.5791             nan     0.5000   -0.0038
##    280        0.5666             nan     0.5000   -0.0025
##    300        0.5502             nan     0.5000   -0.0022
##    320        0.5415             nan     0.5000   -0.0057
##    340        0.5395             nan     0.5000   -0.0031
##    360        0.5228             nan     0.5000   -0.0046
##    380        0.5100             nan     0.5000   -0.0011
##    400        0.5099             nan     0.5000   -0.0047
##    420        0.5015             nan     0.5000   -0.0045
##    440        0.4940             nan     0.5000   -0.0038
##    460        0.4790             nan     0.5000   -0.0025
##    480        0.4733             nan     0.5000   -0.0027
##    500        0.4734             nan     0.5000   -0.0045
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1533             nan     0.5000    0.0539
##      2        1.0963             nan     0.5000    0.0226
##      3        1.0528             nan     0.5000    0.0202
##      4        1.0090             nan     0.5000    0.0185
##      5        0.9797             nan     0.5000   -0.0010
##      6        0.9660             nan     0.5000   -0.0006
##      7        0.9461             nan     0.5000    0.0036
##      8        0.9339             nan     0.5000   -0.0032
##      9        0.9291             nan     0.5000   -0.0041
##     10        0.9153             nan     0.5000   -0.0019
##     20        0.8869             nan     0.5000   -0.0086
##     40        0.8206             nan     0.5000   -0.0086
##     60        0.7918             nan     0.5000   -0.0019
##     80        0.7679             nan     0.5000   -0.0042
##    100        0.7400             nan     0.5000   -0.0018
##    120        0.7156             nan     0.5000    0.0012
##    140        0.6972             nan     0.5000   -0.0043
##    160        0.6866             nan     0.5000   -0.0055
##    180        0.6664             nan     0.5000   -0.0027
##    200        0.6487             nan     0.5000   -0.0068
##    220        0.6315             nan     0.5000   -0.0079
##    240        0.6089             nan     0.5000   -0.0065
##    260        0.5997             nan     0.5000    0.0010
##    280        0.5862             nan     0.5000   -0.0023
##    300        0.5690             nan     0.5000   -0.0035
##    320        0.5641             nan     0.5000   -0.0031
##    340        0.5566             nan     0.5000   -0.0098
##    360        0.5477             nan     0.5000   -0.0039
##    380        0.5380             nan     0.5000   -0.0025
##    400        0.5279             nan     0.5000   -0.0060
##    420        0.5170             nan     0.5000   -0.0022
##    440        0.5062             nan     0.5000   -0.0052
##    460        0.5038             nan     0.5000   -0.0033
##    480        0.4932             nan     0.5000   -0.0082
##    500        0.4834             nan     0.5000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1500             nan     0.5000    0.0630
##      2        1.0874             nan     0.5000    0.0231
##      3        1.0350             nan     0.5000    0.0200
##      4        1.0020             nan     0.5000   -0.0005
##      5        0.9643             nan     0.5000    0.0178
##      6        0.9608             nan     0.5000   -0.0107
##      7        0.9432             nan     0.5000   -0.0019
##      8        0.9355             nan     0.5000    0.0018
##      9        0.9374             nan     0.5000   -0.0141
##     10        0.9233             nan     0.5000    0.0006
##     20        0.8553             nan     0.5000   -0.0030
##     40        0.7997             nan     0.5000   -0.0063
##     60        0.7811             nan     0.5000   -0.0018
##     80        0.7557             nan     0.5000   -0.0082
##    100        0.7330             nan     0.5000   -0.0045
##    120        0.7275             nan     0.5000   -0.0070
##    140        0.7097             nan     0.5000   -0.0049
##    160        0.6841             nan     0.5000   -0.0015
##    180        0.6542             nan     0.5000   -0.0013
##    200        0.6323             nan     0.5000    0.0001
##    220        0.6256             nan     0.5000   -0.0072
##    240        0.6139             nan     0.5000   -0.0023
##    260        0.6074             nan     0.5000   -0.0055
##    280        0.5948             nan     0.5000   -0.0063
##    300        0.5857             nan     0.5000   -0.0022
##    320        0.5831             nan     0.5000   -0.0032
##    340        0.5713             nan     0.5000   -0.0065
##    360        0.5592             nan     0.5000   -0.0061
##    380        0.5517             nan     0.5000   -0.0027
##    400        0.5432             nan     0.5000   -0.0029
##    420        0.5355             nan     0.5000   -0.0053
##    440        0.5279             nan     0.5000   -0.0061
##    460        0.5188             nan     0.5000   -0.0024
##    480        0.5038             nan     0.5000   -0.0073
##    500        0.4967             nan     0.5000   -0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1249             nan     0.5000    0.0888
##      2        1.0366             nan     0.5000    0.0356
##      3        0.9711             nan     0.5000    0.0148
##      4        0.9384             nan     0.5000    0.0050
##      5        0.9227             nan     0.5000   -0.0094
##      6        0.9104             nan     0.5000   -0.0011
##      7        0.8936             nan     0.5000    0.0024
##      8        0.8817             nan     0.5000   -0.0061
##      9        0.8691             nan     0.5000    0.0014
##     10        0.8568             nan     0.5000   -0.0048
##     20        0.7890             nan     0.5000   -0.0019
##     40        0.6709             nan     0.5000   -0.0069
##     60        0.5792             nan     0.5000   -0.0042
##     80        0.5127             nan     0.5000   -0.0074
##    100        0.4569             nan     0.5000   -0.0047
##    120        0.4177             nan     0.5000    0.0003
##    140        0.3777             nan     0.5000   -0.0056
##    160        0.3391             nan     0.5000   -0.0073
##    180        0.9009             nan     0.5000   -0.0049
##    200        0.8725             nan     0.5000   -0.0015
##    220        0.8153             nan     0.5000   -0.0025
##    240        0.7882             nan     0.5000   -0.0024
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1346             nan     0.5000    0.0702
##      2        1.0398             nan     0.5000    0.0492
##      3        0.9951             nan     0.5000    0.0122
##      4        0.9514             nan     0.5000    0.0115
##      5        0.9170             nan     0.5000    0.0056
##      6        0.8955             nan     0.5000    0.0019
##      7        0.8776             nan     0.5000    0.0003
##      8        0.8611             nan     0.5000   -0.0035
##      9        0.8504             nan     0.5000   -0.0001
##     10        0.8444             nan     0.5000   -0.0066
##     20        0.7623             nan     0.5000   -0.0030
##     40        0.6762             nan     0.5000   -0.0035
##     60        0.6089             nan     0.5000   -0.0055
##     80        0.5403             nan     0.5000   -0.0152
##    100        0.4874             nan     0.5000   -0.0077
##    120        0.4435             nan     0.5000   -0.0081
##    140        0.3958             nan     0.5000   -0.0121
##    160        0.3731             nan     0.5000   -0.0075
##    180        0.3300             nan     0.5000   -0.0024
##    200        0.2865             nan     0.5000   -0.0050
##    220        0.2523             nan     0.5000   -0.0024
##    240        0.2287             nan     0.5000   -0.0050
##    260        0.2087             nan     0.5000    0.0001
##    280        0.1882             nan     0.5000   -0.0033
##    300        0.1727             nan     0.5000   -0.0014
##    320        0.1612             nan     0.5000   -0.0001
##    340        0.1444             nan     0.5000   -0.0036
##    360        0.1294             nan     0.5000   -0.0009
##    380        0.1202             nan     0.5000    0.0001
##    400        0.1101             nan     0.5000   -0.0012
##    420        0.1026             nan     0.5000   -0.0022
##    440        0.0922             nan     0.5000   -0.0019
##    460        0.0867             nan     0.5000   -0.0006
##    480        0.0795             nan     0.5000   -0.0013
##    500        0.0736             nan     0.5000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1222             nan     0.5000    0.0776
##      2        1.0239             nan     0.5000    0.0407
##      3        0.9830             nan     0.5000    0.0017
##      4        0.9412             nan     0.5000    0.0184
##      5        0.9165             nan     0.5000    0.0036
##      6        0.8995             nan     0.5000    0.0045
##      7        0.8919             nan     0.5000   -0.0080
##      8        0.8812             nan     0.5000   -0.0139
##      9        0.8649             nan     0.5000    0.0018
##     10        0.8614             nan     0.5000   -0.0088
##     20        0.7958             nan     0.5000   -0.0037
##     40        0.6811             nan     0.5000   -0.0086
##     60        0.6038             nan     0.5000   -0.0101
##     80        0.5356             nan     0.5000   -0.0121
##    100        0.4810             nan     0.5000   -0.0056
##    120        0.4230             nan     0.5000   -0.0031
##    140        0.3844             nan     0.5000   -0.0011
##    160        0.3561             nan     0.5000   -0.0039
##    180        0.3199             nan     0.5000   -0.0017
##    200        0.2736             nan     0.5000   -0.0030
##    220        0.2556             nan     0.5000   -0.0025
##    240        0.2284             nan     0.5000   -0.0023
##    260        0.2063             nan     0.5000   -0.0024
##    280        0.1894             nan     0.5000   -0.0052
##    300        0.1681             nan     0.5000   -0.0007
##    320        0.1605             nan     0.5000   -0.0006
##    340        0.1520             nan     0.5000   -0.0027
##    360        0.1405             nan     0.5000   -0.0010
##    380        0.1261             nan     0.5000   -0.0020
##    400        0.1147             nan     0.5000   -0.0015
##    420        0.1096             nan     0.5000   -0.0008
##    440        0.0949             nan     0.5000   -0.0017
##    460        0.0874             nan     0.5000   -0.0009
##    480        0.0825             nan     0.5000   -0.0009
##    500        0.0746             nan     0.5000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0909             nan     0.5000    0.0841
##      2        1.0127             nan     0.5000    0.0243
##      3        0.9465             nan     0.5000    0.0242
##      4        0.9034             nan     0.5000    0.0168
##      5        0.8889             nan     0.5000   -0.0068
##      6        0.8866             nan     0.5000   -0.0217
##      7        0.8585             nan     0.5000    0.0012
##      8        0.8423             nan     0.5000   -0.0034
##      9        0.8363             nan     0.5000   -0.0069
##     10        0.8206             nan     0.5000   -0.0156
##     20        0.7239             nan     0.5000   -0.0063
##     40        0.6065             nan     0.5000   -0.0041
##     60        0.7013             nan     0.5000   -0.0046
##     80        0.8719             nan     0.5000   -0.0034
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000   -0.0000
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0995             nan     0.5000    0.0874
##      2        0.9964             nan     0.5000    0.0346
##      3        0.9258             nan     0.5000    0.0310
##      4        0.8957             nan     0.5000    0.0002
##      5        0.8701             nan     0.5000    0.0057
##      6        0.8491             nan     0.5000   -0.0093
##      7        0.8125             nan     0.5000    0.0059
##      8        0.7990             nan     0.5000   -0.0081
##      9        0.7906             nan     0.5000   -0.0149
##     10        0.7761             nan     0.5000   -0.0076
##     20        0.6780             nan     0.5000   -0.0040
##     40        0.5577             nan     0.5000    0.0003
##     60        0.4483             nan     0.5000   -0.0115
##     80        0.3901             nan     0.5000   -0.0088
##    100        0.3244             nan     0.5000   -0.0094
##    120        0.2779             nan     0.5000   -0.0036
##    140        0.2284             nan     0.5000   -0.0011
##    160        0.1905             nan     0.5000   -0.0070
##    180        0.1565             nan     0.5000   -0.0043
##    200        0.1360             nan     0.5000   -0.0016
##    220        0.1146             nan     0.5000   -0.0013
##    240        0.1026             nan     0.5000   -0.0014
##    260        0.0914             nan     0.5000   -0.0039
##    280        0.0764             nan     0.5000    0.0000
##    300        0.0647             nan     0.5000   -0.0000
##    320        0.0586             nan     0.5000   -0.0006
##    340        0.0504             nan     0.5000   -0.0003
##    360        0.0446             nan     0.5000   -0.0016
##    380        0.0392             nan     0.5000   -0.0011
##    400        0.0349             nan     0.5000   -0.0006
##    420        0.0315             nan     0.5000   -0.0006
##    440        0.0283             nan     0.5000   -0.0005
##    460        0.0256             nan     0.5000   -0.0005
##    480        0.0223             nan     0.5000   -0.0002
##    500        0.0195             nan     0.5000   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0983             nan     0.5000    0.0953
##      2        0.9804             nan     0.5000    0.0543
##      3        0.9362             nan     0.5000    0.0033
##      4        0.8995             nan     0.5000    0.0037
##      5        0.8685             nan     0.5000    0.0037
##      6        0.8488             nan     0.5000   -0.0039
##      7        0.8435             nan     0.5000   -0.0116
##      8        0.8335             nan     0.5000   -0.0176
##      9        0.8144             nan     0.5000    0.0014
##     10        0.8129             nan     0.5000   -0.0248
##     20        0.7193             nan     0.5000   -0.0116
##     40        0.5934             nan     0.5000   -0.0182
##     60        0.5380             nan     0.5000   -0.0158
##     80        0.4438             nan     0.5000   -0.0077
##    100        0.3841             nan     0.5000   -0.0092
##    120        0.3187             nan     0.5000   -0.0076
##    140        0.2673             nan     0.5000   -0.0047
##    160        0.2254             nan     0.5000   -0.0022
##    180        0.1867             nan     0.5000   -0.0018
##    200        0.1585             nan     0.5000   -0.0017
##    220        0.1313             nan     0.5000   -0.0018
##    240        0.1131             nan     0.5000   -0.0003
##    260        0.0994             nan     0.5000   -0.0003
##    280        0.0850             nan     0.5000   -0.0007
##    300        0.0740             nan     0.5000   -0.0017
##    320        0.0665             nan     0.5000   -0.0023
##    340        0.0585             nan     0.5000   -0.0021
##    360        0.0499             nan     0.5000   -0.0004
##    380        0.0440             nan     0.5000   -0.0006
##    400        0.0385             nan     0.5000   -0.0004
##    420        0.0338             nan     0.5000   -0.0006
##    440        0.0296             nan     0.5000   -0.0003
##    460        0.0255             nan     0.5000   -0.0003
##    480        0.0226             nan     0.5000   -0.0004
##    500        0.0198             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1438             nan     1.0000    0.0411
##      2        1.0793             nan     1.0000    0.0258
##      3        1.0249             nan     1.0000    0.0161
##      4        1.0114             nan     1.0000   -0.0105
##      5        1.0120             nan     1.0000   -0.0213
##      6        1.0051             nan     1.0000   -0.0160
##      7        0.9812             nan     1.0000   -0.0007
##      8        0.9782             nan     1.0000   -0.0152
##      9        0.9440             nan     1.0000    0.0010
##     10        0.9478             nan     1.0000   -0.0300
##     20        0.9318             nan     1.0000   -0.0333
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1393             nan     1.0000    0.0523
##      2        1.0629             nan     1.0000    0.0245
##      3        0.9865             nan     1.0000    0.0403
##      4        0.9433             nan     1.0000    0.0197
##      5        0.9425             nan     1.0000   -0.0182
##      6        0.9176             nan     1.0000    0.0031
##      7        0.9066             nan     1.0000   -0.0139
##      8        0.8991             nan     1.0000   -0.0110
##      9        0.8851             nan     1.0000   -0.0027
##     10        0.8832             nan     1.0000   -0.0075
##     20        0.8358             nan     1.0000   -0.0257
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1128             nan     1.0000    0.0827
##      2        1.0538             nan     1.0000    0.0188
##      3        0.9975             nan     1.0000    0.0032
##      4        0.9748             nan     1.0000   -0.0049
##      5        0.9516             nan     1.0000    0.0044
##      6        0.9427             nan     1.0000   -0.0106
##      7        0.9595             nan     1.0000   -0.0403
##      8        0.9663             nan     1.0000   -0.0242
##      9        0.9700             nan     1.0000   -0.0192
##     10        1.0035             nan     1.0000   -0.0035
##     20        0.9774             nan     1.0000   -0.0698
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0628             nan     1.0000    0.0974
##      2        0.9924             nan     1.0000    0.0254
##      3        0.9893             nan     1.0000   -0.0265
##      4        0.9956             nan     1.0000   -0.0422
##      5        0.9795             nan     1.0000   -0.0280
##      6        0.9753             nan     1.0000   -0.0203
##      7        0.9667             nan     1.0000   -0.0171
##      8     9452.3197             nan     1.0000 -9451.4068
##      9     9452.2987             nan     1.0000    0.0049
##     10     9452.3019             nan     1.0000   -0.0301
##     20     9452.3393             nan     1.0000   -0.0380
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000   -0.0464
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       inf
##    200           inf             nan     1.0000   -0.1423
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0724             nan     1.0000    0.0901
##      2        0.9948             nan     1.0000    0.0139
##      3        0.9837             nan     1.0000   -0.0162
##      4        1.0734             nan     1.0000   -0.1145
##      5        1.0784             nan     1.0000   -0.0527
##      6        1.1603             nan     1.0000   -0.1277
##      7        2.3378             nan     1.0000   -0.9167
##      8        2.3338             nan     1.0000   -0.0164
##      9        2.3114             nan     1.0000    0.0076
##     10        2.3135             nan     1.0000   -0.0144
##     20        4.0213             nan     1.0000   -0.0277
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0573             nan     1.0000    0.1000
##      2        0.9729             nan     1.0000    0.0122
##      3        0.9500             nan     1.0000   -0.0126
##      4        0.9692             nan     1.0000   -0.0475
##      5        0.9361             nan     1.0000    0.0134
##      6        0.9351             nan     1.0000   -0.0305
##      7        0.9245             nan     1.0000   -0.0195
##      8        1.0211             nan     1.0000   -0.1151
##      9      164.2296             nan     1.0000    0.0214
##     10      164.2280             nan     1.0000   -0.0290
##     20      164.1512             nan     1.0000    0.0019
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0336             nan     1.0000    0.0669
##      2        0.9963             nan     1.0000    0.0027
##      3        0.9107             nan     1.0000    0.0305
##      4        0.9034             nan     1.0000   -0.0336
##      5        0.9483             nan     1.0000   -0.0901
##      6        0.9302             nan     1.0000   -0.0379
##      7        0.8866             nan     1.0000   -0.0143
##      8        0.8938             nan     1.0000   -0.0671
##      9        0.8846             nan     1.0000   -0.0201
##     10        0.8656             nan     1.0000   -0.0323
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0602             nan     1.0000    0.0934
##      2        0.9411             nan     1.0000    0.0267
##      3        0.8890             nan     1.0000    0.0122
##      4        0.8873             nan     1.0000   -0.0429
##      5        0.8731             nan     1.0000   -0.0158
##      6        0.8540             nan     1.0000   -0.0344
##      7        0.8265             nan     1.0000   -0.0074
##      8        0.8245             nan     1.0000   -0.0305
##      9        0.8151             nan     1.0000   -0.0123
##     10        0.8088             nan     1.0000   -0.0211
##     20        0.7556             nan     1.0000    0.0544
##     40       13.3954             nan     1.0000   -0.5009
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       inf
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0626             nan     1.0000    0.0801
##      2        1.0018             nan     1.0000   -0.0081
##      3        0.9083             nan     1.0000    0.0388
##      4        0.8861             nan     1.0000   -0.0227
##      5        1.0433             nan     1.0000   -0.1919
##      6       49.4796             nan     1.0000  -48.5403
##      7       49.4700             nan     1.0000   -0.0179
##      8       49.4542             nan     1.0000   -0.0060
##      9       49.4352             nan     1.0000   -0.0419
##     10       49.4264             nan     1.0000   -0.0644
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2906             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2895             nan     0.0010    0.0002
##     20        1.2859             nan     0.0010    0.0002
##     40        1.2782             nan     0.0010    0.0002
##     60        1.2706             nan     0.0010    0.0002
##     80        1.2635             nan     0.0010    0.0002
##    100        1.2567             nan     0.0010    0.0002
##    120        1.2502             nan     0.0010    0.0001
##    140        1.2439             nan     0.0010    0.0002
##    160        1.2378             nan     0.0010    0.0001
##    180        1.2319             nan     0.0010    0.0001
##    200        1.2263             nan     0.0010    0.0001
##    220        1.2210             nan     0.0010    0.0001
##    240        1.2156             nan     0.0010    0.0001
##    260        1.2105             nan     0.0010    0.0001
##    280        1.2054             nan     0.0010    0.0001
##    300        1.2005             nan     0.0010    0.0001
##    320        1.1957             nan     0.0010    0.0001
##    340        1.1912             nan     0.0010    0.0001
##    360        1.1867             nan     0.0010    0.0001
##    380        1.1825             nan     0.0010    0.0001
##    400        1.1782             nan     0.0010    0.0001
##    420        1.1739             nan     0.0010    0.0001
##    440        1.1699             nan     0.0010    0.0001
##    460        1.1659             nan     0.0010    0.0001
##    480        1.1621             nan     0.0010    0.0001
##    500        1.1584             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2921             nan     0.0010    0.0002
##      4        1.2917             nan     0.0010    0.0002
##      5        1.2913             nan     0.0010    0.0002
##      6        1.2909             nan     0.0010    0.0002
##      7        1.2905             nan     0.0010    0.0002
##      8        1.2901             nan     0.0010    0.0002
##      9        1.2897             nan     0.0010    0.0002
##     10        1.2892             nan     0.0010    0.0002
##     20        1.2854             nan     0.0010    0.0002
##     40        1.2781             nan     0.0010    0.0002
##     60        1.2706             nan     0.0010    0.0002
##     80        1.2636             nan     0.0010    0.0002
##    100        1.2567             nan     0.0010    0.0001
##    120        1.2501             nan     0.0010    0.0001
##    140        1.2438             nan     0.0010    0.0001
##    160        1.2376             nan     0.0010    0.0001
##    180        1.2316             nan     0.0010    0.0001
##    200        1.2258             nan     0.0010    0.0001
##    220        1.2203             nan     0.0010    0.0001
##    240        1.2150             nan     0.0010    0.0001
##    260        1.2098             nan     0.0010    0.0001
##    280        1.2049             nan     0.0010    0.0001
##    300        1.2001             nan     0.0010    0.0001
##    320        1.1955             nan     0.0010    0.0001
##    340        1.1911             nan     0.0010    0.0001
##    360        1.1868             nan     0.0010    0.0001
##    380        1.1824             nan     0.0010    0.0001
##    400        1.1781             nan     0.0010    0.0001
##    420        1.1739             nan     0.0010    0.0001
##    440        1.1701             nan     0.0010    0.0001
##    460        1.1661             nan     0.0010    0.0001
##    480        1.1622             nan     0.0010    0.0001
##    500        1.1586             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0001
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2856             nan     0.0010    0.0002
##     40        1.2779             nan     0.0010    0.0002
##     60        1.2705             nan     0.0010    0.0002
##     80        1.2631             nan     0.0010    0.0002
##    100        1.2562             nan     0.0010    0.0002
##    120        1.2498             nan     0.0010    0.0001
##    140        1.2438             nan     0.0010    0.0001
##    160        1.2376             nan     0.0010    0.0001
##    180        1.2319             nan     0.0010    0.0001
##    200        1.2261             nan     0.0010    0.0001
##    220        1.2206             nan     0.0010    0.0001
##    240        1.2152             nan     0.0010    0.0001
##    260        1.2100             nan     0.0010    0.0001
##    280        1.2049             nan     0.0010    0.0001
##    300        1.2001             nan     0.0010    0.0001
##    320        1.1953             nan     0.0010    0.0001
##    340        1.1907             nan     0.0010    0.0001
##    360        1.1863             nan     0.0010    0.0001
##    380        1.1819             nan     0.0010    0.0001
##    400        1.1777             nan     0.0010    0.0001
##    420        1.1736             nan     0.0010    0.0001
##    440        1.1696             nan     0.0010    0.0001
##    460        1.1658             nan     0.0010    0.0001
##    480        1.1619             nan     0.0010    0.0001
##    500        1.1583             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2913             nan     0.0010    0.0003
##      5        1.2908             nan     0.0010    0.0002
##      6        1.2903             nan     0.0010    0.0002
##      7        1.2898             nan     0.0010    0.0002
##      8        1.2893             nan     0.0010    0.0002
##      9        1.2888             nan     0.0010    0.0002
##     10        1.2884             nan     0.0010    0.0002
##     20        1.2834             nan     0.0010    0.0002
##     40        1.2738             nan     0.0010    0.0002
##     60        1.2646             nan     0.0010    0.0002
##     80        1.2555             nan     0.0010    0.0002
##    100        1.2470             nan     0.0010    0.0002
##    120        1.2390             nan     0.0010    0.0002
##    140        1.2308             nan     0.0010    0.0002
##    160        1.2229             nan     0.0010    0.0002
##    180        1.2156             nan     0.0010    0.0002
##    200        1.2082             nan     0.0010    0.0002
##    220        1.2009             nan     0.0010    0.0002
##    240        1.1940             nan     0.0010    0.0001
##    260        1.1873             nan     0.0010    0.0001
##    280        1.1808             nan     0.0010    0.0001
##    300        1.1744             nan     0.0010    0.0001
##    320        1.1681             nan     0.0010    0.0001
##    340        1.1621             nan     0.0010    0.0001
##    360        1.1562             nan     0.0010    0.0001
##    380        1.1504             nan     0.0010    0.0001
##    400        1.1447             nan     0.0010    0.0001
##    420        1.1393             nan     0.0010    0.0001
##    440        1.1341             nan     0.0010    0.0001
##    460        1.1291             nan     0.0010    0.0001
##    480        1.1240             nan     0.0010    0.0001
##    500        1.1191             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2908             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2898             nan     0.0010    0.0002
##      8        1.2892             nan     0.0010    0.0002
##      9        1.2887             nan     0.0010    0.0002
##     10        1.2882             nan     0.0010    0.0002
##     20        1.2833             nan     0.0010    0.0002
##     40        1.2736             nan     0.0010    0.0002
##     60        1.2646             nan     0.0010    0.0002
##     80        1.2556             nan     0.0010    0.0002
##    100        1.2470             nan     0.0010    0.0002
##    120        1.2389             nan     0.0010    0.0002
##    140        1.2309             nan     0.0010    0.0002
##    160        1.2231             nan     0.0010    0.0002
##    180        1.2155             nan     0.0010    0.0002
##    200        1.2082             nan     0.0010    0.0002
##    220        1.2012             nan     0.0010    0.0001
##    240        1.1942             nan     0.0010    0.0002
##    260        1.1873             nan     0.0010    0.0001
##    280        1.1808             nan     0.0010    0.0001
##    300        1.1744             nan     0.0010    0.0001
##    320        1.1683             nan     0.0010    0.0001
##    340        1.1622             nan     0.0010    0.0001
##    360        1.1564             nan     0.0010    0.0001
##    380        1.1507             nan     0.0010    0.0001
##    400        1.1454             nan     0.0010    0.0001
##    420        1.1398             nan     0.0010    0.0001
##    440        1.1345             nan     0.0010    0.0001
##    460        1.1294             nan     0.0010    0.0001
##    480        1.1245             nan     0.0010    0.0001
##    500        1.1197             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2889             nan     0.0010    0.0002
##     10        1.2884             nan     0.0010    0.0002
##     20        1.2835             nan     0.0010    0.0002
##     40        1.2739             nan     0.0010    0.0002
##     60        1.2645             nan     0.0010    0.0002
##     80        1.2556             nan     0.0010    0.0002
##    100        1.2471             nan     0.0010    0.0002
##    120        1.2387             nan     0.0010    0.0002
##    140        1.2307             nan     0.0010    0.0001
##    160        1.2227             nan     0.0010    0.0002
##    180        1.2151             nan     0.0010    0.0002
##    200        1.2078             nan     0.0010    0.0002
##    220        1.2010             nan     0.0010    0.0001
##    240        1.1939             nan     0.0010    0.0001
##    260        1.1872             nan     0.0010    0.0002
##    280        1.1809             nan     0.0010    0.0001
##    300        1.1746             nan     0.0010    0.0001
##    320        1.1685             nan     0.0010    0.0001
##    340        1.1624             nan     0.0010    0.0001
##    360        1.1565             nan     0.0010    0.0001
##    380        1.1509             nan     0.0010    0.0001
##    400        1.1455             nan     0.0010    0.0001
##    420        1.1400             nan     0.0010    0.0001
##    440        1.1347             nan     0.0010    0.0001
##    460        1.1295             nan     0.0010    0.0001
##    480        1.1244             nan     0.0010    0.0001
##    500        1.1196             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2927             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2916             nan     0.0010    0.0002
##      4        1.2910             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0002
##      7        1.2894             nan     0.0010    0.0003
##      8        1.2888             nan     0.0010    0.0002
##      9        1.2883             nan     0.0010    0.0003
##     10        1.2877             nan     0.0010    0.0003
##     20        1.2823             nan     0.0010    0.0002
##     40        1.2714             nan     0.0010    0.0003
##     60        1.2609             nan     0.0010    0.0002
##     80        1.2505             nan     0.0010    0.0002
##    100        1.2407             nan     0.0010    0.0002
##    120        1.2311             nan     0.0010    0.0002
##    140        1.2219             nan     0.0010    0.0002
##    160        1.2132             nan     0.0010    0.0001
##    180        1.2048             nan     0.0010    0.0002
##    200        1.1964             nan     0.0010    0.0002
##    220        1.1882             nan     0.0010    0.0002
##    240        1.1803             nan     0.0010    0.0002
##    260        1.1726             nan     0.0010    0.0001
##    280        1.1650             nan     0.0010    0.0002
##    300        1.1577             nan     0.0010    0.0002
##    320        1.1508             nan     0.0010    0.0002
##    340        1.1443             nan     0.0010    0.0001
##    360        1.1377             nan     0.0010    0.0001
##    380        1.1312             nan     0.0010    0.0001
##    400        1.1251             nan     0.0010    0.0001
##    420        1.1188             nan     0.0010    0.0001
##    440        1.1128             nan     0.0010    0.0001
##    460        1.1069             nan     0.0010    0.0001
##    480        1.1012             nan     0.0010    0.0001
##    500        1.0957             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2927             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2916             nan     0.0010    0.0003
##      4        1.2910             nan     0.0010    0.0003
##      5        1.2905             nan     0.0010    0.0002
##      6        1.2899             nan     0.0010    0.0002
##      7        1.2893             nan     0.0010    0.0003
##      8        1.2888             nan     0.0010    0.0002
##      9        1.2882             nan     0.0010    0.0003
##     10        1.2876             nan     0.0010    0.0002
##     20        1.2819             nan     0.0010    0.0002
##     40        1.2712             nan     0.0010    0.0002
##     60        1.2609             nan     0.0010    0.0002
##     80        1.2508             nan     0.0010    0.0002
##    100        1.2410             nan     0.0010    0.0002
##    120        1.2315             nan     0.0010    0.0002
##    140        1.2222             nan     0.0010    0.0002
##    160        1.2131             nan     0.0010    0.0002
##    180        1.2044             nan     0.0010    0.0002
##    200        1.1962             nan     0.0010    0.0002
##    220        1.1880             nan     0.0010    0.0002
##    240        1.1802             nan     0.0010    0.0002
##    260        1.1727             nan     0.0010    0.0002
##    280        1.1653             nan     0.0010    0.0002
##    300        1.1581             nan     0.0010    0.0002
##    320        1.1511             nan     0.0010    0.0001
##    340        1.1444             nan     0.0010    0.0001
##    360        1.1377             nan     0.0010    0.0001
##    380        1.1314             nan     0.0010    0.0001
##    400        1.1250             nan     0.0010    0.0001
##    420        1.1190             nan     0.0010    0.0001
##    440        1.1132             nan     0.0010    0.0001
##    460        1.1076             nan     0.0010    0.0001
##    480        1.1020             nan     0.0010    0.0001
##    500        1.0965             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0003
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0003
##      8        1.2890             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2821             nan     0.0010    0.0002
##     40        1.2712             nan     0.0010    0.0002
##     60        1.2611             nan     0.0010    0.0002
##     80        1.2510             nan     0.0010    0.0002
##    100        1.2414             nan     0.0010    0.0002
##    120        1.2320             nan     0.0010    0.0002
##    140        1.2231             nan     0.0010    0.0002
##    160        1.2141             nan     0.0010    0.0002
##    180        1.2056             nan     0.0010    0.0002
##    200        1.1973             nan     0.0010    0.0002
##    220        1.1892             nan     0.0010    0.0002
##    240        1.1813             nan     0.0010    0.0002
##    260        1.1736             nan     0.0010    0.0002
##    280        1.1659             nan     0.0010    0.0002
##    300        1.1586             nan     0.0010    0.0002
##    320        1.1518             nan     0.0010    0.0002
##    340        1.1450             nan     0.0010    0.0001
##    360        1.1383             nan     0.0010    0.0001
##    380        1.1319             nan     0.0010    0.0001
##    400        1.1255             nan     0.0010    0.0001
##    420        1.1192             nan     0.0010    0.0001
##    440        1.1132             nan     0.0010    0.0001
##    460        1.1074             nan     0.0010    0.0001
##    480        1.1016             nan     0.0010    0.0001
##    500        1.0962             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2576             nan     0.1000    0.0187
##      2        1.2228             nan     0.1000    0.0149
##      3        1.2037             nan     0.1000    0.0074
##      4        1.1775             nan     0.1000    0.0099
##      5        1.1542             nan     0.1000    0.0091
##      6        1.1392             nan     0.1000    0.0050
##      7        1.1221             nan     0.1000    0.0066
##      8        1.1049             nan     0.1000    0.0077
##      9        1.0906             nan     0.1000    0.0059
##     10        1.0811             nan     0.1000    0.0035
##     20        0.9972             nan     0.1000    0.0007
##     40        0.9117             nan     0.1000    0.0003
##     60        0.8679             nan     0.1000    0.0001
##     80        0.8383             nan     0.1000   -0.0005
##    100        0.8194             nan     0.1000   -0.0009
##    120        0.8011             nan     0.1000   -0.0006
##    140        0.7901             nan     0.1000   -0.0003
##    160        0.7787             nan     0.1000   -0.0027
##    180        0.7680             nan     0.1000   -0.0004
##    200        0.7583             nan     0.1000    0.0001
##    220        0.7501             nan     0.1000   -0.0017
##    240        0.7434             nan     0.1000   -0.0009
##    260        0.7349             nan     0.1000   -0.0015
##    280        0.7303             nan     0.1000   -0.0012
##    300        0.7233             nan     0.1000   -0.0017
##    320        0.7182             nan     0.1000   -0.0007
##    340        0.7146             nan     0.1000   -0.0008
##    360        0.7095             nan     0.1000   -0.0006
##    380        0.7033             nan     0.1000   -0.0007
##    400        0.7000             nan     0.1000   -0.0019
##    420        0.6954             nan     0.1000   -0.0006
##    440        0.6914             nan     0.1000   -0.0013
##    460        0.6870             nan     0.1000   -0.0009
##    480        0.6834             nan     0.1000   -0.0006
##    500        0.6806             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2576             nan     0.1000    0.0173
##      2        1.2249             nan     0.1000    0.0129
##      3        1.1974             nan     0.1000    0.0128
##      4        1.1776             nan     0.1000    0.0072
##      5        1.1534             nan     0.1000    0.0095
##      6        1.1363             nan     0.1000    0.0072
##      7        1.1189             nan     0.1000    0.0069
##      8        1.1040             nan     0.1000    0.0067
##      9        1.0914             nan     0.1000    0.0053
##     10        1.0790             nan     0.1000    0.0061
##     20        0.9957             nan     0.1000    0.0026
##     40        0.9130             nan     0.1000    0.0005
##     60        0.8714             nan     0.1000    0.0004
##     80        0.8433             nan     0.1000   -0.0010
##    100        0.8261             nan     0.1000   -0.0005
##    120        0.8083             nan     0.1000   -0.0005
##    140        0.7935             nan     0.1000   -0.0007
##    160        0.7830             nan     0.1000   -0.0002
##    180        0.7722             nan     0.1000   -0.0003
##    200        0.7616             nan     0.1000   -0.0012
##    220        0.7532             nan     0.1000   -0.0012
##    240        0.7456             nan     0.1000   -0.0016
##    260        0.7404             nan     0.1000   -0.0021
##    280        0.7344             nan     0.1000   -0.0009
##    300        0.7297             nan     0.1000   -0.0013
##    320        0.7235             nan     0.1000   -0.0008
##    340        0.7197             nan     0.1000   -0.0013
##    360        0.7131             nan     0.1000   -0.0013
##    380        0.7066             nan     0.1000   -0.0006
##    400        0.7008             nan     0.1000   -0.0019
##    420        0.6966             nan     0.1000   -0.0003
##    440        0.6926             nan     0.1000   -0.0009
##    460        0.6872             nan     0.1000   -0.0009
##    480        0.6855             nan     0.1000   -0.0014
##    500        0.6809             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2561             nan     0.1000    0.0167
##      2        1.2286             nan     0.1000    0.0142
##      3        1.1968             nan     0.1000    0.0111
##      4        1.1756             nan     0.1000    0.0100
##      5        1.1592             nan     0.1000    0.0056
##      6        1.1433             nan     0.1000    0.0077
##      7        1.1276             nan     0.1000    0.0051
##      8        1.1134             nan     0.1000    0.0067
##      9        1.1006             nan     0.1000    0.0052
##     10        1.0853             nan     0.1000    0.0060
##     20        0.9984             nan     0.1000    0.0023
##     40        0.9112             nan     0.1000    0.0007
##     60        0.8696             nan     0.1000    0.0003
##     80        0.8426             nan     0.1000   -0.0003
##    100        0.8235             nan     0.1000   -0.0009
##    120        0.8090             nan     0.1000   -0.0006
##    140        0.7953             nan     0.1000   -0.0018
##    160        0.7850             nan     0.1000   -0.0007
##    180        0.7728             nan     0.1000   -0.0007
##    200        0.7627             nan     0.1000   -0.0011
##    220        0.7543             nan     0.1000   -0.0007
##    240        0.7466             nan     0.1000   -0.0015
##    260        0.7402             nan     0.1000   -0.0014
##    280        0.7333             nan     0.1000   -0.0010
##    300        0.7263             nan     0.1000   -0.0012
##    320        0.7207             nan     0.1000   -0.0007
##    340        0.7162             nan     0.1000   -0.0013
##    360        0.7089             nan     0.1000   -0.0009
##    380        0.7033             nan     0.1000   -0.0002
##    400        0.6990             nan     0.1000   -0.0009
##    420        0.6948             nan     0.1000   -0.0006
##    440        0.6913             nan     0.1000   -0.0009
##    460        0.6883             nan     0.1000   -0.0011
##    480        0.6848             nan     0.1000   -0.0007
##    500        0.6810             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2538             nan     0.1000    0.0171
##      2        1.2117             nan     0.1000    0.0181
##      3        1.1785             nan     0.1000    0.0139
##      4        1.1474             nan     0.1000    0.0136
##      5        1.1197             nan     0.1000    0.0103
##      6        1.0988             nan     0.1000    0.0101
##      7        1.0753             nan     0.1000    0.0097
##      8        1.0606             nan     0.1000    0.0054
##      9        1.0435             nan     0.1000    0.0039
##     10        1.0280             nan     0.1000    0.0029
##     20        0.9329             nan     0.1000   -0.0004
##     40        0.8406             nan     0.1000   -0.0002
##     60        0.7926             nan     0.1000   -0.0013
##     80        0.7622             nan     0.1000   -0.0023
##    100        0.7319             nan     0.1000   -0.0007
##    120        0.7049             nan     0.1000   -0.0004
##    140        0.6776             nan     0.1000   -0.0022
##    160        0.6598             nan     0.1000   -0.0010
##    180        0.6410             nan     0.1000   -0.0012
##    200        0.6183             nan     0.1000   -0.0019
##    220        0.5962             nan     0.1000   -0.0014
##    240        0.5781             nan     0.1000   -0.0001
##    260        0.5602             nan     0.1000   -0.0010
##    280        0.5458             nan     0.1000   -0.0014
##    300        0.5310             nan     0.1000   -0.0010
##    320        0.5152             nan     0.1000   -0.0003
##    340        0.5030             nan     0.1000   -0.0013
##    360        0.4930             nan     0.1000   -0.0008
##    380        0.4797             nan     0.1000   -0.0008
##    400        0.4665             nan     0.1000   -0.0004
##    420        0.4548             nan     0.1000   -0.0010
##    440        0.4411             nan     0.1000   -0.0007
##    460        0.4300             nan     0.1000   -0.0001
##    480        0.4204             nan     0.1000   -0.0008
##    500        0.4097             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2436             nan     0.1000    0.0212
##      2        1.2041             nan     0.1000    0.0198
##      3        1.1703             nan     0.1000    0.0153
##      4        1.1378             nan     0.1000    0.0137
##      5        1.1135             nan     0.1000    0.0106
##      6        1.0902             nan     0.1000    0.0089
##      7        1.0728             nan     0.1000    0.0064
##      8        1.0551             nan     0.1000    0.0047
##      9        1.0392             nan     0.1000    0.0063
##     10        1.0238             nan     0.1000    0.0043
##     20        0.9266             nan     0.1000    0.0011
##     40        0.8407             nan     0.1000   -0.0004
##     60        0.7922             nan     0.1000   -0.0011
##     80        0.7575             nan     0.1000   -0.0001
##    100        0.7297             nan     0.1000   -0.0018
##    120        0.7092             nan     0.1000   -0.0003
##    140        0.6878             nan     0.1000   -0.0017
##    160        0.6645             nan     0.1000   -0.0013
##    180        0.6427             nan     0.1000   -0.0004
##    200        0.6232             nan     0.1000   -0.0009
##    220        0.6061             nan     0.1000   -0.0009
##    240        0.5876             nan     0.1000   -0.0004
##    260        0.5721             nan     0.1000   -0.0015
##    280        0.5566             nan     0.1000   -0.0007
##    300        0.5460             nan     0.1000   -0.0003
##    320        0.5309             nan     0.1000   -0.0005
##    340        0.5161             nan     0.1000   -0.0016
##    360        0.5053             nan     0.1000   -0.0013
##    380        0.4908             nan     0.1000   -0.0016
##    400        0.4760             nan     0.1000   -0.0008
##    420        0.4674             nan     0.1000   -0.0010
##    440        0.4601             nan     0.1000   -0.0011
##    460        0.4484             nan     0.1000   -0.0006
##    480        0.4378             nan     0.1000   -0.0011
##    500        0.4277             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2440             nan     0.1000    0.0216
##      2        1.2042             nan     0.1000    0.0190
##      3        1.1704             nan     0.1000    0.0141
##      4        1.1388             nan     0.1000    0.0145
##      5        1.1172             nan     0.1000    0.0097
##      6        1.0939             nan     0.1000    0.0099
##      7        1.0732             nan     0.1000    0.0079
##      8        1.0580             nan     0.1000    0.0054
##      9        1.0403             nan     0.1000    0.0067
##     10        1.0233             nan     0.1000    0.0075
##     20        0.9222             nan     0.1000   -0.0004
##     40        0.8451             nan     0.1000   -0.0006
##     60        0.7972             nan     0.1000   -0.0026
##     80        0.7628             nan     0.1000   -0.0005
##    100        0.7393             nan     0.1000   -0.0009
##    120        0.7141             nan     0.1000   -0.0012
##    140        0.6920             nan     0.1000   -0.0015
##    160        0.6718             nan     0.1000   -0.0014
##    180        0.6562             nan     0.1000   -0.0014
##    200        0.6319             nan     0.1000   -0.0013
##    220        0.6150             nan     0.1000   -0.0011
##    240        0.5954             nan     0.1000   -0.0001
##    260        0.5809             nan     0.1000   -0.0007
##    280        0.5653             nan     0.1000   -0.0009
##    300        0.5525             nan     0.1000   -0.0008
##    320        0.5366             nan     0.1000   -0.0003
##    340        0.5252             nan     0.1000   -0.0013
##    360        0.5131             nan     0.1000   -0.0027
##    380        0.5028             nan     0.1000   -0.0010
##    400        0.4934             nan     0.1000   -0.0012
##    420        0.4829             nan     0.1000   -0.0019
##    440        0.4718             nan     0.1000   -0.0009
##    460        0.4620             nan     0.1000   -0.0013
##    480        0.4517             nan     0.1000   -0.0009
##    500        0.4396             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2474             nan     0.1000    0.0204
##      2        1.1979             nan     0.1000    0.0194
##      3        1.1618             nan     0.1000    0.0163
##      4        1.1260             nan     0.1000    0.0159
##      5        1.0947             nan     0.1000    0.0144
##      6        1.0701             nan     0.1000    0.0093
##      7        1.0478             nan     0.1000    0.0069
##      8        1.0256             nan     0.1000    0.0081
##      9        1.0084             nan     0.1000    0.0058
##     10        0.9912             nan     0.1000    0.0075
##     20        0.8883             nan     0.1000    0.0014
##     40        0.7888             nan     0.1000   -0.0015
##     60        0.7318             nan     0.1000   -0.0010
##     80        0.6836             nan     0.1000   -0.0014
##    100        0.6470             nan     0.1000   -0.0008
##    120        0.6154             nan     0.1000   -0.0007
##    140        0.5796             nan     0.1000   -0.0015
##    160        0.5542             nan     0.1000    0.0001
##    180        0.5257             nan     0.1000   -0.0001
##    200        0.4983             nan     0.1000   -0.0006
##    220        0.4760             nan     0.1000   -0.0015
##    240        0.4532             nan     0.1000   -0.0006
##    260        0.4340             nan     0.1000   -0.0015
##    280        0.4145             nan     0.1000   -0.0001
##    300        0.3967             nan     0.1000   -0.0011
##    320        0.3835             nan     0.1000   -0.0008
##    340        0.3681             nan     0.1000   -0.0013
##    360        0.3521             nan     0.1000   -0.0010
##    380        0.3405             nan     0.1000   -0.0013
##    400        0.3247             nan     0.1000   -0.0007
##    420        0.3126             nan     0.1000   -0.0010
##    440        0.3039             nan     0.1000   -0.0009
##    460        0.2933             nan     0.1000   -0.0004
##    480        0.2841             nan     0.1000   -0.0010
##    500        0.2741             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2428             nan     0.1000    0.0204
##      2        1.1946             nan     0.1000    0.0232
##      3        1.1512             nan     0.1000    0.0187
##      4        1.1230             nan     0.1000    0.0128
##      5        1.0993             nan     0.1000    0.0101
##      6        1.0704             nan     0.1000    0.0114
##      7        1.0474             nan     0.1000    0.0084
##      8        1.0263             nan     0.1000    0.0077
##      9        1.0061             nan     0.1000    0.0071
##     10        0.9914             nan     0.1000    0.0044
##     20        0.8874             nan     0.1000   -0.0002
##     40        0.7844             nan     0.1000   -0.0011
##     60        0.7272             nan     0.1000   -0.0025
##     80        0.6753             nan     0.1000   -0.0012
##    100        0.6390             nan     0.1000   -0.0012
##    120        0.6072             nan     0.1000   -0.0012
##    140        0.5798             nan     0.1000   -0.0013
##    160        0.5486             nan     0.1000   -0.0014
##    180        0.5279             nan     0.1000   -0.0016
##    200        0.5042             nan     0.1000   -0.0009
##    220        0.4842             nan     0.1000   -0.0011
##    240        0.4582             nan     0.1000   -0.0013
##    260        0.4371             nan     0.1000   -0.0004
##    280        0.4202             nan     0.1000   -0.0004
##    300        0.4014             nan     0.1000   -0.0008
##    320        0.3865             nan     0.1000   -0.0013
##    340        0.3692             nan     0.1000   -0.0009
##    360        0.3525             nan     0.1000   -0.0006
##    380        0.3400             nan     0.1000   -0.0007
##    400        0.3276             nan     0.1000   -0.0010
##    420        0.3143             nan     0.1000   -0.0007
##    440        0.3042             nan     0.1000   -0.0015
##    460        0.2910             nan     0.1000   -0.0002
##    480        0.2795             nan     0.1000   -0.0010
##    500        0.2695             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2364             nan     0.1000    0.0224
##      2        1.1926             nan     0.1000    0.0178
##      3        1.1523             nan     0.1000    0.0167
##      4        1.1144             nan     0.1000    0.0136
##      5        1.0840             nan     0.1000    0.0131
##      6        1.0589             nan     0.1000    0.0107
##      7        1.0342             nan     0.1000    0.0068
##      8        1.0141             nan     0.1000    0.0085
##      9        0.9990             nan     0.1000    0.0057
##     10        0.9826             nan     0.1000    0.0052
##     20        0.8846             nan     0.1000    0.0011
##     40        0.7835             nan     0.1000   -0.0001
##     60        0.7228             nan     0.1000   -0.0014
##     80        0.6742             nan     0.1000   -0.0019
##    100        0.6413             nan     0.1000   -0.0020
##    120        0.6129             nan     0.1000   -0.0006
##    140        0.5827             nan     0.1000   -0.0014
##    160        0.5562             nan     0.1000   -0.0015
##    180        0.5332             nan     0.1000   -0.0019
##    200        0.5122             nan     0.1000   -0.0014
##    220        0.4896             nan     0.1000   -0.0006
##    240        0.4680             nan     0.1000   -0.0007
##    260        0.4481             nan     0.1000   -0.0013
##    280        0.4288             nan     0.1000   -0.0009
##    300        0.4097             nan     0.1000   -0.0007
##    320        0.3931             nan     0.1000   -0.0011
##    340        0.3784             nan     0.1000   -0.0015
##    360        0.3627             nan     0.1000   -0.0007
##    380        0.3477             nan     0.1000   -0.0011
##    400        0.3347             nan     0.1000   -0.0009
##    420        0.3215             nan     0.1000   -0.0004
##    440        0.3099             nan     0.1000   -0.0000
##    460        0.2987             nan     0.1000   -0.0008
##    480        0.2873             nan     0.1000   -0.0004
##    500        0.2775             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2158             nan     0.2000    0.0309
##      2        1.1696             nan     0.2000    0.0232
##      3        1.1404             nan     0.2000    0.0112
##      4        1.1099             nan     0.2000    0.0135
##      5        1.0853             nan     0.2000    0.0052
##      6        1.0564             nan     0.2000    0.0107
##      7        1.0340             nan     0.2000    0.0088
##      8        1.0180             nan     0.2000    0.0058
##      9        1.0049             nan     0.2000    0.0033
##     10        0.9940             nan     0.2000    0.0033
##     20        0.9128             nan     0.2000   -0.0003
##     40        0.8449             nan     0.2000   -0.0011
##     60        0.8096             nan     0.2000    0.0005
##     80        0.7913             nan     0.2000   -0.0023
##    100        0.7703             nan     0.2000   -0.0009
##    120        0.7521             nan     0.2000   -0.0032
##    140        0.7389             nan     0.2000   -0.0039
##    160        0.7234             nan     0.2000   -0.0014
##    180        0.7143             nan     0.2000   -0.0014
##    200        0.7086             nan     0.2000   -0.0037
##    220        0.7006             nan     0.2000   -0.0041
##    240        0.6922             nan     0.2000   -0.0005
##    260        0.6825             nan     0.2000   -0.0012
##    280        0.6757             nan     0.2000   -0.0021
##    300        0.6723             nan     0.2000   -0.0029
##    320        0.6656             nan     0.2000   -0.0028
##    340        0.6604             nan     0.2000   -0.0017
##    360        0.6565             nan     0.2000   -0.0024
##    380        0.6502             nan     0.2000   -0.0032
##    400        0.6479             nan     0.2000   -0.0017
##    420        0.6419             nan     0.2000   -0.0011
##    440        0.6357             nan     0.2000   -0.0026
##    460        0.6296             nan     0.2000   -0.0022
##    480        0.6251             nan     0.2000   -0.0021
##    500        0.6199             nan     0.2000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2207             nan     0.2000    0.0319
##      2        1.1727             nan     0.2000    0.0218
##      3        1.1412             nan     0.2000    0.0109
##      4        1.1031             nan     0.2000    0.0110
##      5        1.0750             nan     0.2000    0.0124
##      6        1.0512             nan     0.2000    0.0084
##      7        1.0344             nan     0.2000    0.0058
##      8        1.0236             nan     0.2000    0.0015
##      9        1.0052             nan     0.2000    0.0072
##     10        0.9900             nan     0.2000    0.0043
##     20        0.9057             nan     0.2000   -0.0005
##     40        0.8342             nan     0.2000   -0.0016
##     60        0.7984             nan     0.2000   -0.0040
##     80        0.7755             nan     0.2000   -0.0014
##    100        0.7561             nan     0.2000   -0.0022
##    120        0.7413             nan     0.2000   -0.0015
##    140        0.7304             nan     0.2000   -0.0023
##    160        0.7161             nan     0.2000   -0.0016
##    180        0.7080             nan     0.2000   -0.0028
##    200        0.6989             nan     0.2000   -0.0027
##    220        0.6879             nan     0.2000   -0.0025
##    240        0.6793             nan     0.2000    0.0001
##    260        0.6757             nan     0.2000   -0.0022
##    280        0.6673             nan     0.2000   -0.0037
##    300        0.6610             nan     0.2000   -0.0016
##    320        0.6549             nan     0.2000   -0.0014
##    340        0.6493             nan     0.2000   -0.0018
##    360        0.6428             nan     0.2000   -0.0007
##    380        0.6391             nan     0.2000   -0.0022
##    400        0.6355             nan     0.2000   -0.0021
##    420        0.6295             nan     0.2000   -0.0019
##    440        0.6215             nan     0.2000   -0.0023
##    460        0.6154             nan     0.2000   -0.0009
##    480        0.6083             nan     0.2000   -0.0008
##    500        0.6048             nan     0.2000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2138             nan     0.2000    0.0335
##      2        1.1630             nan     0.2000    0.0206
##      3        1.1317             nan     0.2000    0.0150
##      4        1.1053             nan     0.2000    0.0079
##      5        1.0777             nan     0.2000    0.0085
##      6        1.0588             nan     0.2000    0.0070
##      7        1.0374             nan     0.2000    0.0095
##      8        1.0197             nan     0.2000    0.0072
##      9        1.0061             nan     0.2000    0.0031
##     10        0.9964             nan     0.2000    0.0016
##     20        0.9027             nan     0.2000    0.0003
##     40        0.8445             nan     0.2000   -0.0005
##     60        0.8044             nan     0.2000   -0.0014
##     80        0.7793             nan     0.2000   -0.0016
##    100        0.7601             nan     0.2000   -0.0028
##    120        0.7455             nan     0.2000   -0.0037
##    140        0.7374             nan     0.2000   -0.0015
##    160        0.7244             nan     0.2000   -0.0009
##    180        0.7151             nan     0.2000   -0.0011
##    200        0.6996             nan     0.2000   -0.0020
##    220        0.6932             nan     0.2000   -0.0017
##    240        0.6856             nan     0.2000   -0.0010
##    260        0.6818             nan     0.2000   -0.0019
##    280        0.6706             nan     0.2000   -0.0018
##    300        0.6662             nan     0.2000   -0.0015
##    320        0.6608             nan     0.2000   -0.0002
##    340        0.6537             nan     0.2000   -0.0031
##    360        0.6476             nan     0.2000   -0.0004
##    380        0.6442             nan     0.2000   -0.0021
##    400        0.6409             nan     0.2000   -0.0013
##    420        0.6330             nan     0.2000   -0.0018
##    440        0.6277             nan     0.2000   -0.0009
##    460        0.6207             nan     0.2000   -0.0004
##    480        0.6160             nan     0.2000    0.0001
##    500        0.6095             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2127             nan     0.2000    0.0421
##      2        1.1466             nan     0.2000    0.0316
##      3        1.0956             nan     0.2000    0.0153
##      4        1.0519             nan     0.2000    0.0167
##      5        1.0156             nan     0.2000    0.0167
##      6        0.9893             nan     0.2000    0.0092
##      7        0.9676             nan     0.2000    0.0075
##      8        0.9493             nan     0.2000    0.0025
##      9        0.9353             nan     0.2000    0.0033
##     10        0.9204             nan     0.2000    0.0046
##     20        0.8350             nan     0.2000   -0.0050
##     40        0.7491             nan     0.2000   -0.0009
##     60        0.7030             nan     0.2000   -0.0032
##     80        0.6615             nan     0.2000   -0.0041
##    100        0.6286             nan     0.2000   -0.0058
##    120        0.5931             nan     0.2000   -0.0024
##    140        0.6483             nan     0.2000   -0.0048
##    160   844566.6075             nan     0.2000   -0.0022
##    180   844566.5864             nan     0.2000   -0.0012
##    200   844566.5517             nan     0.2000   -0.0013
##    220   844566.5364             nan     0.2000   -0.0012
##    240   844566.5218             nan     0.2000   -0.0018
##    260   844566.5072             nan     0.2000   -0.0019
##    280   844566.4857             nan     0.2000   -0.0030
##    300   844566.4738             nan     0.2000   -0.0012
##    320   844566.4638             nan     0.2000   -0.0016
##    340   844566.4515             nan     0.2000   -0.0010
##    360   844566.4438             nan     0.2000   -0.0014
##    380   844566.4374             nan     0.2000   -0.0033
##    400   844566.4335             nan     0.2000   -0.0015
##    420   844566.4257             nan     0.2000   -0.0032
##    440   844566.4094             nan     0.2000   -0.0009
##    460   844566.3996             nan     0.2000   -0.0011
##    480   844566.3856             nan     0.2000    0.0000
##    500   844566.3718             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2046             nan     0.2000    0.0382
##      2        1.1405             nan     0.2000    0.0235
##      3        1.0969             nan     0.2000    0.0144
##      4        1.0616             nan     0.2000    0.0106
##      5        1.0317             nan     0.2000    0.0103
##      6        1.0072             nan     0.2000    0.0057
##      7        0.9869             nan     0.2000    0.0017
##      8        0.9639             nan     0.2000    0.0069
##      9        0.9448             nan     0.2000    0.0081
##     10        0.9352             nan     0.2000    0.0012
##     20        0.8420             nan     0.2000    0.0006
##     40        0.7601             nan     0.2000   -0.0018
##     60        0.7092             nan     0.2000   -0.0044
##     80        0.6720             nan     0.2000   -0.0019
##    100        0.6359             nan     0.2000   -0.0029
##    120        0.6002             nan     0.2000   -0.0024
##    140        0.5666             nan     0.2000   -0.0024
##    160        0.5440             nan     0.2000   -0.0023
##    180        0.5211             nan     0.2000   -0.0017
##    200        0.4947             nan     0.2000   -0.0015
##    220        0.4769             nan     0.2000   -0.0034
##    240        0.4574             nan     0.2000   -0.0007
##    260        0.4332             nan     0.2000   -0.0008
##    280        0.4142             nan     0.2000   -0.0012
##    300        0.3908             nan     0.2000   -0.0024
##    320        0.3757             nan     0.2000   -0.0009
##    340        0.3604             nan     0.2000   -0.0007
##    360        0.3433             nan     0.2000   -0.0012
##    380        0.3267             nan     0.2000   -0.0010
##    400        0.3155             nan     0.2000   -0.0006
##    420        0.3019             nan     0.2000   -0.0019
##    440        0.2927             nan     0.2000   -0.0017
##    460        0.2834             nan     0.2000   -0.0017
##    480        0.2720             nan     0.2000   -0.0013
##    500        0.2616             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2096             nan     0.2000    0.0383
##      2        1.1451             nan     0.2000    0.0277
##      3        1.0924             nan     0.2000    0.0229
##      4        1.0591             nan     0.2000    0.0116
##      5        1.0253             nan     0.2000    0.0112
##      6        1.0004             nan     0.2000    0.0063
##      7        0.9870             nan     0.2000    0.0001
##      8        0.9656             nan     0.2000    0.0080
##      9        0.9492             nan     0.2000    0.0016
##     10        0.9345             nan     0.2000    0.0025
##     20        0.8374             nan     0.2000   -0.0017
##     40        0.7613             nan     0.2000   -0.0015
##     60        0.7151             nan     0.2000   -0.0033
##     80        0.6689             nan     0.2000   -0.0020
##    100        0.6412             nan     0.2000   -0.0012
##    120        0.6100             nan     0.2000   -0.0011
##    140        0.5745             nan     0.2000   -0.0018
##    160        0.5471             nan     0.2000   -0.0017
##    180        0.5198             nan     0.2000   -0.0035
##    200        0.4932             nan     0.2000   -0.0013
##    220        0.4740             nan     0.2000   -0.0012
##    240        0.4568             nan     0.2000   -0.0017
##    260        0.4401             nan     0.2000   -0.0031
##    280        0.4187             nan     0.2000   -0.0002
##    300        0.4012             nan     0.2000   -0.0009
##    320        0.3847             nan     0.2000   -0.0018
##    340        0.3702             nan     0.2000   -0.0021
##    360        0.3566             nan     0.2000   -0.0019
##    380        0.3422             nan     0.2000   -0.0005
##    400        0.3272             nan     0.2000   -0.0011
##    420        0.3160             nan     0.2000   -0.0012
##    440        0.3041             nan     0.2000   -0.0004
##    460        0.2947             nan     0.2000   -0.0017
##    480        0.2827             nan     0.2000   -0.0016
##    500        0.2725             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1926             nan     0.2000    0.0413
##      2        1.1176             nan     0.2000    0.0348
##      3        1.0686             nan     0.2000    0.0165
##      4        1.0265             nan     0.2000    0.0111
##      5        0.9962             nan     0.2000    0.0059
##      6        0.9656             nan     0.2000    0.0076
##      7        0.9437             nan     0.2000    0.0057
##      8        0.9202             nan     0.2000    0.0067
##      9        0.9052             nan     0.2000    0.0027
##     10        0.8920             nan     0.2000   -0.0006
##     20        0.8053             nan     0.2000   -0.0027
##     40        0.6917             nan     0.2000   -0.0001
##     60        0.6081             nan     0.2000   -0.0009
##     80        0.5594             nan     0.2000   -0.0052
##    100        0.5124             nan     0.2000   -0.0018
##    120        0.4563             nan     0.2000   -0.0032
##    140        0.4214             nan     0.2000   -0.0018
##    160        0.3916             nan     0.2000   -0.0015
##    180        0.3548             nan     0.2000   -0.0002
##    200        0.3316             nan     0.2000   -0.0021
##    220        0.3055             nan     0.2000   -0.0020
##    240        0.2868             nan     0.2000   -0.0026
##    260        0.2654             nan     0.2000   -0.0002
##    280        0.2489             nan     0.2000   -0.0018
##    300        0.2344             nan     0.2000   -0.0003
##    320        0.2201             nan     0.2000   -0.0010
##    340        0.2069             nan     0.2000   -0.0016
##    360        0.1929             nan     0.2000   -0.0021
##    380        0.1792             nan     0.2000   -0.0011
##    400        0.1674             nan     0.2000   -0.0005
##    420        0.1577             nan     0.2000   -0.0006
##    440        0.1477             nan     0.2000   -0.0008
##    460        0.1398             nan     0.2000   -0.0008
##    480        0.1304             nan     0.2000   -0.0002
##    500        0.1228             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1946             nan     0.2000    0.0435
##      2        1.1095             nan     0.2000    0.0352
##      3        1.0553             nan     0.2000    0.0215
##      4        1.0170             nan     0.2000    0.0142
##      5        0.9829             nan     0.2000    0.0130
##      6        0.9637             nan     0.2000    0.0052
##      7        0.9470             nan     0.2000    0.0034
##      8        0.9237             nan     0.2000    0.0062
##      9        0.9142             nan     0.2000   -0.0024
##     10        0.8927             nan     0.2000    0.0065
##     20        0.7926             nan     0.2000   -0.0010
##     40        0.6882             nan     0.2000   -0.0000
##     60        0.6118             nan     0.2000   -0.0003
##     80        0.5609             nan     0.2000   -0.0034
##    100        0.5163             nan     0.2000   -0.0026
##    120        0.4749             nan     0.2000    0.0002
##    140        0.4331             nan     0.2000   -0.0028
##    160        0.3925             nan     0.2000   -0.0023
##    180        0.3599             nan     0.2000   -0.0012
##    200        0.3280             nan     0.2000   -0.0009
##    220        0.3013             nan     0.2000   -0.0017
##    240        0.2712             nan     0.2000   -0.0012
##    260        0.2507             nan     0.2000   -0.0011
##    280        0.2340             nan     0.2000   -0.0010
##    300        0.2158             nan     0.2000   -0.0011
##    320        0.1997             nan     0.2000   -0.0004
##    340        0.1868             nan     0.2000   -0.0007
##    360        0.1758             nan     0.2000   -0.0011
##    380        0.1642             nan     0.2000   -0.0011
##    400        0.1542             nan     0.2000   -0.0002
##    420        0.1461             nan     0.2000   -0.0001
##    440        0.1369             nan     0.2000   -0.0013
##    460        0.1294             nan     0.2000   -0.0001
##    480        0.1217             nan     0.2000   -0.0007
##    500        0.1143             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1984             nan     0.2000    0.0364
##      2        1.1199             nan     0.2000    0.0366
##      3        1.0626             nan     0.2000    0.0224
##      4        1.0215             nan     0.2000    0.0116
##      5        0.9913             nan     0.2000    0.0067
##      6        0.9593             nan     0.2000    0.0108
##      7        0.9355             nan     0.2000    0.0028
##      8        0.9174             nan     0.2000    0.0051
##      9        0.9017             nan     0.2000    0.0034
##     10        0.8881             nan     0.2000    0.0011
##     20        0.8000             nan     0.2000   -0.0002
##     40        0.6881             nan     0.2000   -0.0020
##     60        0.6137             nan     0.2000   -0.0035
##     80        0.5487             nan     0.2000   -0.0019
##    100        0.4980             nan     0.2000   -0.0036
##    120        0.4521             nan     0.2000   -0.0019
##    140        0.4170             nan     0.2000   -0.0028
##    160        0.3843             nan     0.2000   -0.0019
##    180        0.3531             nan     0.2000   -0.0012
##    200        0.3297             nan     0.2000   -0.0036
##    220        0.3034             nan     0.2000   -0.0012
##    240        0.2764             nan     0.2000   -0.0026
##    260        0.2646             nan     0.2000   -0.0015
##    280        0.2445             nan     0.2000   -0.0023
##    300        0.2274             nan     0.2000   -0.0008
##    320        0.2078             nan     0.2000   -0.0013
##    340        0.1947             nan     0.2000   -0.0007
##    360        0.1825             nan     0.2000   -0.0009
##    380        0.1711             nan     0.2000   -0.0013
##    400        0.1625             nan     0.2000   -0.0008
##    420        0.1532             nan     0.2000   -0.0009
##    440        0.1417             nan     0.2000   -0.0014
##    460        0.1321             nan     0.2000   -0.0015
##    480        0.1247             nan     0.2000   -0.0010
##    500        0.1161             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1914             nan     0.3000    0.0524
##      2        1.1401             nan     0.3000    0.0176
##      3        1.0940             nan     0.3000    0.0182
##      4        1.0611             nan     0.3000    0.0141
##      5        1.0228             nan     0.3000    0.0149
##      6        1.0026             nan     0.3000    0.0054
##      7        0.9924             nan     0.3000    0.0007
##      8        0.9777             nan     0.3000    0.0037
##      9        0.9639             nan     0.3000    0.0021
##     10        0.9447             nan     0.3000    0.0069
##     20        0.8665             nan     0.3000    0.0015
##     40        0.8027             nan     0.3000   -0.0010
##     60        0.7672             nan     0.3000   -0.0028
##     80        0.7436             nan     0.3000   -0.0024
##    100        0.7231             nan     0.3000   -0.0061
##    120        0.7065             nan     0.3000   -0.0019
##    140        0.6925             nan     0.3000   -0.0020
##    160        0.6836             nan     0.3000    0.0004
##    180        0.6737             nan     0.3000   -0.0020
##    200        0.6699             nan     0.3000   -0.0028
##    220        0.6612             nan     0.3000   -0.0026
##    240        0.6511             nan     0.3000   -0.0028
##    260        0.6397             nan     0.3000   -0.0017
##    280        0.6313             nan     0.3000   -0.0025
##    300        0.6242             nan     0.3000   -0.0020
##    320        0.6208             nan     0.3000   -0.0029
##    340        0.6134             nan     0.3000   -0.0034
##    360        0.6036             nan     0.3000   -0.0021
##    380        0.6006             nan     0.3000   -0.0034
##    400        0.5954             nan     0.3000   -0.0040
##    420        0.5848             nan     0.3000   -0.0043
##    440        0.5796             nan     0.3000   -0.0042
##    460        0.5732             nan     0.3000   -0.0026
##    480        0.5724             nan     0.3000   -0.0028
##    500        0.5662             nan     0.3000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1824             nan     0.3000    0.0475
##      2        1.1284             nan     0.3000    0.0249
##      3        1.0851             nan     0.3000    0.0151
##      4        1.0528             nan     0.3000    0.0100
##      5        1.0312             nan     0.3000    0.0042
##      6        1.0052             nan     0.3000    0.0101
##      7        0.9776             nan     0.3000    0.0132
##      8        0.9635             nan     0.3000    0.0001
##      9        0.9517             nan     0.3000    0.0009
##     10        0.9364             nan     0.3000    0.0024
##     20        0.8620             nan     0.3000    0.0024
##     40        0.8017             nan     0.3000   -0.0019
##     60        0.7710             nan     0.3000   -0.0051
##     80        0.7452             nan     0.3000   -0.0035
##    100        0.7303             nan     0.3000   -0.0003
##    120        0.7173             nan     0.3000   -0.0004
##    140        0.7033             nan     0.3000   -0.0041
##    160        0.6882             nan     0.3000   -0.0024
##    180        0.6742             nan     0.3000   -0.0035
##    200        0.6662             nan     0.3000   -0.0028
##    220        0.6549             nan     0.3000   -0.0016
##    240        0.6436             nan     0.3000   -0.0025
##    260        0.6379             nan     0.3000   -0.0013
##    280        0.6360             nan     0.3000   -0.0056
##    300        0.6239             nan     0.3000   -0.0015
##    320        0.6189             nan     0.3000   -0.0007
##    340        0.6080             nan     0.3000   -0.0047
##    360        0.5994             nan     0.3000   -0.0025
##    380        0.5939             nan     0.3000   -0.0015
##    400        0.5857             nan     0.3000   -0.0032
##    420        0.5814             nan     0.3000   -0.0029
##    440        0.5801             nan     0.3000   -0.0038
##    460        0.5691             nan     0.3000   -0.0027
##    480        0.5637             nan     0.3000   -0.0025
##    500        0.5592             nan     0.3000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1798             nan     0.3000    0.0486
##      2        1.1220             nan     0.3000    0.0258
##      3        1.0818             nan     0.3000    0.0165
##      4        1.0503             nan     0.3000    0.0142
##      5        1.0226             nan     0.3000    0.0082
##      6        1.0053             nan     0.3000    0.0034
##      7        0.9881             nan     0.3000    0.0079
##      8        0.9676             nan     0.3000    0.0079
##      9        0.9562             nan     0.3000   -0.0028
##     10        0.9444             nan     0.3000    0.0028
##     20        0.8704             nan     0.3000   -0.0014
##     40        0.8123             nan     0.3000   -0.0036
##     60        0.7698             nan     0.3000   -0.0005
##     80        0.7487             nan     0.3000   -0.0008
##    100        0.7308             nan     0.3000   -0.0007
##    120        0.7118             nan     0.3000   -0.0015
##    140        0.6996             nan     0.3000   -0.0015
##    160        0.6859             nan     0.3000   -0.0029
##    180        0.6693             nan     0.3000   -0.0024
##    200        0.6628             nan     0.3000   -0.0031
##    220        0.6529             nan     0.3000   -0.0009
##    240        0.6449             nan     0.3000   -0.0029
##    260        0.6336             nan     0.3000   -0.0009
##    280        0.6264             nan     0.3000   -0.0019
##    300        0.6245             nan     0.3000   -0.0081
##    320        0.6140             nan     0.3000   -0.0009
##    340        0.6088             nan     0.3000   -0.0044
##    360        0.6036             nan     0.3000   -0.0016
##    380        0.6008             nan     0.3000   -0.0021
##    400        0.5924             nan     0.3000   -0.0005
##    420        0.5906             nan     0.3000   -0.0046
##    440        0.5873             nan     0.3000   -0.0015
##    460        0.5825             nan     0.3000   -0.0037
##    480        0.5805             nan     0.3000   -0.0011
##    500        0.5746             nan     0.3000   -0.0041
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1659             nan     0.3000    0.0526
##      2        1.0888             nan     0.3000    0.0306
##      3        1.0276             nan     0.3000    0.0155
##      4        0.9945             nan     0.3000    0.0091
##      5        0.9704             nan     0.3000    0.0059
##      6        0.9412             nan     0.3000    0.0087
##      7        0.9230             nan     0.3000    0.0068
##      8        0.9083             nan     0.3000   -0.0010
##      9        0.8947             nan     0.3000    0.0030
##     10        0.8771             nan     0.3000    0.0017
##     20        0.8000             nan     0.3000   -0.0067
##     40        0.7083             nan     0.3000   -0.0014
##     60        0.6590             nan     0.3000   -0.0059
##     80        0.6082             nan     0.3000   -0.0045
##    100        0.5625             nan     0.3000   -0.0040
##    120        0.5231             nan     0.3000   -0.0044
##    140        0.4843             nan     0.3000   -0.0019
##    160        0.4483             nan     0.3000   -0.0036
##    180        0.4135             nan     0.3000   -0.0025
##    200        0.3862             nan     0.3000   -0.0033
##    220        0.3542             nan     0.3000   -0.0001
##    240        0.3329             nan     0.3000   -0.0044
##    260        0.3166             nan     0.3000   -0.0032
##    280        0.2962             nan     0.3000   -0.0012
##    300        0.2782             nan     0.3000   -0.0004
##    320        0.2662             nan     0.3000   -0.0030
##    340        0.2483             nan     0.3000   -0.0020
##    360        0.2369             nan     0.3000   -0.0009
##    380        0.2235             nan     0.3000    0.0003
##    400        0.2116             nan     0.3000   -0.0011
##    420        0.2016             nan     0.3000   -0.0033
##    440        0.1907             nan     0.3000   -0.0025
##    460        0.1834             nan     0.3000   -0.0021
##    480        0.1740             nan     0.3000   -0.0013
##    500        0.1639             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1697             nan     0.3000    0.0586
##      2        1.1088             nan     0.3000    0.0233
##      3        1.0549             nan     0.3000    0.0256
##      4        1.0151             nan     0.3000    0.0133
##      5        0.9822             nan     0.3000    0.0068
##      6        0.9576             nan     0.3000    0.0059
##      7        0.9448             nan     0.3000   -0.0022
##      8        0.9271             nan     0.3000    0.0019
##      9        0.9102             nan     0.3000    0.0014
##     10        0.8917             nan     0.3000    0.0045
##     20        0.8127             nan     0.3000   -0.0019
##     40        0.7135             nan     0.3000    0.0002
##     60        0.6645             nan     0.3000   -0.0036
##     80        0.6051             nan     0.3000   -0.0013
##    100        0.5601             nan     0.3000   -0.0021
##    120        0.5053             nan     0.3000   -0.0040
##    140        0.4710             nan     0.3000   -0.0048
##    160        0.4433             nan     0.3000   -0.0022
##    180        0.4127             nan     0.3000   -0.0027
##    200        0.3877             nan     0.3000   -0.0036
##    220        0.3653             nan     0.3000   -0.0035
##    240        0.3417             nan     0.3000   -0.0018
##    260        0.3230             nan     0.3000   -0.0009
##    280        0.3089             nan     0.3000   -0.0020
##    300        0.2885             nan     0.3000   -0.0033
##    320        0.2699             nan     0.3000   -0.0026
##    340        0.2559             nan     0.3000   -0.0018
##    360        0.2454             nan     0.3000   -0.0024
##    380        0.2309             nan     0.3000   -0.0010
##    400        0.2210             nan     0.3000   -0.0024
##    420        0.2098             nan     0.3000   -0.0015
##    440        0.1979             nan     0.3000   -0.0007
##    460        0.1855             nan     0.3000   -0.0005
##    480        0.1771             nan     0.3000   -0.0006
##    500        0.1671             nan     0.3000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1692             nan     0.3000    0.0560
##      2        1.0893             nan     0.3000    0.0318
##      3        1.0356             nan     0.3000    0.0223
##      4        1.0030             nan     0.3000    0.0105
##      5        0.9720             nan     0.3000   -0.0012
##      6        0.9483             nan     0.3000    0.0056
##      7        0.9230             nan     0.3000    0.0047
##      8        0.9056             nan     0.3000    0.0042
##      9        0.8893             nan     0.3000    0.0013
##     10        0.8791             nan     0.3000    0.0008
##     20        0.7965             nan     0.3000   -0.0027
##     40        0.7196             nan     0.3000   -0.0020
##     60        0.6528             nan     0.3000   -0.0006
##     80        0.6005             nan     0.3000   -0.0021
##    100        0.5595             nan     0.3000   -0.0033
##    120        0.5360             nan     0.3000   -0.0014
##    140        0.5089             nan     0.3000   -0.0021
##    160        0.4684             nan     0.3000   -0.0004
##    180        0.4293             nan     0.3000   -0.0011
##    200        0.4154             nan     0.3000   -0.0055
##    220        0.3900             nan     0.3000   -0.0064
##    240        0.3660             nan     0.3000   -0.0023
##    260        0.3447             nan     0.3000   -0.0018
##    280        0.3185             nan     0.3000   -0.0026
##    300        0.2993             nan     0.3000   -0.0039
##    320        0.2773             nan     0.3000   -0.0041
##    340        0.2603             nan     0.3000   -0.0030
##    360        0.2439             nan     0.3000   -0.0014
##    380        0.2327             nan     0.3000   -0.0021
##    400        0.2183             nan     0.3000   -0.0020
##    420        0.2070             nan     0.3000   -0.0018
##    440        0.1989             nan     0.3000   -0.0017
##    460        0.1862             nan     0.3000   -0.0010
##    480        0.1758             nan     0.3000   -0.0009
##    500        0.1676             nan     0.3000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1512             nan     0.3000    0.0562
##      2        1.0646             nan     0.3000    0.0375
##      3        1.0040             nan     0.3000    0.0211
##      4        0.9555             nan     0.3000    0.0200
##      5        0.9329             nan     0.3000   -0.0027
##      6        0.9129             nan     0.3000    0.0014
##      7        0.8895             nan     0.3000    0.0051
##      8        0.8727             nan     0.3000   -0.0022
##      9        0.8536             nan     0.3000   -0.0021
##     10        0.8412             nan     0.3000   -0.0022
##     20        0.7440             nan     0.3000   -0.0045
##     40        0.6321             nan     0.3000   -0.0087
##     60        0.5490             nan     0.3000   -0.0027
##     80        0.4845             nan     0.3000   -0.0007
##    100        0.4319             nan     0.3000   -0.0005
##    120        0.3833             nan     0.3000   -0.0044
##    140        0.3386             nan     0.3000   -0.0031
##    160        0.3049             nan     0.3000   -0.0021
##    180 18440999.8231             nan     0.3000   -0.0008
##    200           inf             nan     0.3000       nan
##    220           inf             nan     0.3000       nan
##    240           inf             nan     0.3000       nan
##    260           inf             nan     0.3000       nan
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000   -0.0006
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1470             nan     0.3000    0.0628
##      2        1.0531             nan     0.3000    0.0436
##      3        0.9951             nan     0.3000    0.0223
##      4        0.9598             nan     0.3000    0.0117
##      5        0.9289             nan     0.3000    0.0099
##      6        0.9072             nan     0.3000    0.0026
##      7        0.8788             nan     0.3000    0.0051
##      8        0.8541             nan     0.3000    0.0045
##      9        0.8390             nan     0.3000    0.0021
##     10        0.8291             nan     0.3000   -0.0051
##     20        0.7302             nan     0.3000   -0.0034
##     40        0.6247             nan     0.3000   -0.0076
##     60        0.5590             nan     0.3000   -0.0058
##     80        0.4830             nan     0.3000   -0.0010
##    100        0.4310             nan     0.3000   -0.0044
##    120        0.3718             nan     0.3000   -0.0010
##    140        0.3243             nan     0.3000   -0.0027
##    160        0.2799             nan     0.3000   -0.0067
##    180        0.2588             nan     0.3000   -0.0034
##    200        0.2260             nan     0.3000   -0.0018
##    220        0.2020             nan     0.3000   -0.0020
##    240        0.1865             nan     0.3000   -0.0009
##    260        0.1689             nan     0.3000   -0.0020
##    280        0.1563             nan     0.3000   -0.0020
##    300        0.1450             nan     0.3000   -0.0020
##    320        0.1290             nan     0.3000   -0.0012
##    340        0.1223             nan     0.3000   -0.0015
##    360        0.1087             nan     0.3000   -0.0004
##    380        0.1006             nan     0.3000   -0.0003
##    400        0.0922             nan     0.3000   -0.0010
##    420        0.0846             nan     0.3000   -0.0007
##    440        0.0770             nan     0.3000   -0.0002
##    460        0.0703             nan     0.3000   -0.0003
##    480        0.0647             nan     0.3000   -0.0002
##    500        0.0605             nan     0.3000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1443             nan     0.3000    0.0584
##      2        1.0642             nan     0.3000    0.0380
##      3        1.0077             nan     0.3000    0.0252
##      4        0.9774             nan     0.3000    0.0068
##      5        0.9385             nan     0.3000    0.0061
##      6        0.9138             nan     0.3000    0.0010
##      7        0.8843             nan     0.3000    0.0060
##      8        0.8669             nan     0.3000    0.0001
##      9        0.8516             nan     0.3000   -0.0016
##     10        0.8387             nan     0.3000    0.0020
##     20        0.7643             nan     0.3000   -0.0055
##     40        0.6463             nan     0.3000   -0.0021
##     60        0.5617             nan     0.3000   -0.0017
##     80        0.4965             nan     0.3000   -0.0039
##    100        0.4524             nan     0.3000   -0.0038
##    120        0.3964             nan     0.3000   -0.0046
##    140        0.3492             nan     0.3000   -0.0022
##    160        0.3129             nan     0.3000   -0.0023
##    180        0.2729             nan     0.3000   -0.0004
##    200        0.2457             nan     0.3000   -0.0025
##    220        0.2253             nan     0.3000   -0.0010
##    240        0.2043             nan     0.3000   -0.0018
##    260        0.1804             nan     0.3000   -0.0012
##    280        0.1658             nan     0.3000   -0.0014
##    300        0.1480             nan     0.3000   -0.0012
##    320        0.1343             nan     0.3000   -0.0003
##    340        0.1265             nan     0.3000   -0.0010
##    360        0.1172             nan     0.3000   -0.0014
##    380        0.1074             nan     0.3000   -0.0008
##    400        0.0973             nan     0.3000   -0.0009
##    420        0.0887             nan     0.3000   -0.0007
##    440        0.0817             nan     0.3000   -0.0005
##    460        0.0754             nan     0.3000   -0.0009
##    480        0.0690             nan     0.3000   -0.0009
##    500        0.0636             nan     0.3000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1381             nan     0.5000    0.0557
##      2        1.0834             nan     0.5000    0.0241
##      3        1.0282             nan     0.5000    0.0249
##      4        0.9849             nan     0.5000    0.0196
##      5        0.9659             nan     0.5000    0.0055
##      6        0.9502             nan     0.5000   -0.0001
##      7        0.9398             nan     0.5000   -0.0060
##      8        0.9233             nan     0.5000    0.0007
##      9        0.9172             nan     0.5000   -0.0034
##     10        0.9115             nan     0.5000   -0.0011
##     20        0.8443             nan     0.5000   -0.0064
##     40        0.7825             nan     0.5000   -0.0049
##     60        0.7473             nan     0.5000   -0.0016
##     80        0.7257             nan     0.5000   -0.0077
##    100        0.7021             nan     0.5000   -0.0103
##    120        0.6795             nan     0.5000   -0.0029
##    140        0.6677             nan     0.5000   -0.0012
##    160        0.6577             nan     0.5000   -0.0052
##    180        0.6403             nan     0.5000   -0.0085
##    200        0.6340             nan     0.5000   -0.0063
##    220        0.6229             nan     0.5000   -0.0054
##    240        0.6093             nan     0.5000   -0.0065
##    260        0.5927             nan     0.5000   -0.0036
##    280        0.5812             nan     0.5000   -0.0087
##    300        0.5721             nan     0.5000   -0.0054
##    320        0.5724             nan     0.5000   -0.0060
##    340        0.5651             nan     0.5000   -0.0058
##    360        0.5753             nan     0.5000   -0.0071
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1432             nan     0.5000    0.0691
##      2        1.0747             nan     0.5000    0.0254
##      3        1.0234             nan     0.5000    0.0153
##      4        0.9910             nan     0.5000    0.0127
##      5        0.9783             nan     0.5000   -0.0011
##      6        0.9627             nan     0.5000    0.0047
##      7        0.9540             nan     0.5000   -0.0011
##      8        0.9370             nan     0.5000    0.0049
##      9        0.9122             nan     0.5000    0.0078
##     10        0.8982             nan     0.5000   -0.0016
##     20        0.8277             nan     0.5000    0.0009
##     40        0.7788             nan     0.5000   -0.0043
##     60        0.7508             nan     0.5000    0.0008
##     80        0.7368             nan     0.5000   -0.0086
##    100        0.7130             nan     0.5000   -0.0059
##    120        0.6822             nan     0.5000   -0.0065
##    140        0.6783             nan     0.5000   -0.0060
##    160        0.6529             nan     0.5000   -0.0001
##    180        0.6388             nan     0.5000   -0.0051
##    200        0.6205             nan     0.5000   -0.0034
##    220        0.6112             nan     0.5000   -0.0052
##    240        0.6013             nan     0.5000   -0.0064
##    260        0.5946             nan     0.5000   -0.0047
##    280        0.5808             nan     0.5000   -0.0068
##    300        0.5750             nan     0.5000   -0.0060
##    320        0.5702             nan     0.5000   -0.0048
##    340        0.5549             nan     0.5000   -0.0073
##    360        0.5431             nan     0.5000   -0.0082
##    380        0.5288             nan     0.5000   -0.0054
##    400        0.5228             nan     0.5000   -0.0028
##    420       35.0230             nan     0.5000   -0.0062
##    440       35.0203             nan     0.5000   -0.0017
##    460       35.0113             nan     0.5000    0.0000
##    480       35.0078             nan     0.5000   -0.0016
##    500       34.9963             nan     0.5000    0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1454             nan     0.5000    0.0619
##      2        1.0708             nan     0.5000    0.0175
##      3        1.0190             nan     0.5000    0.0206
##      4        0.9994             nan     0.5000    0.0024
##      5        0.9594             nan     0.5000    0.0145
##      6        0.9452             nan     0.5000    0.0039
##      7        0.9345             nan     0.5000   -0.0007
##      8        0.9264             nan     0.5000    0.0017
##      9        0.9090             nan     0.5000    0.0056
##     10        0.8964             nan     0.5000    0.0029
##     20        0.8568             nan     0.5000   -0.0251
##     40        0.8005             nan     0.5000   -0.0044
##     60        0.7573             nan     0.5000    0.0020
##     80        0.7207             nan     0.5000   -0.0043
##    100        0.7094             nan     0.5000   -0.0067
##    120        0.6798             nan     0.5000   -0.0055
##    140        0.6578             nan     0.5000   -0.0074
##    160        0.6476             nan     0.5000   -0.0028
##    180        0.6381             nan     0.5000   -0.0045
##    200        0.6279             nan     0.5000   -0.0008
##    220        0.6093             nan     0.5000   -0.0041
##    240        0.6003             nan     0.5000   -0.0009
##    260        0.5876             nan     0.5000   -0.0038
##    280        0.5848             nan     0.5000   -0.0077
##    300        0.5772             nan     0.5000   -0.0071
##    320        0.5690             nan     0.5000   -0.0076
##    340        0.5577             nan     0.5000   -0.0042
##    360        0.5590             nan     0.5000   -0.0046
##    380        0.5417             nan     0.5000   -0.0033
##    400        0.5361             nan     0.5000   -0.0046
##    420        0.5310             nan     0.5000   -0.0027
##    440        0.5200             nan     0.5000   -0.0060
##    460        0.5118             nan     0.5000   -0.0012
##    480        0.5044             nan     0.5000   -0.0048
##    500        0.4994             nan     0.5000   -0.0028
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1067             nan     0.5000    0.0826
##      2        1.0124             nan     0.5000    0.0456
##      3        0.9616             nan     0.5000    0.0143
##      4        0.9357             nan     0.5000    0.0006
##      5        0.9220             nan     0.5000   -0.0026
##      6        0.9052             nan     0.5000   -0.0112
##      7        0.8745             nan     0.5000    0.0082
##      8        0.8741             nan     0.5000   -0.0168
##      9        0.8595             nan     0.5000   -0.0005
##     10        0.8527             nan     0.5000   -0.0069
##     20        0.7604             nan     0.5000   -0.0019
##     40        0.6525             nan     0.5000   -0.0132
##     60 3258755157287.3828             nan     0.5000   -0.0117
##     80 3258755157287.2988             nan     0.5000   -0.0033
##    100 3258755172485.5679             nan     0.5000   -0.0040
##    120 3258755172485.5469             nan     0.5000   -0.0074
##    140 3258755172485.5361             nan     0.5000    0.0036
##    160 3258755172485.5327             nan     0.5000   -0.0000
##    180 3258755172485.5244             nan     0.5000   -0.0004
##    200 3258755172485.5225             nan     0.5000   -0.0017
##    220 3258755172485.5215             nan     0.5000    0.0006
##    240 3258755172485.5190             nan     0.5000   -0.0017
##    260 3258755172485.5059             nan     0.5000    0.0001
##    280 3258755172485.5049             nan     0.5000   -0.0021
##    300 3258755172485.4941             nan     0.5000   -0.0031
##    320 3258755172485.4951             nan     0.5000   -0.0002
##    340 3258755172485.5000             nan     0.5000   -0.0047
##    360 3258755172485.4980             nan     0.5000   -0.0033
##    380 3258755172485.4971             nan     0.5000   -0.0000
##    400 3258755172485.4775             nan     0.5000   -0.0017
##    420 3258755172485.4731             nan     0.5000   -0.0006
##    440 3258755172485.4644             nan     0.5000    0.0001
##    460 3258755172485.4702             nan     0.5000   -0.0008
##    480 3258755172485.4629             nan     0.5000   -0.0029
##    500 3258755172485.4487             nan     0.5000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1339             nan     0.5000    0.0780
##      2        1.0322             nan     0.5000    0.0349
##      3        0.9860             nan     0.5000    0.0166
##      4        0.9712             nan     0.5000   -0.0112
##      5        0.9422             nan     0.5000    0.0046
##      6        0.9087             nan     0.5000    0.0132
##      7        0.8959             nan     0.5000   -0.0057
##      8        0.8799             nan     0.5000   -0.0026
##      9        0.8535             nan     0.5000    0.0017
##     10        0.8455             nan     0.5000   -0.0090
##     20        0.7926             nan     0.5000   -0.0048
##     40           inf             nan     0.5000       nan
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000   -0.0034
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1167             nan     0.5000    0.0815
##      2        1.0270             nan     0.5000    0.0373
##      3        0.9668             nan     0.5000    0.0238
##      4        0.9292             nan     0.5000    0.0058
##      5        0.9081             nan     0.5000    0.0011
##      6        0.8933             nan     0.5000   -0.0031
##      7        0.8789             nan     0.5000    0.0009
##      8        0.8643             nan     0.5000   -0.0023
##      9        0.8543             nan     0.5000   -0.0011
##     10        0.8534             nan     0.5000   -0.0130
##     20        0.7635             nan     0.5000   -0.0035
##     40        0.6672             nan     0.5000   -0.0011
##     60        0.5930             nan     0.5000   -0.0119
##     80        0.5317             nan     0.5000   -0.0077
##    100        0.4676             nan     0.5000   -0.0011
##    120        0.4262             nan     0.5000   -0.0067
##    140        0.3700             nan     0.5000   -0.0023
##    160        0.3498             nan     0.5000   -0.0065
##    180        0.3115             nan     0.5000   -0.0036
##    200        0.2989             nan     0.5000   -0.0102
##    220        0.2715             nan     0.5000   -0.0052
##    240        0.2397             nan     0.5000   -0.0057
##    260        0.2159             nan     0.5000   -0.0024
##    280        0.1920             nan     0.5000   -0.0010
##    300        0.1679             nan     0.5000   -0.0014
##    320        0.1513             nan     0.5000   -0.0026
##    340        0.1362             nan     0.5000   -0.0023
##    360        0.1267             nan     0.5000   -0.0016
##    380        0.1190             nan     0.5000   -0.0007
##    400        0.1125             nan     0.5000   -0.0017
##    420        0.1018             nan     0.5000   -0.0011
##    440        0.0950             nan     0.5000   -0.0006
##    460        0.0859             nan     0.5000    0.0000
##    480        0.0788             nan     0.5000   -0.0003
##    500        0.0742             nan     0.5000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1008             nan     0.5000    0.0847
##      2        1.0128             nan     0.5000    0.0393
##      3        0.9589             nan     0.5000    0.0221
##      4        0.9412             nan     0.5000   -0.0094
##      5        0.9113             nan     0.5000   -0.0050
##      6        0.8902             nan     0.5000   -0.0088
##      7        0.8749             nan     0.5000   -0.0034
##      8        0.8463             nan     0.5000    0.0026
##      9        0.8293             nan     0.5000   -0.0067
##     10        0.8144             nan     0.5000   -0.0038
##     20        0.6973             nan     0.5000    0.0070
##     40           inf             nan     0.5000       nan
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0894             nan     0.5000    0.1006
##      2        0.9994             nan     0.5000    0.0402
##      3        0.9547             nan     0.5000    0.0108
##      4        0.9140             nan     0.5000    0.0026
##      5        0.8704             nan     0.5000    0.0136
##      6        0.8538             nan     0.5000   -0.0064
##      7        0.8408             nan     0.5000   -0.0031
##      8        0.8185             nan     0.5000    0.0020
##      9        0.8038             nan     0.5000   -0.0045
##     10        0.7876             nan     0.5000    0.0024
##     20        0.7093             nan     0.5000   -0.0156
##     40        0.6109             nan     0.5000   -0.0318
##     60           inf             nan     0.5000      -inf
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0741             nan     0.5000    0.0830
##      2        0.9673             nan     0.5000    0.0309
##      3        0.9210             nan     0.5000    0.0094
##      4        0.8860             nan     0.5000   -0.0014
##      5        0.8597             nan     0.5000   -0.0061
##      6        0.8339             nan     0.5000    0.0016
##      7        0.8140             nan     0.5000   -0.0045
##      8        0.7953             nan     0.5000   -0.0069
##      9        0.7893             nan     0.5000   -0.0144
##     10        0.7762             nan     0.5000   -0.0097
##     20        0.6945             nan     0.5000   -0.0209
##     40        0.5814             nan     0.5000   -0.0084
##     60        0.4997             nan     0.5000   -0.0112
##     80        0.4048             nan     0.5000   -0.0039
##    100        0.3415             nan     0.5000   -0.0074
##    120        0.2707             nan     0.5000   -0.0022
##    140        0.2280             nan     0.5000   -0.0012
##    160        0.1891             nan     0.5000   -0.0018
##    180        0.1574             nan     0.5000   -0.0014
##    200        0.1301             nan     0.5000    0.0001
##    220        0.1125             nan     0.5000   -0.0016
##    240        0.0980             nan     0.5000   -0.0023
##    260        0.0851             nan     0.5000   -0.0012
##    280        0.0756             nan     0.5000   -0.0005
##    300        0.0645             nan     0.5000   -0.0008
##    320        0.0581             nan     0.5000   -0.0009
##    340        0.0523             nan     0.5000   -0.0003
##    360        0.0459             nan     0.5000   -0.0008
##    380        0.0394             nan     0.5000   -0.0005
##    400        0.0362             nan     0.5000   -0.0007
##    420        0.0316             nan     0.5000   -0.0002
##    440        0.0280             nan     0.5000   -0.0010
##    460        0.0242             nan     0.5000   -0.0005
##    480        0.0218             nan     0.5000   -0.0003
##    500        0.0203             nan     0.5000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1159             nan     1.0000    0.0756
##      2        1.0469             nan     1.0000    0.0225
##      3        0.9903             nan     1.0000    0.0237
##      4        0.9406             nan     1.0000    0.0242
##      5        0.9317             nan     1.0000   -0.0127
##      6        0.9155             nan     1.0000    0.0019
##      7        0.9086             nan     1.0000   -0.0049
##      8        0.8997             nan     1.0000   -0.0086
##      9        0.8917             nan     1.0000   -0.0034
##     10        0.8909             nan     1.0000   -0.0104
##     20     2101.9198             nan     1.0000 -2098.0314
##     40     2101.8575             nan     1.0000   -0.0076
##     60     2103.0130             nan     1.0000   -0.0005
##     80     2102.9515             nan     1.0000   -0.0255
##    100     2102.9348             nan     1.0000   -0.0018
##    120  7177454.2512             nan     1.0000    0.0000
##    140  7177454.2578             nan     1.0000   -0.0017
##    160  7177454.2440             nan     1.0000    0.0002
##    180 3356926643719341586884604862.0000             nan     1.0000 -3352075593656163168868428206.0000
##    200 3356926643719341586884604862.0000             nan     1.0000   -0.0003
##    220 3356926643719341586884604862.0000             nan     1.0000   -0.0001
##    240 3356926643719341586884604862.0000             nan     1.0000    0.0004
##    260 3356926643719341586884604862.0000             nan     1.0000   -0.0014
##    280 3356926643719341586884604862.0000             nan     1.0000   -0.0038
##    300 3356926643719341586884604862.0000             nan     1.0000   -0.0038
##    320 3356926643719341586884604862.0000             nan     1.0000   -0.0558
##    340 3356926643719341586884604862.0000             nan     1.0000    0.0003
##    360 3356926643719341586884604862.0000             nan     1.0000   -0.0035
##    380 3356926643719341586884604862.0000             nan     1.0000   -0.0029
##    400 3356926643719341586884604862.0000             nan     1.0000   -0.0017
##    420 3356926643719341586884604862.0000             nan     1.0000   -0.0041
##    440 3356926643719341586884604862.0000             nan     1.0000    0.0003
##    460 3356926643719341586884604862.0000             nan     1.0000   -0.0021
##    480 3356926643719341586884604862.0000             nan     1.0000    0.0037
##    500 3356926643719341586884604862.0000             nan     1.0000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1167             nan     1.0000    0.0791
##      2        1.0536             nan     1.0000    0.0162
##      3        0.9981             nan     1.0000    0.0165
##      4        0.9559             nan     1.0000    0.0209
##      5        0.9492             nan     1.0000   -0.0194
##      6        0.9573             nan     1.0000   -0.0287
##      7        0.9560             nan     1.0000   -0.0194
##      8        0.9696             nan     1.0000   -0.0422
##      9        0.9507             nan     1.0000   -0.0014
##     10        0.9603             nan     1.0000   -0.0399
##     20        0.9112             nan     1.0000   -0.0346
##     40        1.4919             nan     1.0000    0.0074
##     60        1.4667             nan     1.0000   -0.0266
##     80        1.5041             nan     1.0000   -0.0179
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1110             nan     1.0000    0.0726
##      2        1.0440             nan     1.0000    0.0194
##      3        1.0140             nan     1.0000   -0.0046
##      4        0.9556             nan     1.0000    0.0169
##      5        0.9491             nan     1.0000   -0.0146
##      6        0.9504             nan     1.0000   -0.0180
##      7        0.9172             nan     1.0000    0.0167
##      8        0.9167             nan     1.0000   -0.0150
##      9        0.9105             nan     1.0000   -0.0105
##     10        0.9030             nan     1.0000   -0.0025
##     20        0.9197             nan     1.0000   -0.0470
##     40        0.8185             nan     1.0000   -0.0306
##     60        0.8111             nan     1.0000   -0.0093
##     80        0.7839             nan     1.0000   -0.0183
##    100        0.8366             nan     1.0000   -0.0352
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0577             nan     1.0000    0.0828
##      2        0.9858             nan     1.0000    0.0178
##      3        0.9451             nan     1.0000    0.0080
##      4        0.9328             nan     1.0000   -0.0139
##      5        0.9435             nan     1.0000   -0.0393
##      6        0.9202             nan     1.0000   -0.0071
##      7        0.9047             nan     1.0000   -0.0063
##      8        0.8843             nan     1.0000   -0.0171
##      9        0.8883             nan     1.0000   -0.0331
##     10        0.9291             nan     1.0000   -0.0857
##     20        0.9084             nan     1.0000   -0.0358
##     40        0.8070             nan     1.0000   -0.0162
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000    0.0000
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0512             nan     1.0000    0.0883
##      2        0.9844             nan     1.0000    0.0128
##      3        0.9731             nan     1.0000   -0.0327
##      4        0.9483             nan     1.0000   -0.0140
##      5        0.9356             nan     1.0000   -0.0272
##      6        0.9155             nan     1.0000   -0.0168
##      7        0.8799             nan     1.0000    0.0083
##      8        0.8878             nan     1.0000   -0.0423
##      9        0.8766             nan     1.0000   -0.0066
##     10        0.8629             nan     1.0000   -0.0143
##     20        0.9221             nan     1.0000   -0.0393
##     40        0.9332             nan     1.0000   -0.0781
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0579             nan     1.0000    0.1052
##      2        0.9751             nan     1.0000    0.0312
##      3        0.9490             nan     1.0000   -0.0102
##      4        0.9466             nan     1.0000   -0.0368
##      5        0.9360             nan     1.0000   -0.0340
##      6        0.9540             nan     1.0000   -0.0557
##      7        0.9075             nan     1.0000    0.0047
##      8        0.9053             nan     1.0000   -0.0460
##      9        0.8918             nan     1.0000   -0.0096
##     10        0.9113             nan     1.0000   -0.0539
##     20        2.2372             nan     1.0000   -0.0153
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000   -0.0555
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0481             nan     1.0000    0.0802
##      2        0.9871             nan     1.0000   -0.0170
##      3        0.9653             nan     1.0000   -0.0241
##      4        0.9266             nan     1.0000   -0.0060
##      5        0.8726             nan     1.0000   -0.0164
##      6        0.9266             nan     1.0000   -0.1141
##      7        0.8383             nan     1.0000    0.0204
##      8        0.8226             nan     1.0000   -0.0228
##      9        0.9830             nan     1.0000   -0.1941
##     10        1.0042             nan     1.0000   -0.0585
##     20        1.3105             nan     1.0000   -0.1265
##     40        2.2336             nan     1.0000   -0.0402
##     60 156595565351557801078280404822288682422642208844686806222468262440282804600666042282846042062824446480848648242602422800820468000244608440064408428288488246646026246080080426626282022402868020880866628060684282466866248260404628.0000             nan     1.0000       inf
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0344             nan     1.0000    0.1226
##      2        0.9879             nan     1.0000   -0.0199
##      3        0.9722             nan     1.0000   -0.0209
##      4        0.9815             nan     1.0000   -0.0550
##      5        0.9826             nan     1.0000   -0.0360
##      6        0.9605             nan     1.0000   -0.0138
##      7        0.9535             nan     1.0000   -0.0442
##      8        0.9027             nan     1.0000   -0.0082
##      9        0.8845             nan     1.0000   -0.0149
##     10        0.8866             nan     1.0000   -0.0445
##     20        0.7629             nan     1.0000   -0.0667
##     40        0.5829             nan     1.0000   -0.0488
##     60 12624308.1751             nan     1.0000   -0.0228
##     80 12624316.1764             nan     1.0000   -0.0339
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0423             nan     1.0000    0.1088
##      2        0.9598             nan     1.0000    0.0227
##      3        0.9244             nan     1.0000   -0.0180
##      4        0.9104             nan     1.0000   -0.0226
##      5        0.9100             nan     1.0000   -0.0322
##      6        0.8944             nan     1.0000   -0.0258
##      7        0.8875             nan     1.0000   -0.0420
##      8        0.8936             nan     1.0000   -0.0539
##      9        0.8811             nan     1.0000   -0.0255
##     10        0.8777             nan     1.0000   -0.0422
##     20       16.4256             nan     1.0000   -0.0707
##     40 94453736.8072             nan     1.0000 -73357843.1509
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0001
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0001
##      8        1.2905             nan     0.0010    0.0001
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0001
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2793             nan     0.0010    0.0002
##     60        1.2727             nan     0.0010    0.0002
##     80        1.2661             nan     0.0010    0.0001
##    100        1.2599             nan     0.0010    0.0002
##    120        1.2538             nan     0.0010    0.0001
##    140        1.2480             nan     0.0010    0.0001
##    160        1.2420             nan     0.0010    0.0001
##    180        1.2364             nan     0.0010    0.0001
##    200        1.2310             nan     0.0010    0.0001
##    220        1.2260             nan     0.0010    0.0001
##    240        1.2209             nan     0.0010    0.0001
##    260        1.2161             nan     0.0010    0.0001
##    280        1.2116             nan     0.0010    0.0001
##    300        1.2067             nan     0.0010    0.0001
##    320        1.2023             nan     0.0010    0.0001
##    340        1.1980             nan     0.0010    0.0001
##    360        1.1937             nan     0.0010    0.0001
##    380        1.1896             nan     0.0010    0.0001
##    400        1.1856             nan     0.0010    0.0001
##    420        1.1818             nan     0.0010    0.0001
##    440        1.1780             nan     0.0010    0.0001
##    460        1.1742             nan     0.0010    0.0001
##    480        1.1706             nan     0.0010    0.0001
##    500        1.1671             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2928             nan     0.0010    0.0001
##      3        1.2924             nan     0.0010    0.0002
##      4        1.2920             nan     0.0010    0.0002
##      5        1.2917             nan     0.0010    0.0002
##      6        1.2913             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2899             nan     0.0010    0.0002
##     20        1.2863             nan     0.0010    0.0002
##     40        1.2794             nan     0.0010    0.0002
##     60        1.2728             nan     0.0010    0.0002
##     80        1.2662             nan     0.0010    0.0001
##    100        1.2598             nan     0.0010    0.0001
##    120        1.2536             nan     0.0010    0.0001
##    140        1.2477             nan     0.0010    0.0001
##    160        1.2422             nan     0.0010    0.0001
##    180        1.2369             nan     0.0010    0.0001
##    200        1.2314             nan     0.0010    0.0001
##    220        1.2263             nan     0.0010    0.0001
##    240        1.2213             nan     0.0010    0.0001
##    260        1.2165             nan     0.0010    0.0001
##    280        1.2119             nan     0.0010    0.0001
##    300        1.2074             nan     0.0010    0.0001
##    320        1.2030             nan     0.0010    0.0001
##    340        1.1985             nan     0.0010    0.0001
##    360        1.1943             nan     0.0010    0.0001
##    380        1.1901             nan     0.0010    0.0001
##    400        1.1861             nan     0.0010    0.0001
##    420        1.1820             nan     0.0010    0.0001
##    440        1.1780             nan     0.0010    0.0001
##    460        1.1743             nan     0.0010    0.0001
##    480        1.1709             nan     0.0010    0.0001
##    500        1.1674             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2860             nan     0.0010    0.0002
##     40        1.2790             nan     0.0010    0.0002
##     60        1.2722             nan     0.0010    0.0001
##     80        1.2655             nan     0.0010    0.0002
##    100        1.2594             nan     0.0010    0.0001
##    120        1.2535             nan     0.0010    0.0001
##    140        1.2477             nan     0.0010    0.0001
##    160        1.2420             nan     0.0010    0.0001
##    180        1.2366             nan     0.0010    0.0001
##    200        1.2310             nan     0.0010    0.0001
##    220        1.2259             nan     0.0010    0.0001
##    240        1.2209             nan     0.0010    0.0001
##    260        1.2161             nan     0.0010    0.0001
##    280        1.2115             nan     0.0010    0.0001
##    300        1.2070             nan     0.0010    0.0001
##    320        1.2024             nan     0.0010    0.0001
##    340        1.1982             nan     0.0010    0.0001
##    360        1.1939             nan     0.0010    0.0001
##    380        1.1898             nan     0.0010    0.0001
##    400        1.1858             nan     0.0010    0.0001
##    420        1.1818             nan     0.0010    0.0001
##    440        1.1781             nan     0.0010    0.0001
##    460        1.1744             nan     0.0010    0.0001
##    480        1.1708             nan     0.0010    0.0001
##    500        1.1672             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2840             nan     0.0010    0.0002
##     40        1.2752             nan     0.0010    0.0002
##     60        1.2666             nan     0.0010    0.0002
##     80        1.2583             nan     0.0010    0.0002
##    100        1.2503             nan     0.0010    0.0002
##    120        1.2426             nan     0.0010    0.0001
##    140        1.2352             nan     0.0010    0.0001
##    160        1.2278             nan     0.0010    0.0002
##    180        1.2207             nan     0.0010    0.0001
##    200        1.2139             nan     0.0010    0.0001
##    220        1.2071             nan     0.0010    0.0001
##    240        1.2005             nan     0.0010    0.0001
##    260        1.1942             nan     0.0010    0.0001
##    280        1.1882             nan     0.0010    0.0001
##    300        1.1823             nan     0.0010    0.0001
##    320        1.1765             nan     0.0010    0.0001
##    340        1.1708             nan     0.0010    0.0001
##    360        1.1652             nan     0.0010    0.0001
##    380        1.1597             nan     0.0010    0.0001
##    400        1.1544             nan     0.0010    0.0001
##    420        1.1492             nan     0.0010    0.0001
##    440        1.1441             nan     0.0010    0.0001
##    460        1.1393             nan     0.0010    0.0001
##    480        1.1346             nan     0.0010    0.0001
##    500        1.1299             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2902             nan     0.0010    0.0002
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2842             nan     0.0010    0.0002
##     40        1.2755             nan     0.0010    0.0002
##     60        1.2668             nan     0.0010    0.0002
##     80        1.2587             nan     0.0010    0.0002
##    100        1.2507             nan     0.0010    0.0002
##    120        1.2427             nan     0.0010    0.0001
##    140        1.2350             nan     0.0010    0.0002
##    160        1.2277             nan     0.0010    0.0002
##    180        1.2206             nan     0.0010    0.0001
##    200        1.2137             nan     0.0010    0.0002
##    220        1.2070             nan     0.0010    0.0001
##    240        1.2005             nan     0.0010    0.0001
##    260        1.1941             nan     0.0010    0.0001
##    280        1.1881             nan     0.0010    0.0001
##    300        1.1820             nan     0.0010    0.0001
##    320        1.1761             nan     0.0010    0.0001
##    340        1.1705             nan     0.0010    0.0001
##    360        1.1650             nan     0.0010    0.0001
##    380        1.1594             nan     0.0010    0.0001
##    400        1.1541             nan     0.0010    0.0001
##    420        1.1493             nan     0.0010    0.0001
##    440        1.1444             nan     0.0010    0.0001
##    460        1.1395             nan     0.0010    0.0001
##    480        1.1345             nan     0.0010    0.0001
##    500        1.1300             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2842             nan     0.0010    0.0002
##     40        1.2754             nan     0.0010    0.0002
##     60        1.2671             nan     0.0010    0.0001
##     80        1.2588             nan     0.0010    0.0002
##    100        1.2508             nan     0.0010    0.0002
##    120        1.2428             nan     0.0010    0.0002
##    140        1.2353             nan     0.0010    0.0002
##    160        1.2281             nan     0.0010    0.0001
##    180        1.2208             nan     0.0010    0.0002
##    200        1.2139             nan     0.0010    0.0001
##    220        1.2073             nan     0.0010    0.0001
##    240        1.2007             nan     0.0010    0.0002
##    260        1.1943             nan     0.0010    0.0001
##    280        1.1883             nan     0.0010    0.0001
##    300        1.1822             nan     0.0010    0.0001
##    320        1.1764             nan     0.0010    0.0001
##    340        1.1707             nan     0.0010    0.0001
##    360        1.1651             nan     0.0010    0.0001
##    380        1.1597             nan     0.0010    0.0001
##    400        1.1545             nan     0.0010    0.0001
##    420        1.1494             nan     0.0010    0.0001
##    440        1.1442             nan     0.0010    0.0001
##    460        1.1393             nan     0.0010    0.0001
##    480        1.1344             nan     0.0010    0.0001
##    500        1.1296             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0003
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2890             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0003
##     20        1.2827             nan     0.0010    0.0002
##     40        1.2727             nan     0.0010    0.0003
##     60        1.2627             nan     0.0010    0.0002
##     80        1.2531             nan     0.0010    0.0002
##    100        1.2439             nan     0.0010    0.0002
##    120        1.2352             nan     0.0010    0.0002
##    140        1.2265             nan     0.0010    0.0002
##    160        1.2181             nan     0.0010    0.0002
##    180        1.2100             nan     0.0010    0.0002
##    200        1.2020             nan     0.0010    0.0001
##    220        1.1945             nan     0.0010    0.0001
##    240        1.1869             nan     0.0010    0.0001
##    260        1.1795             nan     0.0010    0.0002
##    280        1.1723             nan     0.0010    0.0001
##    300        1.1656             nan     0.0010    0.0001
##    320        1.1588             nan     0.0010    0.0001
##    340        1.1523             nan     0.0010    0.0001
##    360        1.1459             nan     0.0010    0.0001
##    380        1.1399             nan     0.0010    0.0001
##    400        1.1337             nan     0.0010    0.0001
##    420        1.1279             nan     0.0010    0.0001
##    440        1.1225             nan     0.0010    0.0001
##    460        1.1170             nan     0.0010    0.0001
##    480        1.1115             nan     0.0010    0.0001
##    500        1.1063             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2908             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2897             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0002
##     10        1.2880             nan     0.0010    0.0003
##     20        1.2829             nan     0.0010    0.0002
##     40        1.2726             nan     0.0010    0.0002
##     60        1.2631             nan     0.0010    0.0002
##     80        1.2535             nan     0.0010    0.0002
##    100        1.2442             nan     0.0010    0.0002
##    120        1.2353             nan     0.0010    0.0002
##    140        1.2266             nan     0.0010    0.0002
##    160        1.2184             nan     0.0010    0.0002
##    180        1.2102             nan     0.0010    0.0002
##    200        1.2024             nan     0.0010    0.0001
##    220        1.1946             nan     0.0010    0.0002
##    240        1.1873             nan     0.0010    0.0001
##    260        1.1800             nan     0.0010    0.0002
##    280        1.1728             nan     0.0010    0.0002
##    300        1.1659             nan     0.0010    0.0001
##    320        1.1594             nan     0.0010    0.0001
##    340        1.1529             nan     0.0010    0.0001
##    360        1.1467             nan     0.0010    0.0001
##    380        1.1404             nan     0.0010    0.0001
##    400        1.1344             nan     0.0010    0.0001
##    420        1.1284             nan     0.0010    0.0001
##    440        1.1226             nan     0.0010    0.0001
##    460        1.1172             nan     0.0010    0.0001
##    480        1.1120             nan     0.0010    0.0001
##    500        1.1065             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2890             nan     0.0010    0.0003
##      9        1.2886             nan     0.0010    0.0002
##     10        1.2880             nan     0.0010    0.0002
##     20        1.2830             nan     0.0010    0.0002
##     40        1.2728             nan     0.0010    0.0002
##     60        1.2628             nan     0.0010    0.0002
##     80        1.2531             nan     0.0010    0.0002
##    100        1.2441             nan     0.0010    0.0002
##    120        1.2350             nan     0.0010    0.0002
##    140        1.2265             nan     0.0010    0.0002
##    160        1.2181             nan     0.0010    0.0002
##    180        1.2100             nan     0.0010    0.0002
##    200        1.2021             nan     0.0010    0.0001
##    220        1.1944             nan     0.0010    0.0001
##    240        1.1868             nan     0.0010    0.0001
##    260        1.1794             nan     0.0010    0.0002
##    280        1.1725             nan     0.0010    0.0002
##    300        1.1654             nan     0.0010    0.0001
##    320        1.1586             nan     0.0010    0.0002
##    340        1.1522             nan     0.0010    0.0001
##    360        1.1460             nan     0.0010    0.0001
##    380        1.1397             nan     0.0010    0.0001
##    400        1.1336             nan     0.0010    0.0001
##    420        1.1277             nan     0.0010    0.0001
##    440        1.1221             nan     0.0010    0.0001
##    460        1.1163             nan     0.0010    0.0001
##    480        1.1108             nan     0.0010    0.0001
##    500        1.1057             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2564             nan     0.1000    0.0154
##      2        1.2284             nan     0.1000    0.0129
##      3        1.2055             nan     0.1000    0.0110
##      4        1.1851             nan     0.1000    0.0086
##      5        1.1700             nan     0.1000    0.0058
##      6        1.1527             nan     0.1000    0.0071
##      7        1.1368             nan     0.1000    0.0066
##      8        1.1220             nan     0.1000    0.0063
##      9        1.1080             nan     0.1000    0.0050
##     10        1.0961             nan     0.1000    0.0037
##     20        1.0020             nan     0.1000    0.0016
##     40        0.9276             nan     0.1000   -0.0006
##     60        0.8885             nan     0.1000    0.0001
##     80        0.8580             nan     0.1000   -0.0002
##    100        0.8413             nan     0.1000   -0.0010
##    120        0.8265             nan     0.1000   -0.0015
##    140        0.8137             nan     0.1000   -0.0014
##    160        0.8040             nan     0.1000   -0.0004
##    180        0.7981             nan     0.1000   -0.0009
##    200        0.7914             nan     0.1000   -0.0008
##    220        0.7830             nan     0.1000   -0.0007
##    240        0.7754             nan     0.1000   -0.0009
##    260        0.7693             nan     0.1000   -0.0016
##    280        0.7616             nan     0.1000   -0.0008
##    300        0.7536             nan     0.1000   -0.0003
##    320        0.7477             nan     0.1000   -0.0008
##    340        0.7419             nan     0.1000   -0.0005
##    360        0.7369             nan     0.1000   -0.0004
##    380        0.7313             nan     0.1000   -0.0007
##    400        0.7267             nan     0.1000   -0.0006
##    420        0.7218             nan     0.1000   -0.0009
##    440        0.7192             nan     0.1000   -0.0009
##    460        0.7144             nan     0.1000   -0.0009
##    480        0.7101             nan     0.1000   -0.0009
##    500        0.7048             nan     0.1000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2571             nan     0.1000    0.0170
##      2        1.2309             nan     0.1000    0.0135
##      3        1.2018             nan     0.1000    0.0113
##      4        1.1816             nan     0.1000    0.0094
##      5        1.1652             nan     0.1000    0.0070
##      6        1.1507             nan     0.1000    0.0060
##      7        1.1328             nan     0.1000    0.0073
##      8        1.1200             nan     0.1000    0.0057
##      9        1.1065             nan     0.1000    0.0055
##     10        1.0947             nan     0.1000    0.0046
##     20        1.0080             nan     0.1000    0.0026
##     40        0.9268             nan     0.1000    0.0003
##     60        0.8862             nan     0.1000   -0.0007
##     80        0.8645             nan     0.1000   -0.0004
##    100        0.8448             nan     0.1000   -0.0005
##    120        0.8301             nan     0.1000   -0.0005
##    140        0.8204             nan     0.1000   -0.0014
##    160        0.8103             nan     0.1000   -0.0005
##    180        0.8005             nan     0.1000   -0.0025
##    200        0.7937             nan     0.1000   -0.0013
##    220        0.7846             nan     0.1000   -0.0015
##    240        0.7782             nan     0.1000   -0.0016
##    260        0.7714             nan     0.1000   -0.0009
##    280        0.7625             nan     0.1000   -0.0007
##    300        0.7570             nan     0.1000   -0.0009
##    320        0.7511             nan     0.1000   -0.0012
##    340        0.7461             nan     0.1000   -0.0010
##    360        0.7409             nan     0.1000   -0.0006
##    380        0.7364             nan     0.1000   -0.0004
##    400        0.7326             nan     0.1000   -0.0007
##    420        0.7258             nan     0.1000   -0.0006
##    440        0.7218             nan     0.1000   -0.0008
##    460        0.7197             nan     0.1000   -0.0006
##    480        0.7155             nan     0.1000   -0.0012
##    500        0.7116             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2580             nan     0.1000    0.0155
##      2        1.2276             nan     0.1000    0.0139
##      3        1.2006             nan     0.1000    0.0120
##      4        1.1796             nan     0.1000    0.0087
##      5        1.1664             nan     0.1000    0.0057
##      6        1.1510             nan     0.1000    0.0066
##      7        1.1358             nan     0.1000    0.0057
##      8        1.1182             nan     0.1000    0.0065
##      9        1.1037             nan     0.1000    0.0064
##     10        1.0899             nan     0.1000    0.0052
##     20        1.0087             nan     0.1000    0.0000
##     40        0.9251             nan     0.1000    0.0008
##     60        0.8862             nan     0.1000   -0.0005
##     80        0.8578             nan     0.1000   -0.0000
##    100        0.8389             nan     0.1000   -0.0000
##    120        0.8233             nan     0.1000   -0.0002
##    140        0.8130             nan     0.1000   -0.0016
##    160        0.8021             nan     0.1000    0.0001
##    180        0.7923             nan     0.1000   -0.0005
##    200        0.7827             nan     0.1000   -0.0014
##    220        0.7781             nan     0.1000   -0.0014
##    240        0.7714             nan     0.1000   -0.0012
##    260        0.7653             nan     0.1000   -0.0010
##    280        0.7596             nan     0.1000   -0.0006
##    300        0.7544             nan     0.1000   -0.0008
##    320        0.7479             nan     0.1000   -0.0005
##    340        0.7424             nan     0.1000   -0.0014
##    360        0.7383             nan     0.1000   -0.0014
##    380        0.7350             nan     0.1000   -0.0014
##    400        0.7283             nan     0.1000   -0.0005
##    420        0.7233             nan     0.1000   -0.0011
##    440        0.7198             nan     0.1000   -0.0011
##    460        0.7149             nan     0.1000   -0.0009
##    480        0.7103             nan     0.1000   -0.0007
##    500        0.7057             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2516             nan     0.1000    0.0206
##      2        1.2084             nan     0.1000    0.0171
##      3        1.1741             nan     0.1000    0.0142
##      4        1.1445             nan     0.1000    0.0140
##      5        1.1188             nan     0.1000    0.0112
##      6        1.0966             nan     0.1000    0.0088
##      7        1.0786             nan     0.1000    0.0058
##      8        1.0620             nan     0.1000    0.0063
##      9        1.0493             nan     0.1000    0.0053
##     10        1.0375             nan     0.1000    0.0049
##     20        0.9395             nan     0.1000    0.0016
##     40        0.8595             nan     0.1000   -0.0006
##     60        0.8117             nan     0.1000   -0.0007
##     80        0.7807             nan     0.1000   -0.0020
##    100        0.7546             nan     0.1000   -0.0007
##    120        0.7299             nan     0.1000   -0.0026
##    140        0.7054             nan     0.1000   -0.0006
##    160        0.6836             nan     0.1000   -0.0019
##    180        0.6631             nan     0.1000   -0.0014
##    200        0.6389             nan     0.1000   -0.0007
##    220        0.6245             nan     0.1000   -0.0003
##    240        0.6055             nan     0.1000   -0.0009
##    260        0.5897             nan     0.1000   -0.0008
##    280        0.5745             nan     0.1000   -0.0015
##    300        0.5596             nan     0.1000   -0.0016
##    320        0.5459             nan     0.1000   -0.0009
##    340        0.5314             nan     0.1000   -0.0015
##    360        0.5193             nan     0.1000   -0.0015
##    380        0.5077             nan     0.1000   -0.0013
##    400        0.4951             nan     0.1000   -0.0001
##    420        0.4870             nan     0.1000   -0.0011
##    440        0.4756             nan     0.1000   -0.0010
##    460        0.4657             nan     0.1000   -0.0010
##    480        0.4568             nan     0.1000   -0.0002
##    500        0.4454             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2446             nan     0.1000    0.0199
##      2        1.2109             nan     0.1000    0.0173
##      3        1.1748             nan     0.1000    0.0118
##      4        1.1483             nan     0.1000    0.0113
##      5        1.1219             nan     0.1000    0.0117
##      6        1.1052             nan     0.1000    0.0068
##      7        1.0865             nan     0.1000    0.0075
##      8        1.0685             nan     0.1000    0.0085
##      9        1.0522             nan     0.1000    0.0033
##     10        1.0372             nan     0.1000    0.0077
##     20        0.9408             nan     0.1000    0.0014
##     40        0.8600             nan     0.1000   -0.0000
##     60        0.8066             nan     0.1000   -0.0001
##     80        0.7728             nan     0.1000   -0.0009
##    100        0.7403             nan     0.1000   -0.0011
##    120        0.7164             nan     0.1000   -0.0016
##    140        0.6927             nan     0.1000   -0.0005
##    160        0.6753             nan     0.1000   -0.0004
##    180        0.6584             nan     0.1000   -0.0008
##    200        0.6405             nan     0.1000   -0.0010
##    220        0.6240             nan     0.1000   -0.0014
##    240        0.6076             nan     0.1000   -0.0009
##    260        0.5918             nan     0.1000   -0.0006
##    280        0.5787             nan     0.1000    0.0001
##    300        0.5634             nan     0.1000   -0.0008
##    320        0.5533             nan     0.1000   -0.0015
##    340        0.5365             nan     0.1000   -0.0008
##    360        0.5236             nan     0.1000   -0.0010
##    380        0.5110             nan     0.1000   -0.0011
##    400        0.4985             nan     0.1000   -0.0014
##    420        0.4867             nan     0.1000   -0.0011
##    440        0.4752             nan     0.1000   -0.0009
##    460        0.4612             nan     0.1000   -0.0011
##    480        0.4506             nan     0.1000   -0.0002
##    500        0.4416             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2517             nan     0.1000    0.0191
##      2        1.2115             nan     0.1000    0.0153
##      3        1.1793             nan     0.1000    0.0107
##      4        1.1500             nan     0.1000    0.0108
##      5        1.1282             nan     0.1000    0.0103
##      6        1.1070             nan     0.1000    0.0095
##      7        1.0831             nan     0.1000    0.0070
##      8        1.0632             nan     0.1000    0.0080
##      9        1.0483             nan     0.1000    0.0063
##     10        1.0348             nan     0.1000    0.0038
##     20        0.9343             nan     0.1000    0.0021
##     40        0.8594             nan     0.1000    0.0009
##     60        0.8098             nan     0.1000   -0.0008
##     80        0.7704             nan     0.1000   -0.0013
##    100        0.7483             nan     0.1000   -0.0011
##    120        0.7245             nan     0.1000   -0.0009
##    140        0.7034             nan     0.1000   -0.0015
##    160        0.6852             nan     0.1000   -0.0010
##    180        0.6657             nan     0.1000   -0.0000
##    200        0.6467             nan     0.1000   -0.0006
##    220        0.6300             nan     0.1000   -0.0010
##    240        0.6127             nan     0.1000   -0.0019
##    260        0.5962             nan     0.1000   -0.0006
##    280        0.5854             nan     0.1000   -0.0013
##    300        0.5739             nan     0.1000   -0.0010
##    320        0.5613             nan     0.1000   -0.0001
##    340        0.5510             nan     0.1000   -0.0013
##    360        0.5384             nan     0.1000   -0.0006
##    380        0.5256             nan     0.1000   -0.0006
##    400        0.5133             nan     0.1000   -0.0008
##    420        0.5030             nan     0.1000   -0.0001
##    440        0.4925             nan     0.1000   -0.0010
##    460        0.4802             nan     0.1000   -0.0004
##    480        0.4700             nan     0.1000   -0.0008
##    500        0.4589             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2382             nan     0.1000    0.0216
##      2        1.1986             nan     0.1000    0.0145
##      3        1.1690             nan     0.1000    0.0122
##      4        1.1344             nan     0.1000    0.0130
##      5        1.1008             nan     0.1000    0.0145
##      6        1.0768             nan     0.1000    0.0077
##      7        1.0546             nan     0.1000    0.0081
##      8        1.0346             nan     0.1000    0.0049
##      9        1.0153             nan     0.1000    0.0062
##     10        0.9993             nan     0.1000    0.0051
##     20        0.8986             nan     0.1000    0.0012
##     40        0.8063             nan     0.1000    0.0000
##     60        0.7508             nan     0.1000   -0.0023
##     80        0.7052             nan     0.1000   -0.0019
##    100        0.6682             nan     0.1000   -0.0019
##    120        0.6307             nan     0.1000   -0.0012
##    140        0.6007             nan     0.1000   -0.0014
##    160        0.5742             nan     0.1000   -0.0005
##    180        0.5506             nan     0.1000   -0.0018
##    200        0.5283             nan     0.1000   -0.0011
##    220        0.5086             nan     0.1000   -0.0013
##    240        0.4876             nan     0.1000   -0.0017
##    260        0.4629             nan     0.1000   -0.0015
##    280        0.4477             nan     0.1000   -0.0011
##    300        0.4271             nan     0.1000   -0.0010
##    320        0.4113             nan     0.1000   -0.0013
##    340        0.3960             nan     0.1000   -0.0008
##    360        0.3835             nan     0.1000   -0.0019
##    380        0.3715             nan     0.1000   -0.0006
##    400        0.3585             nan     0.1000   -0.0009
##    420        0.3461             nan     0.1000   -0.0011
##    440        0.3310             nan     0.1000   -0.0008
##    460        0.3187             nan     0.1000   -0.0005
##    480        0.3088             nan     0.1000   -0.0002
##    500        0.2990             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2504             nan     0.1000    0.0192
##      2        1.2045             nan     0.1000    0.0186
##      3        1.1602             nan     0.1000    0.0197
##      4        1.1324             nan     0.1000    0.0098
##      5        1.1055             nan     0.1000    0.0078
##      6        1.0796             nan     0.1000    0.0094
##      7        1.0555             nan     0.1000    0.0091
##      8        1.0409             nan     0.1000    0.0044
##      9        1.0227             nan     0.1000    0.0065
##     10        1.0049             nan     0.1000    0.0068
##     20        0.9040             nan     0.1000    0.0009
##     40        0.8107             nan     0.1000   -0.0021
##     60        0.7542             nan     0.1000   -0.0008
##     80        0.7104             nan     0.1000   -0.0016
##    100        0.6682             nan     0.1000    0.0004
##    120        0.6346             nan     0.1000   -0.0003
##    140        0.6053             nan     0.1000   -0.0020
##    160        0.5761             nan     0.1000   -0.0005
##    180        0.5468             nan     0.1000   -0.0023
##    200        0.5254             nan     0.1000   -0.0013
##    220        0.4977             nan     0.1000   -0.0010
##    240        0.4781             nan     0.1000   -0.0007
##    260        0.4577             nan     0.1000   -0.0008
##    280        0.4403             nan     0.1000   -0.0015
##    300        0.4219             nan     0.1000   -0.0013
##    320        0.4052             nan     0.1000   -0.0011
##    340        0.3887             nan     0.1000   -0.0011
##    360        0.3736             nan     0.1000   -0.0006
##    380        0.3609             nan     0.1000   -0.0010
##    400        0.3445             nan     0.1000   -0.0016
##    420        0.3304             nan     0.1000   -0.0012
##    440        0.3186             nan     0.1000   -0.0014
##    460        0.3052             nan     0.1000   -0.0003
##    480        0.2932             nan     0.1000   -0.0001
##    500        0.2810             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2455             nan     0.1000    0.0229
##      2        1.1976             nan     0.1000    0.0205
##      3        1.1619             nan     0.1000    0.0148
##      4        1.1297             nan     0.1000    0.0123
##      5        1.0998             nan     0.1000    0.0103
##      6        1.0775             nan     0.1000    0.0066
##      7        1.0591             nan     0.1000    0.0076
##      8        1.0350             nan     0.1000    0.0070
##      9        1.0189             nan     0.1000    0.0046
##     10        1.0033             nan     0.1000    0.0056
##     20        0.9026             nan     0.1000    0.0007
##     40        0.8093             nan     0.1000   -0.0016
##     60        0.7516             nan     0.1000    0.0011
##     80        0.7037             nan     0.1000   -0.0009
##    100        0.6703             nan     0.1000   -0.0012
##    120        0.6417             nan     0.1000   -0.0013
##    140        0.6111             nan     0.1000   -0.0009
##    160        0.5830             nan     0.1000   -0.0010
##    180        0.5559             nan     0.1000   -0.0013
##    200        0.5321             nan     0.1000   -0.0007
##    220        0.5063             nan     0.1000   -0.0019
##    240        0.4852             nan     0.1000   -0.0009
##    260        0.4680             nan     0.1000   -0.0019
##    280        0.4481             nan     0.1000   -0.0004
##    300        0.4289             nan     0.1000   -0.0004
##    320        0.4118             nan     0.1000   -0.0017
##    340        0.3941             nan     0.1000   -0.0002
##    360        0.3794             nan     0.1000   -0.0003
##    380        0.3666             nan     0.1000   -0.0010
##    400        0.3530             nan     0.1000   -0.0009
##    420        0.3389             nan     0.1000   -0.0007
##    440        0.3275             nan     0.1000   -0.0006
##    460        0.3169             nan     0.1000   -0.0009
##    480        0.3031             nan     0.1000   -0.0004
##    500        0.2915             nan     0.1000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2270             nan     0.2000    0.0319
##      2        1.1848             nan     0.2000    0.0209
##      3        1.1528             nan     0.2000    0.0131
##      4        1.1274             nan     0.2000    0.0101
##      5        1.1056             nan     0.2000    0.0103
##      6        1.0839             nan     0.2000    0.0060
##      7        1.0594             nan     0.2000    0.0112
##      8        1.0449             nan     0.2000    0.0046
##      9        1.0286             nan     0.2000    0.0068
##     10        1.0189             nan     0.2000    0.0030
##     20        0.9301             nan     0.2000    0.0011
##     40        0.8658             nan     0.2000   -0.0039
##     60        0.8345             nan     0.2000   -0.0031
##     80        0.8094             nan     0.2000   -0.0032
##    100        0.7934             nan     0.2000   -0.0010
##    120        0.7805             nan     0.2000   -0.0003
##    140        0.7659             nan     0.2000   -0.0021
##    160        0.7542             nan     0.2000   -0.0023
##    180        0.7456             nan     0.2000   -0.0027
##    200        0.7315             nan     0.2000   -0.0014
##    220        0.7242             nan     0.2000   -0.0012
##    240        0.7128             nan     0.2000   -0.0018
##    260        0.7091             nan     0.2000   -0.0024
##    280        0.6992             nan     0.2000    0.0005
##    300        0.6906             nan     0.2000   -0.0020
##    320        0.6861             nan     0.2000   -0.0048
##    340        0.6798             nan     0.2000   -0.0009
##    360        0.6719             nan     0.2000   -0.0016
##    380        0.6665             nan     0.2000   -0.0007
##    400        0.6634             nan     0.2000   -0.0011
##    420        0.6553             nan     0.2000   -0.0027
##    440        0.6508             nan     0.2000   -0.0041
##    460        0.6455             nan     0.2000   -0.0036
##    480        0.6407             nan     0.2000   -0.0019
##    500        0.6351             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2231             nan     0.2000    0.0262
##      2        1.1769             nan     0.2000    0.0193
##      3        1.1477             nan     0.2000    0.0119
##      4        1.1171             nan     0.2000    0.0119
##      5        1.0984             nan     0.2000    0.0076
##      6        1.0759             nan     0.2000    0.0091
##      7        1.0521             nan     0.2000    0.0074
##      8        1.0370             nan     0.2000    0.0037
##      9        1.0212             nan     0.2000    0.0033
##     10        1.0089             nan     0.2000    0.0040
##     20        0.9371             nan     0.2000   -0.0007
##     40        0.8697             nan     0.2000    0.0002
##     60        0.8263             nan     0.2000   -0.0024
##     80        0.8056             nan     0.2000    0.0001
##    100        0.7951             nan     0.2000   -0.0025
##    120        0.7795             nan     0.2000   -0.0013
##    140        0.7674             nan     0.2000   -0.0012
##    160        0.7532             nan     0.2000   -0.0021
##    180        0.7443             nan     0.2000   -0.0015
##    200        0.7329             nan     0.2000   -0.0013
##    220        0.7228             nan     0.2000   -0.0001
##    240        0.7123             nan     0.2000   -0.0007
##    260        0.7056             nan     0.2000   -0.0016
##    280        0.6964             nan     0.2000   -0.0006
##    300        0.6877             nan     0.2000   -0.0001
##    320        0.6843             nan     0.2000   -0.0019
##    340        0.6739             nan     0.2000   -0.0035
##    360        0.6722             nan     0.2000   -0.0020
##    380        0.6646             nan     0.2000   -0.0025
##    400        0.6592             nan     0.2000   -0.0015
##    420        0.6533             nan     0.2000   -0.0014
##    440        0.6469             nan     0.2000   -0.0020
##    460        0.6405             nan     0.2000   -0.0005
##    480        0.6337             nan     0.2000   -0.0005
##    500        0.6268             nan     0.2000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2208             nan     0.2000    0.0295
##      2        1.1797             nan     0.2000    0.0196
##      3        1.1475             nan     0.2000    0.0149
##      4        1.1182             nan     0.2000    0.0125
##      5        1.0966             nan     0.2000    0.0076
##      6        1.0672             nan     0.2000    0.0100
##      7        1.0485             nan     0.2000    0.0087
##      8        1.0314             nan     0.2000    0.0071
##      9        1.0199             nan     0.2000    0.0049
##     10        1.0103             nan     0.2000    0.0014
##     20        0.9308             nan     0.2000   -0.0003
##     40        0.8650             nan     0.2000   -0.0018
##     60        0.8326             nan     0.2000   -0.0034
##     80        0.8125             nan     0.2000   -0.0006
##    100        0.7908             nan     0.2000   -0.0011
##    120        0.7752             nan     0.2000   -0.0017
##    140        0.7629             nan     0.2000   -0.0015
##    160        0.7525             nan     0.2000   -0.0020
##    180        0.7439             nan     0.2000   -0.0017
##    200        0.7371             nan     0.2000   -0.0005
##    220        0.7293             nan     0.2000   -0.0036
##    240        0.7184             nan     0.2000   -0.0030
##    260        0.7103             nan     0.2000   -0.0011
##    280        0.7027             nan     0.2000   -0.0019
##    300        0.6913             nan     0.2000   -0.0000
##    320        0.6866             nan     0.2000   -0.0020
##    340        0.6807             nan     0.2000   -0.0021
##    360        0.6747             nan     0.2000   -0.0013
##    380        0.6682             nan     0.2000   -0.0024
##    400        0.6641             nan     0.2000   -0.0023
##    420        0.6547             nan     0.2000   -0.0014
##    440        0.6500             nan     0.2000   -0.0024
##    460        0.6432             nan     0.2000   -0.0027
##    480        0.6366             nan     0.2000   -0.0023
##    500        0.6319             nan     0.2000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2076             nan     0.2000    0.0293
##      2        1.1513             nan     0.2000    0.0236
##      3        1.1056             nan     0.2000    0.0183
##      4        1.0655             nan     0.2000    0.0149
##      5        1.0379             nan     0.2000    0.0086
##      6        1.0167             nan     0.2000    0.0013
##      7        0.9920             nan     0.2000    0.0078
##      8        0.9782             nan     0.2000    0.0049
##      9        0.9625             nan     0.2000    0.0022
##     10        0.9461             nan     0.2000    0.0010
##     20        0.8654             nan     0.2000   -0.0041
##     40        0.7866             nan     0.2000   -0.0022
##     60        0.7298             nan     0.2000   -0.0011
##     80        0.6971             nan     0.2000   -0.0015
##    100        0.6626             nan     0.2000   -0.0012
##    120        0.6367             nan     0.2000   -0.0020
##    140        0.6238             nan     0.2000   -0.0032
##    160        0.5907             nan     0.2000   -0.0027
##    180        0.5683             nan     0.2000   -0.0026
##    200        0.5395             nan     0.2000   -0.0027
##    220        0.5147             nan     0.2000   -0.0013
##    240        0.4914             nan     0.2000   -0.0036
##    260        0.4721             nan     0.2000   -0.0020
##    280        0.4515             nan     0.2000   -0.0009
##    300        0.4236             nan     0.2000   -0.0013
##    320        0.4080             nan     0.2000   -0.0019
##    340        0.3918             nan     0.2000   -0.0010
##    360        0.3739             nan     0.2000   -0.0011
##    380        0.3582             nan     0.2000   -0.0017
##    400        0.3443             nan     0.2000   -0.0030
##    420        0.3299             nan     0.2000   -0.0010
##    440        0.3211             nan     0.2000   -0.0018
##    460        0.3080             nan     0.2000   -0.0004
##    480        0.2958             nan     0.2000   -0.0007
##    500        0.2851             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2091             nan     0.2000    0.0318
##      2        1.1443             nan     0.2000    0.0275
##      3        1.0927             nan     0.2000    0.0245
##      4        1.0601             nan     0.2000    0.0124
##      5        1.0354             nan     0.2000    0.0073
##      6        1.0151             nan     0.2000    0.0062
##      7        0.9964             nan     0.2000    0.0050
##      8        0.9739             nan     0.2000    0.0033
##      9        0.9586             nan     0.2000    0.0050
##     10        0.9423             nan     0.2000    0.0059
##     20        0.8632             nan     0.2000   -0.0049
##     40        0.7839             nan     0.2000   -0.0008
##     60        0.7279             nan     0.2000   -0.0017
##     80        0.6898             nan     0.2000   -0.0005
##    100        0.6624             nan     0.2000   -0.0035
##    120        0.6276             nan     0.2000   -0.0020
##    140        0.5875             nan     0.2000   -0.0023
##    160        0.5606             nan     0.2000   -0.0012
##    180        0.5366             nan     0.2000   -0.0036
##    200        0.5131             nan     0.2000   -0.0025
##    220        0.4945             nan     0.2000   -0.0027
##    240        0.4717             nan     0.2000   -0.0033
##    260        0.4583             nan     0.2000   -0.0029
##    280        0.4414             nan     0.2000   -0.0015
##    300        0.4289             nan     0.2000   -0.0026
##    320        0.4131             nan     0.2000   -0.0014
##    340        0.3923             nan     0.2000   -0.0021
##    360        0.3787             nan     0.2000   -0.0012
##    380        0.3646             nan     0.2000   -0.0026
##    400        0.3489             nan     0.2000    0.0001
##    420        0.3370             nan     0.2000   -0.0034
##    440        0.3263             nan     0.2000   -0.0012
##    460        0.3141             nan     0.2000   -0.0022
##    480        0.3005             nan     0.2000   -0.0001
##    500        0.2883             nan     0.2000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2046             nan     0.2000    0.0373
##      2        1.1351             nan     0.2000    0.0254
##      3        1.0896             nan     0.2000    0.0194
##      4        1.0580             nan     0.2000    0.0131
##      5        1.0370             nan     0.2000    0.0059
##      6        1.0182             nan     0.2000    0.0042
##      7        0.9919             nan     0.2000    0.0108
##      8        0.9710             nan     0.2000    0.0015
##      9        0.9613             nan     0.2000    0.0020
##     10        0.9447             nan     0.2000    0.0032
##     20        0.8709             nan     0.2000   -0.0024
##     40        0.7885             nan     0.2000    0.0007
##     60        0.7370             nan     0.2000   -0.0047
##     80        0.6912             nan     0.2000   -0.0011
##    100        0.6508             nan     0.2000   -0.0020
##    120        0.6223             nan     0.2000   -0.0019
##    140        0.5920             nan     0.2000   -0.0015
##    160        0.5666             nan     0.2000   -0.0013
##    180        0.5396             nan     0.2000   -0.0026
##    200        0.5166             nan     0.2000   -0.0032
##    220        0.4949             nan     0.2000   -0.0012
##    240        0.4728             nan     0.2000   -0.0034
##    260        0.4532             nan     0.2000   -0.0021
##    280        0.4366             nan     0.2000   -0.0017
##    300        0.4228             nan     0.2000   -0.0012
##    320        0.4009             nan     0.2000   -0.0022
##    340        0.3882             nan     0.2000   -0.0010
##    360        0.3714             nan     0.2000   -0.0012
##    380        0.3614             nan     0.2000   -0.0029
##    400        0.3476             nan     0.2000   -0.0018
##    420        0.3357             nan     0.2000   -0.0024
##    440        0.3210             nan     0.2000   -0.0009
##    460        0.3080             nan     0.2000   -0.0011
##    480        0.2963             nan     0.2000   -0.0026
##    500        0.2855             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2037             nan     0.2000    0.0453
##      2        1.1350             nan     0.2000    0.0238
##      3        1.0809             nan     0.2000    0.0206
##      4        1.0422             nan     0.2000    0.0118
##      5        1.0071             nan     0.2000    0.0089
##      6        0.9907             nan     0.2000    0.0016
##      7        0.9643             nan     0.2000    0.0051
##      8        0.9435             nan     0.2000    0.0043
##      9        0.9259             nan     0.2000    0.0006
##     10        0.9045             nan     0.2000    0.0047
##     20        0.8188             nan     0.2000   -0.0028
##     40        0.7329             nan     0.2000   -0.0002
##     60        0.6667             nan     0.2000   -0.0033
##     80        0.6110             nan     0.2000   -0.0013
##    100        0.5537             nan     0.2000   -0.0026
##    120        0.5108             nan     0.2000   -0.0043
##    140        0.4627             nan     0.2000   -0.0012
##    160        0.4286             nan     0.2000   -0.0022
##    180        0.3919             nan     0.2000   -0.0011
##    200        0.3591             nan     0.2000    0.0000
##    220        0.3357             nan     0.2000   -0.0022
##    240        0.3108             nan     0.2000   -0.0013
##    260        0.2909             nan     0.2000   -0.0017
##    280        0.2698             nan     0.2000   -0.0015
##    300        0.2530             nan     0.2000   -0.0015
##    320        0.2388             nan     0.2000   -0.0019
##    340        0.2247             nan     0.2000   -0.0008
##    360        0.2073             nan     0.2000   -0.0017
##    380        0.1941             nan     0.2000   -0.0009
##    400        0.1819             nan     0.2000   -0.0008
##    420        0.1710             nan     0.2000   -0.0005
##    440        0.1623             nan     0.2000   -0.0011
##    460        0.1516             nan     0.2000   -0.0009
##    480        0.1455             nan     0.2000   -0.0012
##    500        0.1369             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2000             nan     0.2000    0.0428
##      2        1.1344             nan     0.2000    0.0254
##      3        1.0890             nan     0.2000    0.0206
##      4        1.0488             nan     0.2000    0.0136
##      5        1.0138             nan     0.2000    0.0144
##      6        0.9831             nan     0.2000    0.0111
##      7        0.9557             nan     0.2000    0.0067
##      8        0.9396             nan     0.2000    0.0049
##      9        0.9174             nan     0.2000    0.0046
##     10        0.9025             nan     0.2000    0.0017
##     20        0.8076             nan     0.2000   -0.0026
##     40        0.7198             nan     0.2000   -0.0064
##     60        0.6412             nan     0.2000   -0.0067
##     80        0.5883             nan     0.2000   -0.0032
##    100        0.5323             nan     0.2000   -0.0012
##    120        0.4830             nan     0.2000   -0.0024
##    140        0.4448             nan     0.2000   -0.0027
##    160        0.4099             nan     0.2000   -0.0017
##    180        0.3776             nan     0.2000   -0.0013
##    200        0.3444             nan     0.2000   -0.0011
##    220        0.3213             nan     0.2000   -0.0022
##    240        0.2992             nan     0.2000   -0.0015
##    260        0.2778             nan     0.2000   -0.0013
##    280        0.2557             nan     0.2000   -0.0004
##    300        0.2415             nan     0.2000   -0.0017
##    320        0.2284             nan     0.2000   -0.0007
##    340        0.2142             nan     0.2000   -0.0018
##    360        0.1986             nan     0.2000   -0.0001
##    380        0.1877             nan     0.2000   -0.0007
##    400        0.1801             nan     0.2000   -0.0007
##    420        0.1689             nan     0.2000   -0.0003
##    440        0.1606             nan     0.2000   -0.0006
##    460        0.1503             nan     0.2000   -0.0003
##    480        0.1413             nan     0.2000   -0.0009
##    500        0.1343             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2030             nan     0.2000    0.0431
##      2        1.1297             nan     0.2000    0.0291
##      3        1.0848             nan     0.2000    0.0183
##      4        1.0393             nan     0.2000    0.0144
##      5        1.0060             nan     0.2000    0.0099
##      6        0.9760             nan     0.2000    0.0123
##      7        0.9473             nan     0.2000    0.0071
##      8        0.9309             nan     0.2000    0.0015
##      9        0.9168             nan     0.2000    0.0000
##     10        0.9006             nan     0.2000    0.0014
##     20        0.8175             nan     0.2000   -0.0020
##     40        0.7205             nan     0.2000   -0.0037
##     60        0.6472             nan     0.2000   -0.0019
##     80        0.5932             nan     0.2000   -0.0051
##    100        0.5437             nan     0.2000   -0.0017
##    120        0.4950             nan     0.2000   -0.0047
##    140        0.4589             nan     0.2000   -0.0007
##    160        0.4165             nan     0.2000   -0.0001
##    180        0.3801             nan     0.2000   -0.0024
##    200        0.3494             nan     0.2000   -0.0023
##    220        0.3249             nan     0.2000   -0.0006
##    240        0.3022             nan     0.2000   -0.0018
##    260        0.2820             nan     0.2000   -0.0014
##    280        0.2623             nan     0.2000   -0.0015
##    300        0.2464             nan     0.2000   -0.0016
##    320        0.2301             nan     0.2000   -0.0021
##    340        0.2160             nan     0.2000   -0.0010
##    360        0.2041             nan     0.2000   -0.0012
##    380        0.1900             nan     0.2000   -0.0006
##    400        0.1783             nan     0.2000   -0.0002
##    420        0.1652             nan     0.2000   -0.0019
##    440        0.1562             nan     0.2000   -0.0006
##    460        0.1476             nan     0.2000   -0.0008
##    480        0.1390             nan     0.2000   -0.0004
##    500        0.1329             nan     0.2000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1895             nan     0.3000    0.0425
##      2        1.1422             nan     0.3000    0.0211
##      3        1.0902             nan     0.3000    0.0170
##      4        1.0498             nan     0.3000    0.0143
##      5        1.0294             nan     0.3000    0.0069
##      6        1.0046             nan     0.3000    0.0112
##      7        0.9909             nan     0.3000    0.0040
##      8        0.9728             nan     0.3000    0.0053
##      9        0.9610             nan     0.3000    0.0019
##     10        0.9496             nan     0.3000    0.0035
##     20        0.8831             nan     0.3000    0.0015
##     40        0.8305             nan     0.3000   -0.0063
##     60        0.7948             nan     0.3000   -0.0016
##     80        0.7653             nan     0.3000   -0.0020
##    100        0.7459             nan     0.3000   -0.0012
##    120        0.7261             nan     0.3000   -0.0039
##    140        0.7158             nan     0.3000   -0.0036
##    160        0.7047             nan     0.3000   -0.0037
##    180        0.6924             nan     0.3000   -0.0021
##    200        0.6830             nan     0.3000   -0.0055
##    220        0.6736             nan     0.3000   -0.0029
##    240        0.6628             nan     0.3000   -0.0022
##    260        0.6549             nan     0.3000   -0.0073
##    280        0.6453             nan     0.3000   -0.0017
##    300        0.6411             nan     0.3000   -0.0055
##    320        0.6327             nan     0.3000   -0.0040
##    340        0.6258             nan     0.3000   -0.0029
##    360        0.6184             nan     0.3000   -0.0031
##    380        0.6140             nan     0.3000   -0.0004
##    400        0.6028             nan     0.3000   -0.0031
##    420        0.5981             nan     0.3000   -0.0040
##    440        0.5893             nan     0.3000   -0.0023
##    460        0.5865             nan     0.3000   -0.0041
##    480        0.5841             nan     0.3000   -0.0019
##    500        0.5741             nan     0.3000   -0.0039
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2092             nan     0.3000    0.0453
##      2        1.1587             nan     0.3000    0.0208
##      3        1.1122             nan     0.3000    0.0181
##      4        1.0833             nan     0.3000    0.0125
##      5        1.0544             nan     0.3000    0.0146
##      6        1.0311             nan     0.3000    0.0092
##      7        1.0121             nan     0.3000    0.0070
##      8        1.0024             nan     0.3000   -0.0012
##      9        0.9900             nan     0.3000    0.0015
##     10        0.9826             nan     0.3000   -0.0028
##     20        0.9031             nan     0.3000   -0.0014
##     40        0.8391             nan     0.3000   -0.0012
##     60        0.8107             nan     0.3000   -0.0022
##     80        0.7871             nan     0.3000   -0.0069
##    100        0.7695             nan     0.3000   -0.0043
##    120        0.7497             nan     0.3000   -0.0019
##    140        0.7353             nan     0.3000   -0.0030
##    160        0.7238             nan     0.3000   -0.0044
##    180        0.7075             nan     0.3000   -0.0011
##    200        0.6910             nan     0.3000   -0.0032
##    220        0.6812             nan     0.3000   -0.0030
##    240        0.6691             nan     0.3000   -0.0027
##    260        0.6664             nan     0.3000   -0.0038
##    280        0.6561             nan     0.3000   -0.0040
##    300        0.6474             nan     0.3000   -0.0017
##    320        0.6401             nan     0.3000   -0.0028
##    340        0.6322             nan     0.3000   -0.0009
##    360        0.6246             nan     0.3000   -0.0018
##    380        0.6188             nan     0.3000   -0.0035
##    400        0.6099             nan     0.3000   -0.0053
##    420        0.6013             nan     0.3000   -0.0027
##    440        0.5982             nan     0.3000    0.0004
##    460        0.5888             nan     0.3000   -0.0010
##    480        0.5809             nan     0.3000   -0.0015
##    500        0.5731             nan     0.3000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1974             nan     0.3000    0.0455
##      2        1.1455             nan     0.3000    0.0227
##      3        1.1032             nan     0.3000    0.0165
##      4        1.0747             nan     0.3000    0.0135
##      5        1.0388             nan     0.3000    0.0154
##      6        1.0186             nan     0.3000    0.0091
##      7        1.0037             nan     0.3000    0.0037
##      8        0.9849             nan     0.3000    0.0071
##      9        0.9715             nan     0.3000    0.0059
##     10        0.9600             nan     0.3000    0.0030
##     20        0.8950             nan     0.3000   -0.0035
##     40        0.8489             nan     0.3000   -0.0013
##     60        0.8089             nan     0.3000   -0.0037
##     80        0.7894             nan     0.3000   -0.0031
##    100        0.7690             nan     0.3000   -0.0042
##    120        0.7577             nan     0.3000   -0.0028
##    140        0.7404             nan     0.3000   -0.0035
##    160        0.7286             nan     0.3000   -0.0009
##    180        0.7241             nan     0.3000   -0.0055
##    200        0.7013             nan     0.3000   -0.0026
##    220        0.6877             nan     0.3000   -0.0032
##    240        0.6751             nan     0.3000   -0.0031
##    260        0.6670             nan     0.3000   -0.0040
##    280        0.6594             nan     0.3000   -0.0044
##    300        0.6495             nan     0.3000   -0.0022
##    320        0.6412             nan     0.3000   -0.0047
##    340        0.6326             nan     0.3000   -0.0025
##    360        0.6248             nan     0.3000   -0.0019
##    380        0.6169             nan     0.3000   -0.0013
##    400        0.6155             nan     0.3000   -0.0021
##    420        0.6042             nan     0.3000   -0.0034
##    440        0.6000             nan     0.3000   -0.0031
##    460        0.5930             nan     0.3000   -0.0018
##    480        0.5893             nan     0.3000   -0.0019
##    500        0.5857             nan     0.3000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2004             nan     0.3000    0.0323
##      2        1.1106             nan     0.3000    0.0448
##      3        1.0646             nan     0.3000    0.0148
##      4        1.0166             nan     0.3000    0.0075
##      5        0.9859             nan     0.3000    0.0056
##      6        0.9643             nan     0.3000    0.0041
##      7        0.9475             nan     0.3000    0.0026
##      8        0.9344             nan     0.3000    0.0021
##      9        0.9112             nan     0.3000    0.0056
##     10        0.8974             nan     0.3000    0.0029
##     20        0.8216             nan     0.3000    0.0086
##     40        0.7406             nan     0.3000   -0.0001
##     60        0.6755             nan     0.3000   -0.0020
##     80        0.6258             nan     0.3000   -0.0039
##    100        0.5879             nan     0.3000   -0.0008
##    120        0.5581             nan     0.3000   -0.0062
##    140        0.5225             nan     0.3000   -0.0026
##    160        0.4923             nan     0.3000   -0.0025
##    180        0.4553             nan     0.3000   -0.0044
##    200        0.4300             nan     0.3000   -0.0020
##    220        0.4029             nan     0.3000   -0.0018
##    240        0.3835             nan     0.3000   -0.0029
##    260        0.3657             nan     0.3000   -0.0029
##    280        0.3466             nan     0.3000   -0.0019
##    300        0.3310             nan     0.3000   -0.0021
##    320        0.3159             nan     0.3000   -0.0025
##    340        0.2948             nan     0.3000   -0.0006
##    360        0.2798             nan     0.3000   -0.0020
##    380        0.2662             nan     0.3000   -0.0024
##    400        0.2520             nan     0.3000   -0.0005
##    420        0.2424             nan     0.3000   -0.0016
##    440        0.2329             nan     0.3000   -0.0009
##    460        0.2220             nan     0.3000   -0.0007
##    480        0.2109             nan     0.3000   -0.0006
##    500        0.2026             nan     0.3000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1695             nan     0.3000    0.0510
##      2        1.0950             nan     0.3000    0.0180
##      3        1.0390             nan     0.3000    0.0178
##      4        1.0111             nan     0.3000    0.0074
##      5        0.9763             nan     0.3000    0.0114
##      6        0.9571             nan     0.3000    0.0010
##      7        0.9394             nan     0.3000   -0.0010
##      8        0.9187             nan     0.3000    0.0078
##      9        0.9030             nan     0.3000    0.0008
##     10        0.9000             nan     0.3000   -0.0037
##     20        0.8409             nan     0.3000   -0.0050
##     40        0.7495             nan     0.3000   -0.0009
##     60        0.6956             nan     0.3000   -0.0082
##     80        0.6505             nan     0.3000   -0.0026
##    100        0.5986             nan     0.3000   -0.0012
##    120        0.5616             nan     0.3000   -0.0043
##    140        0.5234             nan     0.3000   -0.0044
##    160        0.4924             nan     0.3000   -0.0021
##    180        0.4711             nan     0.3000   -0.0090
##    200        0.4424             nan     0.3000   -0.0041
##    220        0.4113             nan     0.3000   -0.0032
##    240        0.3876             nan     0.3000   -0.0045
##    260        0.3758             nan     0.3000   -0.0011
##    280        0.3504             nan     0.3000   -0.0029
##    300        0.3373             nan     0.3000   -0.0015
##    320        0.3229             nan     0.3000   -0.0019
##    340        0.3057             nan     0.3000   -0.0022
##    360        0.2868             nan     0.3000   -0.0006
##    380        0.2718             nan     0.3000   -0.0021
##    400        0.2617             nan     0.3000   -0.0005
##    420        0.2483             nan     0.3000   -0.0021
##    440        0.2376             nan     0.3000   -0.0013
##    460        0.2276             nan     0.3000   -0.0010
##    480        0.2144             nan     0.3000   -0.0026
##    500        0.2026             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1804             nan     0.3000    0.0503
##      2        1.1007             nan     0.3000    0.0337
##      3        1.0459             nan     0.3000    0.0237
##      4        0.9973             nan     0.3000    0.0177
##      5        0.9693             nan     0.3000    0.0068
##      6        0.9488             nan     0.3000    0.0037
##      7        0.9330             nan     0.3000    0.0013
##      8        0.9178             nan     0.3000    0.0036
##      9        0.9096             nan     0.3000   -0.0011
##     10        0.8937             nan     0.3000    0.0055
##     20        0.8280             nan     0.3000   -0.0016
##     40        0.7435             nan     0.3000   -0.0026
##     60        0.6931             nan     0.3000   -0.0047
##     80        0.6478             nan     0.3000   -0.0037
##    100        0.6003             nan     0.3000   -0.0034
##    120        0.5682             nan     0.3000   -0.0039
##    140        0.5339             nan     0.3000   -0.0022
##    160        0.4882             nan     0.3000   -0.0025
##    180        0.4671             nan     0.3000   -0.0026
##    200        0.4366             nan     0.3000   -0.0016
##    220        0.4168             nan     0.3000   -0.0023
##    240        0.3870             nan     0.3000   -0.0040
##    260        0.3627             nan     0.3000   -0.0020
##    280        0.3414             nan     0.3000   -0.0022
##    300        0.3233             nan     0.3000   -0.0024
##    320        0.3004             nan     0.3000   -0.0042
##    340        0.2847             nan     0.3000   -0.0010
##    360        0.2660             nan     0.3000   -0.0001
##    380        0.2499             nan     0.3000   -0.0023
##    400        0.2372             nan     0.3000   -0.0022
##    420        0.2242             nan     0.3000   -0.0029
##    440        0.2163             nan     0.3000   -0.0027
##    460        0.2066             nan     0.3000   -0.0013
##    480        0.1955             nan     0.3000   -0.0009
##    500        0.1849             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1683             nan     0.3000    0.0688
##      2        1.0768             nan     0.3000    0.0345
##      3        1.0220             nan     0.3000    0.0161
##      4        0.9739             nan     0.3000    0.0152
##      5        0.9463             nan     0.3000    0.0040
##      6        0.9150             nan     0.3000    0.0055
##      7        0.8972             nan     0.3000   -0.0030
##      8        0.8821             nan     0.3000   -0.0054
##      9        0.8705             nan     0.3000   -0.0003
##     10        0.8649             nan     0.3000   -0.0056
##     20        0.7804             nan     0.3000   -0.0087
##     40        0.6498             nan     0.3000   -0.0041
##     60        0.5780             nan     0.3000   -0.0036
##     80        0.5179             nan     0.3000   -0.0030
##    100        0.4573             nan     0.3000   -0.0047
##    120        0.4052             nan     0.3000   -0.0036
##    140        0.3592             nan     0.3000   -0.0029
##    160        0.3237             nan     0.3000   -0.0013
##    180        0.2847             nan     0.3000   -0.0006
##    200        0.2611             nan     0.3000   -0.0010
##    220        0.2318             nan     0.3000   -0.0033
##    240        0.2081             nan     0.3000   -0.0022
##    260        0.1885             nan     0.3000   -0.0004
##    280        0.1754             nan     0.3000   -0.0016
##    300        0.1611             nan     0.3000   -0.0023
##    320        0.1483             nan     0.3000   -0.0018
##    340        0.1381             nan     0.3000   -0.0004
##    360        0.1247             nan     0.3000   -0.0003
##    380        0.1137             nan     0.3000   -0.0001
##    400        0.1038             nan     0.3000   -0.0006
##    420        0.0946             nan     0.3000   -0.0003
##    440        0.0875             nan     0.3000   -0.0003
##    460        0.0799             nan     0.3000   -0.0002
##    480        0.0757             nan     0.3000   -0.0011
##    500        0.0693             nan     0.3000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1668             nan     0.3000    0.0617
##      2        1.0629             nan     0.3000    0.0443
##      3        1.0027             nan     0.3000    0.0244
##      4        0.9592             nan     0.3000    0.0142
##      5        0.9377             nan     0.3000    0.0013
##      6        0.9164             nan     0.3000    0.0052
##      7        0.8947             nan     0.3000    0.0023
##      8        0.8745             nan     0.3000    0.0077
##      9        0.8590             nan     0.3000   -0.0030
##     10        0.8481             nan     0.3000   -0.0023
##     20        0.7800             nan     0.3000   -0.0022
##     40        0.6680             nan     0.3000   -0.0058
##     60        0.5844             nan     0.3000   -0.0040
##     80        0.5200             nan     0.3000   -0.0070
##    100        0.4590             nan     0.3000   -0.0028
##    120        0.4045             nan     0.3000   -0.0039
##    140        0.3490             nan     0.3000   -0.0015
##    160        0.3113             nan     0.3000   -0.0046
##    180        0.2799             nan     0.3000   -0.0020
##    200        0.2613             nan     0.3000   -0.0014
##    220        0.2389             nan     0.3000   -0.0009
##    240        0.2104             nan     0.3000   -0.0023
##    260        0.1934             nan     0.3000   -0.0008
##    280        0.1748             nan     0.3000   -0.0012
##    300        0.1607             nan     0.3000   -0.0028
##    320        0.1456             nan     0.3000   -0.0017
##    340        0.1343             nan     0.3000   -0.0013
##    360        0.1211             nan     0.3000   -0.0006
##    380        0.1115             nan     0.3000   -0.0011
##    400        0.1050             nan     0.3000   -0.0004
##    420        0.0992             nan     0.3000   -0.0023
##    440        0.0888             nan     0.3000   -0.0002
##    460        0.0810             nan     0.3000   -0.0011
##    480        0.0748             nan     0.3000   -0.0001
##    500        0.0697             nan     0.3000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1683             nan     0.3000    0.0536
##      2        1.0892             nan     0.3000    0.0280
##      3        1.0274             nan     0.3000    0.0211
##      4        0.9902             nan     0.3000    0.0145
##      5        0.9508             nan     0.3000    0.0140
##      6        0.9275             nan     0.3000    0.0039
##      7        0.9065             nan     0.3000   -0.0023
##      8        0.8936             nan     0.3000   -0.0067
##      9        0.8808             nan     0.3000   -0.0012
##     10        0.8689             nan     0.3000    0.0004
##     20        0.7803             nan     0.3000   -0.0053
##     40        0.6779             nan     0.3000   -0.0108
##     60        0.5959             nan     0.3000    0.0015
##     80        0.5400             nan     0.3000   -0.0071
##    100        0.4770             nan     0.3000   -0.0057
##    120        0.4124             nan     0.3000   -0.0032
##    140        0.3616             nan     0.3000   -0.0025
##    160        0.3263             nan     0.3000   -0.0031
##    180        0.2928             nan     0.3000   -0.0044
##    200        0.2648             nan     0.3000   -0.0035
##    220        0.2374             nan     0.3000   -0.0006
##    240        0.2161             nan     0.3000   -0.0016
##    260        0.1952             nan     0.3000   -0.0007
##    280        0.1766             nan     0.3000   -0.0021
##    300        0.1615             nan     0.3000   -0.0030
##    320        0.1417             nan     0.3000   -0.0007
##    340        0.1316             nan     0.3000   -0.0024
##    360        0.1189             nan     0.3000   -0.0011
##    380        0.1069             nan     0.3000   -0.0013
##    400        0.0962             nan     0.3000   -0.0005
##    420        0.0916             nan     0.3000   -0.0009
##    440        0.0833             nan     0.3000   -0.0011
##    460        0.0750             nan     0.3000   -0.0010
##    480        0.0701             nan     0.3000   -0.0006
##    500        0.0652             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1557             nan     0.5000    0.0673
##      2        1.0933             nan     0.5000    0.0190
##      3        1.0732             nan     0.5000   -0.0057
##      4        1.0299             nan     0.5000    0.0227
##      5        0.9952             nan     0.5000    0.0121
##      6        0.9598             nan     0.5000    0.0045
##      7        0.9486             nan     0.5000   -0.0059
##      8        0.9392             nan     0.5000    0.0002
##      9        0.9294             nan     0.5000   -0.0009
##     10        0.9238             nan     0.5000   -0.0059
##     20        0.8731             nan     0.5000    0.0005
##     40        0.8129             nan     0.5000    0.0024
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000   -0.0002
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000   -0.0025
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1553             nan     0.5000    0.0597
##      2        1.1032             nan     0.5000    0.0240
##      3        1.0552             nan     0.5000    0.0211
##      4        1.0164             nan     0.5000    0.0144
##      5        0.9962             nan     0.5000    0.0019
##      6        0.9580             nan     0.5000    0.0145
##      7        0.9465             nan     0.5000    0.0010
##      8        0.9307             nan     0.5000    0.0030
##      9        0.9154             nan     0.5000    0.0001
##     10        0.9047             nan     0.5000   -0.0085
##     20        0.8502             nan     0.5000   -0.0003
##     40        0.7946             nan     0.5000   -0.0047
##     60        0.7704             nan     0.5000   -0.0013
##     80        0.7437             nan     0.5000   -0.0045
##    100        0.7158             nan     0.5000   -0.0027
##    120        0.7051             nan     0.5000   -0.0085
##    140        0.6809             nan     0.5000   -0.0059
##    160        0.6626             nan     0.5000   -0.0049
##    180        0.6553             nan     0.5000   -0.0060
##    200        0.6423             nan     0.5000   -0.0044
##    220        0.6273             nan     0.5000   -0.0030
##    240        0.6183             nan     0.5000   -0.0079
##    260        0.6097             nan     0.5000   -0.0052
##    280        0.5930             nan     0.5000   -0.0048
##    300        0.5805             nan     0.5000   -0.0023
##    320        0.5711             nan     0.5000   -0.0021
##    340        0.5676             nan     0.5000   -0.0051
##    360        0.5600             nan     0.5000   -0.0046
##    380        0.5618             nan     0.5000   -0.0068
##    400        0.5401             nan     0.5000   -0.0035
##    420        0.5343             nan     0.5000   -0.0036
##    440        0.5282             nan     0.5000   -0.0049
##    460        0.5227             nan     0.5000   -0.0040
##    480        0.5065             nan     0.5000   -0.0032
##    500        0.5008             nan     0.5000   -0.0051
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1442             nan     0.5000    0.0527
##      2        1.0786             nan     0.5000    0.0139
##      3        1.0369             nan     0.5000    0.0203
##      4        1.0004             nan     0.5000    0.0165
##      5        0.9658             nan     0.5000    0.0135
##      6        0.9516             nan     0.5000    0.0003
##      7        0.9360             nan     0.5000    0.0004
##      8        0.9248             nan     0.5000   -0.0011
##      9        0.9225             nan     0.5000   -0.0064
##     10        0.9157             nan     0.5000    0.0004
##     20        0.8457             nan     0.5000   -0.0065
##     40        0.8059             nan     0.5000   -0.0074
##     60        0.7879             nan     0.5000   -0.0049
##     80        0.7552             nan     0.5000   -0.0032
##    100        0.7289             nan     0.5000   -0.0074
##    120        0.7165             nan     0.5000   -0.0062
##    140        0.7071             nan     0.5000   -0.0078
##    160        0.6998             nan     0.5000   -0.0075
##    180        0.6706             nan     0.5000   -0.0013
##    200        0.6597             nan     0.5000   -0.0133
##    220        0.6486             nan     0.5000   -0.0025
##    240        0.6409             nan     0.5000   -0.0074
##    260        0.6270             nan     0.5000   -0.0013
##    280        0.6123             nan     0.5000   -0.0082
##    300        0.5940             nan     0.5000   -0.0038
##    320        0.5876             nan     0.5000   -0.0027
##    340        0.5774             nan     0.5000   -0.0058
##    360        0.5706             nan     0.5000   -0.0041
##    380        0.5647             nan     0.5000   -0.0035
##    400        0.5576             nan     0.5000   -0.0029
##    420        0.5478             nan     0.5000   -0.0041
##    440        0.5314             nan     0.5000   -0.0012
##    460        0.5225             nan     0.5000   -0.0070
##    480        0.5190             nan     0.5000   -0.0032
##    500        0.5019             nan     0.5000   -0.0043
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1306             nan     0.5000    0.0841
##      2        1.0494             nan     0.5000    0.0320
##      3        1.0176             nan     0.5000    0.0026
##      4        0.9675             nan     0.5000    0.0150
##      5        0.9429             nan     0.5000    0.0036
##      6        0.9242             nan     0.5000    0.0036
##      7        0.9115             nan     0.5000   -0.0047
##      8        0.8910             nan     0.5000   -0.0057
##      9        0.8719             nan     0.5000   -0.0029
##     10        0.8688             nan     0.5000   -0.0115
##     20        0.7969             nan     0.5000   -0.0046
##     40        0.9901             nan     0.5000   -0.0125
##     60        0.8761             nan     0.5000   -0.0009
##     80           inf             nan     0.5000   -0.1296
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1248             nan     0.5000    0.0700
##      2        1.0281             nan     0.5000    0.0435
##      3        0.9841             nan     0.5000    0.0092
##      4        0.9432             nan     0.5000    0.0070
##      5        0.9349             nan     0.5000   -0.0085
##      6        0.9145             nan     0.5000   -0.0066
##      7        0.8885             nan     0.5000    0.0046
##      8        0.8662             nan     0.5000    0.0041
##      9        0.8544             nan     0.5000   -0.0003
##     10        0.8439             nan     0.5000    0.0002
##     20        0.7804             nan     0.5000   -0.0042
##     40        0.6877             nan     0.5000   -0.0017
##     60        0.6104             nan     0.5000   -0.0045
##     80        0.5556             nan     0.5000   -0.0087
##    100        0.5086             nan     0.5000   -0.0041
##    120        0.4594             nan     0.5000   -0.0115
##    140        0.4226             nan     0.5000   -0.0084
##    160        0.3852             nan     0.5000   -0.0070
##    180        0.3395             nan     0.5000   -0.0047
##    200        0.3040             nan     0.5000   -0.0023
##    220        0.2774             nan     0.5000   -0.0057
##    240        0.2458             nan     0.5000   -0.0015
##    260        0.2154             nan     0.5000   -0.0017
##    280        0.1922             nan     0.5000   -0.0002
##    300        0.1781             nan     0.5000   -0.0012
##    320        0.1651             nan     0.5000   -0.0008
##    340        0.1469             nan     0.5000   -0.0038
##    360        0.1354             nan     0.5000   -0.0011
##    380        0.1235             nan     0.5000   -0.0020
##    400        0.1153             nan     0.5000   -0.0002
##    420        0.1058             nan     0.5000   -0.0006
##    440        0.0970             nan     0.5000   -0.0022
##    460        0.0889             nan     0.5000   -0.0018
##    480        0.0825             nan     0.5000   -0.0008
##    500        0.0764             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1146             nan     0.5000    0.0776
##      2        1.0287             nan     0.5000    0.0304
##      3        0.9681             nan     0.5000    0.0139
##      4        0.9413             nan     0.5000    0.0069
##      5        0.9300             nan     0.5000   -0.0091
##      6        0.9205             nan     0.5000   -0.0155
##      7        0.8946             nan     0.5000    0.0020
##      8        0.8758             nan     0.5000    0.0007
##      9        0.8681             nan     0.5000   -0.0111
##     10        0.8595             nan     0.5000   -0.0082
##     20        0.8548             nan     0.5000   -0.0156
##     40        0.7634             nan     0.5000   -0.0065
##     60        0.6608             nan     0.5000   -0.0029
##     80        0.5694             nan     0.5000   -0.0039
##    100        0.5021             nan     0.5000   -0.0062
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1055             nan     0.5000    0.0699
##      2        1.0284             nan     0.5000    0.0202
##      3        0.9820             nan     0.5000    0.0101
##      4        0.9338             nan     0.5000    0.0143
##      5        0.9131             nan     0.5000   -0.0032
##      6        0.8937             nan     0.5000   -0.0071
##      7        0.8873             nan     0.5000   -0.0176
##      8        0.8706             nan     0.5000   -0.0082
##      9        0.8681             nan     0.5000   -0.0143
##     10        0.8581             nan     0.5000   -0.0049
##     20        0.7503             nan     0.5000   -0.0088
##     40        0.5897             nan     0.5000   -0.0155
##     60        0.5431             nan     0.5000   -0.0079
##     80        0.4930             nan     0.5000   -0.0014
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0821             nan     0.5000    0.0868
##      2        0.9915             nan     0.5000    0.0335
##      3        0.9423             nan     0.5000    0.0122
##      4        0.9147             nan     0.5000    0.0023
##      5        0.8823             nan     0.5000    0.0033
##      6        0.8633             nan     0.5000   -0.0071
##      7        0.8446             nan     0.5000   -0.0086
##      8        0.8291             nan     0.5000   -0.0048
##      9        0.8221             nan     0.5000   -0.0123
##     10        0.8061             nan     0.5000   -0.0081
##     20        0.7246             nan     0.5000   -0.0098
##     40        0.6040             nan     0.5000   -0.0078
##     60        0.4750             nan     0.5000   -0.0050
##     80        0.4091             nan     0.5000   -0.0099
##    100        0.3297             nan     0.5000   -0.0037
##    120        0.9646             nan     0.5000   -0.0030
##    140        0.8291             nan     0.5000   -0.0013
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000   -0.0015
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000   -0.0015
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000   -0.0007
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0945             nan     0.5000    0.0863
##      2        0.9933             nan     0.5000    0.0369
##      3        0.9526             nan     0.5000    0.0037
##      4        0.9145             nan     0.5000    0.0081
##      5        0.8902             nan     0.5000    0.0001
##      6        0.8647             nan     0.5000   -0.0009
##      7        0.8448             nan     0.5000    0.0025
##      8        0.8371             nan     0.5000   -0.0088
##      9        0.8258             nan     0.5000   -0.0090
##     10        0.8093             nan     0.5000   -0.0031
##     20        0.7091             nan     0.5000   -0.0053
##     40        0.5853             nan     0.5000   -0.0103
##     60        0.4806             nan     0.5000   -0.0034
##     80        0.3814             nan     0.5000   -0.0085
##    100        0.3095             nan     0.5000   -0.0074
##    120        0.2530             nan     0.5000   -0.0007
##    140        0.2102             nan     0.5000   -0.0029
##    160        0.1794             nan     0.5000   -0.0009
##    180        0.1539             nan     0.5000   -0.0004
##    200        0.1341             nan     0.5000   -0.0032
##    220        0.1129             nan     0.5000   -0.0016
##    240        0.0989             nan     0.5000   -0.0031
##    260        0.0884             nan     0.5000   -0.0010
##    280        0.0765             nan     0.5000   -0.0012
##    300        0.0662             nan     0.5000   -0.0015
##    320        0.0592             nan     0.5000   -0.0010
##    340        0.0511             nan     0.5000   -0.0011
##    360        0.0445             nan     0.5000   -0.0009
##    380        0.0392             nan     0.5000   -0.0000
##    400        0.0337             nan     0.5000   -0.0004
##    420        0.0310             nan     0.5000   -0.0005
##    440        0.0276             nan     0.5000   -0.0004
##    460        0.0237             nan     0.5000   -0.0003
##    480        0.0211             nan     0.5000   -0.0003
##    500        0.0189             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1263             nan     1.0000    0.0821
##      2        1.0685             nan     1.0000    0.0063
##      3        1.0150             nan     1.0000    0.0139
##      4        0.9791             nan     1.0000    0.0106
##      5        1.0292             nan     1.0000   -0.0619
##      6        1.0154             nan     1.0000   -0.0046
##      7        1.0102             nan     1.0000   -0.0103
##      8        1.0092             nan     1.0000   -0.0170
##      9        1.0389             nan     1.0000   -0.0212
##     10        1.0349             nan     1.0000   -0.0115
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1161             nan     1.0000    0.0782
##      2        1.0603             nan     1.0000   -0.0070
##      3        1.0098             nan     1.0000    0.0242
##      4        1.0343             nan     1.0000   -0.0455
##      5        1.0140             nan     1.0000   -0.0025
##      6        0.9956             nan     1.0000    0.0011
##      7        0.9915             nan     1.0000   -0.0264
##      8        0.9977             nan     1.0000   -0.0206
##      9        1.0166             nan     1.0000   -0.0389
##     10        1.0087             nan     1.0000   -0.0290
##     20 937493056.1750             nan     1.0000    0.0077
##     40 937493058.6785             nan     1.0000   -0.0312
##     60 937493058.6082             nan     1.0000   -0.0013
##     80 937493058.5094             nan     1.0000   -0.0072
##    100 937493078.2023             nan     1.0000   -0.0448
##    120 937493078.2081             nan     1.0000   -0.0171
##    140 373204708981721595924.0000             nan     1.0000    0.0033
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360 1825765563213561993860248620806022228600224226004466826428802284066484048262040488600288206248226066822084026862024040884062260206866442228820404862446202266400684246466002442802824260480462040604868488664420884600626062.0000             nan     1.0000    0.0007
##    380 1825765563213561993860248620806022228600224226004466826428802284066484048262040488600288206248226066822084026862024040884062260206866442228820404862446202266400684246466002442802824260480462040604868488664420884600626062.0000             nan     1.0000  -38.0565
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1245             nan     1.0000    0.0501
##      2        1.0548             nan     1.0000    0.0316
##      3        1.0425             nan     1.0000   -0.0129
##      4        1.0162             nan     1.0000    0.0091
##      5        0.9983             nan     1.0000   -0.0046
##      6        0.9936             nan     1.0000   -0.0161
##      7        0.9751             nan     1.0000    0.0002
##      8        0.9632             nan     1.0000    0.0001
##      9        0.9603             nan     1.0000   -0.0141
##     10        0.9538             nan     1.0000   -0.0137
##     20        0.9024             nan     1.0000   -0.0132
##     40        0.8625             nan     1.0000   -0.0274
##     60        0.7845             nan     1.0000   -0.0264
##     80        0.7300             nan     1.0000    0.0000
##    100        0.7396             nan     1.0000   -0.0225
##    120        0.7152             nan     1.0000   -0.0361
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000             nan     1.0000   -0.0361
##    240 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000             nan     1.0000   -0.0000
##    260 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000             nan     1.0000    0.0020
##    280 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000             nan     1.0000   -0.0830
##    300 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000             nan     1.0000    0.0119
##    320 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000             nan     1.0000   -0.0268
##    340 11654420909069658208086828200668440080800400822686848880086002868404666084862446868668006084800644608280222086860468282220880886280468202044622462460680660264086604884280420642860026664008442804208602620248664640422246806888408060406082000408866.0000             nan     1.0000   -0.0220
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0747             nan     1.0000    0.1075
##      2        1.0179             nan     1.0000    0.0039
##      3        0.9734             nan     1.0000    0.0091
##      4        0.9783             nan     1.0000   -0.0329
##      5        0.9971             nan     1.0000   -0.0625
##      6        3.9797             nan     1.0000   -3.0321
##      7        3.9466             nan     1.0000   -0.0090
##      8        3.9457             nan     1.0000   -0.0193
##      9        3.9275             nan     1.0000   -0.0167
##     10        3.9205             nan     1.0000   -0.0032
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0744             nan     1.0000    0.0851
##      2        0.9750             nan     1.0000    0.0350
##      3        0.9513             nan     1.0000   -0.0026
##      4        0.9451             nan     1.0000   -0.0117
##      5        0.9262             nan     1.0000   -0.0056
##      6        0.9423             nan     1.0000   -0.0377
##      7        0.9091             nan     1.0000    0.0038
##      8        0.9226             nan     1.0000   -0.0361
##      9        0.9146             nan     1.0000   -0.0208
##     10        0.8934             nan     1.0000   -0.0123
##     20        2.0729             nan     1.0000   -0.0522
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0801             nan     1.0000    0.1057
##      2        1.0156             nan     1.0000    0.0185
##      3        0.9622             nan     1.0000    0.0042
##      4        0.9342             nan     1.0000   -0.0214
##      5        0.9181             nan     1.0000   -0.0239
##      6        0.9295             nan     1.0000   -0.0536
##      7        0.9127             nan     1.0000   -0.0249
##      8        0.9131             nan     1.0000   -0.0358
##      9        0.9638             nan     1.0000   -0.0796
##     10        0.9257             nan     1.0000    0.0131
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0551             nan     1.0000    0.0744
##      2        0.9964             nan     1.0000   -0.0098
##      3        0.9518             nan     1.0000   -0.0190
##      4        0.9897             nan     1.0000   -0.0793
##      5        0.9569             nan     1.0000   -0.0056
##      6        1.0045             nan     1.0000   -0.0817
##      7        1.0051             nan     1.0000   -0.0547
##      8        0.9842             nan     1.0000   -0.0330
##      9        0.9843             nan     1.0000   -0.0490
##     10        0.9591             nan     1.0000   -0.0180
##     20        1.5144             nan     1.0000   -0.0437
##     40     1284.4598             nan     1.0000   -0.0295
##     60 38681437849941152112000468044028060646208684220488828048642048886428204.0000             nan     1.0000   -0.0216
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000 -250.3232
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0586             nan     1.0000    0.0904
##      2        0.9605             nan     1.0000    0.0344
##      3        1.0113             nan     1.0000   -0.0978
##      4        0.9842             nan     1.0000   -0.0016
##      5        0.9495             nan     1.0000   -0.0077
##      6        0.9606             nan     1.0000   -0.0401
##      7        1.0022             nan     1.0000   -0.0782
##      8        0.9570             nan     1.0000   -0.0058
##      9        0.9690             nan     1.0000   -0.0588
##     10        1.1244             nan     1.0000   -0.2113
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0652             nan     1.0000    0.0907
##      2        1.0248             nan     1.0000   -0.0400
##      3        0.9759             nan     1.0000   -0.0401
##      4        0.9214             nan     1.0000    0.0114
##      5        0.9494             nan     1.0000   -0.0483
##      6        0.9560             nan     1.0000   -0.0198
##      7        0.9827             nan     1.0000   -0.0910
##      8        1.1044             nan     1.0000   -0.1859
##      9        1.1145             nan     1.0000   -0.0336
##     10        1.1529             nan     1.0000   -0.0815
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0001
##     20        1.2859             nan     0.0010    0.0002
##     40        1.2787             nan     0.0010    0.0002
##     60        1.2719             nan     0.0010    0.0002
##     80        1.2653             nan     0.0010    0.0001
##    100        1.2587             nan     0.0010    0.0001
##    120        1.2525             nan     0.0010    0.0001
##    140        1.2466             nan     0.0010    0.0001
##    160        1.2405             nan     0.0010    0.0001
##    180        1.2349             nan     0.0010    0.0001
##    200        1.2293             nan     0.0010    0.0001
##    220        1.2243             nan     0.0010    0.0001
##    240        1.2192             nan     0.0010    0.0001
##    260        1.2142             nan     0.0010    0.0001
##    280        1.2091             nan     0.0010    0.0001
##    300        1.2043             nan     0.0010    0.0001
##    320        1.1997             nan     0.0010    0.0001
##    340        1.1952             nan     0.0010    0.0001
##    360        1.1907             nan     0.0010    0.0001
##    380        1.1866             nan     0.0010    0.0001
##    400        1.1824             nan     0.0010    0.0001
##    420        1.1783             nan     0.0010    0.0001
##    440        1.1743             nan     0.0010    0.0001
##    460        1.1704             nan     0.0010    0.0001
##    480        1.1665             nan     0.0010    0.0001
##    500        1.1628             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2790             nan     0.0010    0.0002
##     60        1.2720             nan     0.0010    0.0002
##     80        1.2655             nan     0.0010    0.0001
##    100        1.2592             nan     0.0010    0.0001
##    120        1.2528             nan     0.0010    0.0001
##    140        1.2468             nan     0.0010    0.0001
##    160        1.2408             nan     0.0010    0.0001
##    180        1.2352             nan     0.0010    0.0001
##    200        1.2297             nan     0.0010    0.0001
##    220        1.2243             nan     0.0010    0.0001
##    240        1.2191             nan     0.0010    0.0001
##    260        1.2141             nan     0.0010    0.0001
##    280        1.2090             nan     0.0010    0.0001
##    300        1.2043             nan     0.0010    0.0001
##    320        1.1997             nan     0.0010    0.0001
##    340        1.1952             nan     0.0010    0.0001
##    360        1.1907             nan     0.0010    0.0001
##    380        1.1864             nan     0.0010    0.0001
##    400        1.1823             nan     0.0010    0.0001
##    420        1.1783             nan     0.0010    0.0001
##    440        1.1743             nan     0.0010    0.0001
##    460        1.1705             nan     0.0010    0.0001
##    480        1.1666             nan     0.0010    0.0001
##    500        1.1629             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2860             nan     0.0010    0.0002
##     40        1.2785             nan     0.0010    0.0002
##     60        1.2716             nan     0.0010    0.0002
##     80        1.2650             nan     0.0010    0.0002
##    100        1.2585             nan     0.0010    0.0002
##    120        1.2522             nan     0.0010    0.0001
##    140        1.2461             nan     0.0010    0.0001
##    160        1.2401             nan     0.0010    0.0001
##    180        1.2344             nan     0.0010    0.0001
##    200        1.2292             nan     0.0010    0.0001
##    220        1.2239             nan     0.0010    0.0001
##    240        1.2187             nan     0.0010    0.0001
##    260        1.2136             nan     0.0010    0.0001
##    280        1.2089             nan     0.0010    0.0001
##    300        1.2040             nan     0.0010    0.0001
##    320        1.1994             nan     0.0010    0.0001
##    340        1.1948             nan     0.0010    0.0001
##    360        1.1905             nan     0.0010    0.0001
##    380        1.1863             nan     0.0010    0.0001
##    400        1.1821             nan     0.0010    0.0001
##    420        1.1779             nan     0.0010    0.0001
##    440        1.1739             nan     0.0010    0.0001
##    460        1.1700             nan     0.0010    0.0001
##    480        1.1663             nan     0.0010    0.0001
##    500        1.1625             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0003
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2889             nan     0.0010    0.0002
##     10        1.2883             nan     0.0010    0.0002
##     20        1.2837             nan     0.0010    0.0002
##     40        1.2743             nan     0.0010    0.0002
##     60        1.2653             nan     0.0010    0.0002
##     80        1.2567             nan     0.0010    0.0002
##    100        1.2482             nan     0.0010    0.0002
##    120        1.2399             nan     0.0010    0.0002
##    140        1.2319             nan     0.0010    0.0001
##    160        1.2241             nan     0.0010    0.0002
##    180        1.2164             nan     0.0010    0.0002
##    200        1.2089             nan     0.0010    0.0002
##    220        1.2018             nan     0.0010    0.0002
##    240        1.1951             nan     0.0010    0.0002
##    260        1.1881             nan     0.0010    0.0001
##    280        1.1817             nan     0.0010    0.0001
##    300        1.1755             nan     0.0010    0.0001
##    320        1.1694             nan     0.0010    0.0001
##    340        1.1634             nan     0.0010    0.0001
##    360        1.1575             nan     0.0010    0.0001
##    380        1.1517             nan     0.0010    0.0001
##    400        1.1464             nan     0.0010    0.0001
##    420        1.1411             nan     0.0010    0.0001
##    440        1.1359             nan     0.0010    0.0001
##    460        1.1309             nan     0.0010    0.0001
##    480        1.1258             nan     0.0010    0.0001
##    500        1.1209             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2889             nan     0.0010    0.0002
##     10        1.2884             nan     0.0010    0.0002
##     20        1.2835             nan     0.0010    0.0002
##     40        1.2741             nan     0.0010    0.0002
##     60        1.2652             nan     0.0010    0.0002
##     80        1.2564             nan     0.0010    0.0002
##    100        1.2478             nan     0.0010    0.0002
##    120        1.2398             nan     0.0010    0.0002
##    140        1.2317             nan     0.0010    0.0002
##    160        1.2241             nan     0.0010    0.0002
##    180        1.2168             nan     0.0010    0.0002
##    200        1.2092             nan     0.0010    0.0001
##    220        1.2020             nan     0.0010    0.0002
##    240        1.1951             nan     0.0010    0.0001
##    260        1.1884             nan     0.0010    0.0001
##    280        1.1820             nan     0.0010    0.0002
##    300        1.1756             nan     0.0010    0.0001
##    320        1.1697             nan     0.0010    0.0001
##    340        1.1637             nan     0.0010    0.0001
##    360        1.1579             nan     0.0010    0.0001
##    380        1.1521             nan     0.0010    0.0001
##    400        1.1465             nan     0.0010    0.0001
##    420        1.1411             nan     0.0010    0.0001
##    440        1.1358             nan     0.0010    0.0001
##    460        1.1306             nan     0.0010    0.0001
##    480        1.1253             nan     0.0010    0.0001
##    500        1.1203             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2839             nan     0.0010    0.0002
##     40        1.2745             nan     0.0010    0.0002
##     60        1.2653             nan     0.0010    0.0002
##     80        1.2564             nan     0.0010    0.0002
##    100        1.2481             nan     0.0010    0.0002
##    120        1.2398             nan     0.0010    0.0002
##    140        1.2317             nan     0.0010    0.0002
##    160        1.2241             nan     0.0010    0.0002
##    180        1.2166             nan     0.0010    0.0001
##    200        1.2093             nan     0.0010    0.0002
##    220        1.2023             nan     0.0010    0.0001
##    240        1.1954             nan     0.0010    0.0002
##    260        1.1888             nan     0.0010    0.0002
##    280        1.1825             nan     0.0010    0.0001
##    300        1.1761             nan     0.0010    0.0001
##    320        1.1699             nan     0.0010    0.0001
##    340        1.1640             nan     0.0010    0.0001
##    360        1.1582             nan     0.0010    0.0001
##    380        1.1526             nan     0.0010    0.0001
##    400        1.1469             nan     0.0010    0.0001
##    420        1.1415             nan     0.0010    0.0001
##    440        1.1362             nan     0.0010    0.0001
##    460        1.1310             nan     0.0010    0.0001
##    480        1.1259             nan     0.0010    0.0001
##    500        1.1211             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0003
##      6        1.2899             nan     0.0010    0.0003
##      7        1.2894             nan     0.0010    0.0003
##      8        1.2888             nan     0.0010    0.0003
##      9        1.2883             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0002
##     20        1.2823             nan     0.0010    0.0003
##     40        1.2717             nan     0.0010    0.0002
##     60        1.2612             nan     0.0010    0.0002
##     80        1.2512             nan     0.0010    0.0002
##    100        1.2414             nan     0.0010    0.0002
##    120        1.2318             nan     0.0010    0.0002
##    140        1.2228             nan     0.0010    0.0002
##    160        1.2142             nan     0.0010    0.0002
##    180        1.2055             nan     0.0010    0.0002
##    200        1.1969             nan     0.0010    0.0002
##    220        1.1888             nan     0.0010    0.0001
##    240        1.1810             nan     0.0010    0.0002
##    260        1.1735             nan     0.0010    0.0002
##    280        1.1659             nan     0.0010    0.0001
##    300        1.1585             nan     0.0010    0.0002
##    320        1.1514             nan     0.0010    0.0001
##    340        1.1447             nan     0.0010    0.0001
##    360        1.1382             nan     0.0010    0.0002
##    380        1.1318             nan     0.0010    0.0001
##    400        1.1253             nan     0.0010    0.0001
##    420        1.1192             nan     0.0010    0.0001
##    440        1.1133             nan     0.0010    0.0001
##    460        1.1072             nan     0.0010    0.0001
##    480        1.1018             nan     0.0010    0.0001
##    500        1.0963             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2905             nan     0.0010    0.0002
##      6        1.2899             nan     0.0010    0.0003
##      7        1.2894             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0002
##      9        1.2883             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2823             nan     0.0010    0.0003
##     40        1.2716             nan     0.0010    0.0002
##     60        1.2611             nan     0.0010    0.0002
##     80        1.2507             nan     0.0010    0.0002
##    100        1.2409             nan     0.0010    0.0002
##    120        1.2315             nan     0.0010    0.0002
##    140        1.2226             nan     0.0010    0.0002
##    160        1.2136             nan     0.0010    0.0002
##    180        1.2050             nan     0.0010    0.0002
##    200        1.1966             nan     0.0010    0.0002
##    220        1.1884             nan     0.0010    0.0002
##    240        1.1805             nan     0.0010    0.0002
##    260        1.1732             nan     0.0010    0.0002
##    280        1.1658             nan     0.0010    0.0001
##    300        1.1588             nan     0.0010    0.0002
##    320        1.1515             nan     0.0010    0.0002
##    340        1.1447             nan     0.0010    0.0002
##    360        1.1382             nan     0.0010    0.0001
##    380        1.1319             nan     0.0010    0.0001
##    400        1.1255             nan     0.0010    0.0001
##    420        1.1193             nan     0.0010    0.0001
##    440        1.1132             nan     0.0010    0.0001
##    460        1.1076             nan     0.0010    0.0001
##    480        1.1021             nan     0.0010    0.0001
##    500        1.0964             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2916             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2905             nan     0.0010    0.0003
##      6        1.2899             nan     0.0010    0.0002
##      7        1.2894             nan     0.0010    0.0003
##      8        1.2888             nan     0.0010    0.0002
##      9        1.2883             nan     0.0010    0.0002
##     10        1.2876             nan     0.0010    0.0003
##     20        1.2821             nan     0.0010    0.0003
##     40        1.2715             nan     0.0010    0.0002
##     60        1.2612             nan     0.0010    0.0002
##     80        1.2508             nan     0.0010    0.0003
##    100        1.2409             nan     0.0010    0.0002
##    120        1.2316             nan     0.0010    0.0002
##    140        1.2224             nan     0.0010    0.0002
##    160        1.2134             nan     0.0010    0.0002
##    180        1.2048             nan     0.0010    0.0002
##    200        1.1965             nan     0.0010    0.0002
##    220        1.1883             nan     0.0010    0.0002
##    240        1.1805             nan     0.0010    0.0002
##    260        1.1727             nan     0.0010    0.0002
##    280        1.1654             nan     0.0010    0.0001
##    300        1.1582             nan     0.0010    0.0002
##    320        1.1509             nan     0.0010    0.0001
##    340        1.1440             nan     0.0010    0.0001
##    360        1.1374             nan     0.0010    0.0002
##    380        1.1309             nan     0.0010    0.0001
##    400        1.1247             nan     0.0010    0.0001
##    420        1.1186             nan     0.0010    0.0001
##    440        1.1125             nan     0.0010    0.0001
##    460        1.1069             nan     0.0010    0.0001
##    480        1.1011             nan     0.0010    0.0001
##    500        1.0956             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2590             nan     0.1000    0.0166
##      2        1.2316             nan     0.1000    0.0111
##      3        1.2054             nan     0.1000    0.0099
##      4        1.1805             nan     0.1000    0.0104
##      5        1.1639             nan     0.1000    0.0080
##      6        1.1444             nan     0.1000    0.0075
##      7        1.1299             nan     0.1000    0.0070
##      8        1.1133             nan     0.1000    0.0056
##      9        1.0992             nan     0.1000    0.0053
##     10        1.0882             nan     0.1000    0.0056
##     20        0.9982             nan     0.1000    0.0029
##     40        0.9185             nan     0.1000   -0.0003
##     60        0.8754             nan     0.1000   -0.0007
##     80        0.8446             nan     0.1000   -0.0008
##    100        0.8232             nan     0.1000    0.0002
##    120        0.8133             nan     0.1000   -0.0003
##    140        0.7992             nan     0.1000   -0.0004
##    160        0.7912             nan     0.1000   -0.0022
##    180        0.7800             nan     0.1000   -0.0021
##    200        0.7712             nan     0.1000   -0.0020
##    220        0.7656             nan     0.1000   -0.0026
##    240        0.7599             nan     0.1000   -0.0006
##    260        0.7509             nan     0.1000    0.0003
##    280        0.7439             nan     0.1000   -0.0012
##    300        0.7382             nan     0.1000   -0.0004
##    320        0.7328             nan     0.1000   -0.0005
##    340        0.7253             nan     0.1000   -0.0007
##    360        0.7202             nan     0.1000   -0.0008
##    380        0.7135             nan     0.1000   -0.0006
##    400        0.7065             nan     0.1000   -0.0007
##    420        0.7028             nan     0.1000   -0.0010
##    440        0.6965             nan     0.1000   -0.0007
##    460        0.6912             nan     0.1000   -0.0002
##    480        0.6877             nan     0.1000   -0.0018
##    500        0.6830             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2618             nan     0.1000    0.0160
##      2        1.2290             nan     0.1000    0.0147
##      3        1.2035             nan     0.1000    0.0110
##      4        1.1818             nan     0.1000    0.0076
##      5        1.1604             nan     0.1000    0.0079
##      6        1.1428             nan     0.1000    0.0064
##      7        1.1275             nan     0.1000    0.0062
##      8        1.1123             nan     0.1000    0.0071
##      9        1.0988             nan     0.1000    0.0056
##     10        1.0846             nan     0.1000    0.0049
##     20        0.9896             nan     0.1000    0.0025
##     40        0.9057             nan     0.1000    0.0007
##     60        0.8616             nan     0.1000   -0.0007
##     80        0.8389             nan     0.1000   -0.0002
##    100        0.8189             nan     0.1000   -0.0017
##    120        0.8013             nan     0.1000   -0.0004
##    140        0.7870             nan     0.1000   -0.0015
##    160        0.7774             nan     0.1000   -0.0010
##    180        0.7670             nan     0.1000   -0.0006
##    200        0.7585             nan     0.1000   -0.0006
##    220        0.7512             nan     0.1000   -0.0012
##    240        0.7426             nan     0.1000   -0.0008
##    260        0.7365             nan     0.1000   -0.0007
##    280        0.7292             nan     0.1000   -0.0010
##    300        0.7241             nan     0.1000   -0.0006
##    320        0.7204             nan     0.1000   -0.0014
##    340        0.7159             nan     0.1000   -0.0019
##    360        0.7093             nan     0.1000   -0.0000
##    380        0.7048             nan     0.1000   -0.0008
##    400        0.7002             nan     0.1000   -0.0009
##    420        0.6960             nan     0.1000   -0.0004
##    440        0.6919             nan     0.1000   -0.0011
##    460        0.6877             nan     0.1000   -0.0002
##    480        0.6830             nan     0.1000   -0.0010
##    500        0.6789             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2564             nan     0.1000    0.0171
##      2        1.2244             nan     0.1000    0.0149
##      3        1.1952             nan     0.1000    0.0125
##      4        1.1721             nan     0.1000    0.0089
##      5        1.1514             nan     0.1000    0.0070
##      6        1.1322             nan     0.1000    0.0074
##      7        1.1175             nan     0.1000    0.0043
##      8        1.1047             nan     0.1000    0.0037
##      9        1.0902             nan     0.1000    0.0070
##     10        1.0791             nan     0.1000    0.0033
##     20        0.9885             nan     0.1000    0.0004
##     40        0.9134             nan     0.1000    0.0012
##     60        0.8702             nan     0.1000    0.0008
##     80        0.8419             nan     0.1000   -0.0003
##    100        0.8221             nan     0.1000   -0.0007
##    120        0.8067             nan     0.1000   -0.0009
##    140        0.7974             nan     0.1000   -0.0006
##    160        0.7861             nan     0.1000   -0.0003
##    180        0.7755             nan     0.1000   -0.0011
##    200        0.7688             nan     0.1000   -0.0012
##    220        0.7612             nan     0.1000   -0.0010
##    240        0.7555             nan     0.1000   -0.0016
##    260        0.7506             nan     0.1000   -0.0009
##    280        0.7461             nan     0.1000   -0.0015
##    300        0.7396             nan     0.1000   -0.0006
##    320        0.7337             nan     0.1000   -0.0008
##    340        0.7278             nan     0.1000   -0.0011
##    360        0.7198             nan     0.1000   -0.0009
##    380        0.7132             nan     0.1000   -0.0009
##    400        0.7092             nan     0.1000   -0.0007
##    420        0.7052             nan     0.1000   -0.0017
##    440        0.6991             nan     0.1000   -0.0006
##    460        0.6950             nan     0.1000   -0.0008
##    480        0.6899             nan     0.1000   -0.0006
##    500        0.6866             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2459             nan     0.1000    0.0222
##      2        1.2073             nan     0.1000    0.0201
##      3        1.1742             nan     0.1000    0.0097
##      4        1.1448             nan     0.1000    0.0139
##      5        1.1197             nan     0.1000    0.0125
##      6        1.0963             nan     0.1000    0.0090
##      7        1.0777             nan     0.1000    0.0055
##      8        1.0578             nan     0.1000    0.0080
##      9        1.0433             nan     0.1000    0.0050
##     10        1.0268             nan     0.1000    0.0074
##     20        0.9320             nan     0.1000   -0.0007
##     40        0.8362             nan     0.1000    0.0006
##     60        0.7838             nan     0.1000   -0.0007
##     80        0.7476             nan     0.1000   -0.0004
##    100        0.7232             nan     0.1000   -0.0007
##    120        0.7009             nan     0.1000   -0.0011
##    140        0.6732             nan     0.1000   -0.0002
##    160        0.6510             nan     0.1000   -0.0006
##    180        0.6315             nan     0.1000   -0.0014
##    200        0.6132             nan     0.1000   -0.0012
##    220        0.5958             nan     0.1000   -0.0014
##    240        0.5758             nan     0.1000   -0.0014
##    260        0.5616             nan     0.1000   -0.0012
##    280        0.5424             nan     0.1000   -0.0019
##    300        0.5297             nan     0.1000   -0.0007
##    320        0.5133             nan     0.1000   -0.0007
##    340        0.4974             nan     0.1000   -0.0012
##    360        0.4850             nan     0.1000   -0.0010
##    380        0.4733             nan     0.1000   -0.0004
##    400        0.4639             nan     0.1000   -0.0005
##    420        0.4524             nan     0.1000    0.0001
##    440        0.4401             nan     0.1000   -0.0011
##    460        0.4282             nan     0.1000   -0.0020
##    480        0.4181             nan     0.1000   -0.0011
##    500        0.4077             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2482             nan     0.1000    0.0234
##      2        1.2084             nan     0.1000    0.0170
##      3        1.1795             nan     0.1000    0.0148
##      4        1.1464             nan     0.1000    0.0135
##      5        1.1220             nan     0.1000    0.0125
##      6        1.0964             nan     0.1000    0.0123
##      7        1.0781             nan     0.1000    0.0066
##      8        1.0607             nan     0.1000    0.0068
##      9        1.0415             nan     0.1000    0.0079
##     10        1.0257             nan     0.1000    0.0070
##     20        0.9278             nan     0.1000    0.0007
##     40        0.8428             nan     0.1000   -0.0008
##     60        0.7878             nan     0.1000   -0.0008
##     80        0.7535             nan     0.1000   -0.0012
##    100        0.7249             nan     0.1000   -0.0019
##    120        0.7045             nan     0.1000   -0.0017
##    140        0.6838             nan     0.1000   -0.0010
##    160        0.6604             nan     0.1000   -0.0012
##    180        0.6398             nan     0.1000   -0.0005
##    200        0.6197             nan     0.1000   -0.0004
##    220        0.6035             nan     0.1000   -0.0013
##    240        0.5884             nan     0.1000   -0.0007
##    260        0.5731             nan     0.1000   -0.0009
##    280        0.5567             nan     0.1000   -0.0004
##    300        0.5425             nan     0.1000   -0.0003
##    320        0.5266             nan     0.1000   -0.0007
##    340        0.5138             nan     0.1000   -0.0025
##    360        0.5029             nan     0.1000   -0.0014
##    380        0.4922             nan     0.1000   -0.0010
##    400        0.4781             nan     0.1000   -0.0007
##    420        0.4639             nan     0.1000   -0.0016
##    440        0.4526             nan     0.1000   -0.0002
##    460        0.4432             nan     0.1000   -0.0010
##    480        0.4315             nan     0.1000   -0.0009
##    500        0.4209             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2500             nan     0.1000    0.0205
##      2        1.2089             nan     0.1000    0.0181
##      3        1.1763             nan     0.1000    0.0163
##      4        1.1436             nan     0.1000    0.0127
##      5        1.1170             nan     0.1000    0.0114
##      6        1.0943             nan     0.1000    0.0105
##      7        1.0712             nan     0.1000    0.0084
##      8        1.0530             nan     0.1000    0.0030
##      9        1.0383             nan     0.1000    0.0034
##     10        1.0234             nan     0.1000    0.0065
##     20        0.9276             nan     0.1000    0.0032
##     40        0.8283             nan     0.1000   -0.0003
##     60        0.7783             nan     0.1000   -0.0012
##     80        0.7385             nan     0.1000   -0.0009
##    100        0.7109             nan     0.1000   -0.0020
##    120        0.6896             nan     0.1000   -0.0009
##    140        0.6683             nan     0.1000   -0.0010
##    160        0.6440             nan     0.1000   -0.0008
##    180        0.6255             nan     0.1000   -0.0006
##    200        0.6041             nan     0.1000   -0.0014
##    220        0.5927             nan     0.1000   -0.0019
##    240        0.5788             nan     0.1000   -0.0008
##    260        0.5605             nan     0.1000   -0.0011
##    280        0.5471             nan     0.1000   -0.0006
##    300        0.5332             nan     0.1000   -0.0005
##    320        0.5190             nan     0.1000   -0.0014
##    340        0.5040             nan     0.1000   -0.0003
##    360        0.4925             nan     0.1000   -0.0007
##    380        0.4803             nan     0.1000   -0.0013
##    400        0.4669             nan     0.1000   -0.0013
##    420        0.4571             nan     0.1000   -0.0012
##    440        0.4437             nan     0.1000   -0.0003
##    460        0.4328             nan     0.1000   -0.0009
##    480        0.4219             nan     0.1000   -0.0010
##    500        0.4143             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2407             nan     0.1000    0.0260
##      2        1.2018             nan     0.1000    0.0131
##      3        1.1617             nan     0.1000    0.0181
##      4        1.1281             nan     0.1000    0.0136
##      5        1.0947             nan     0.1000    0.0123
##      6        1.0710             nan     0.1000    0.0117
##      7        1.0438             nan     0.1000    0.0113
##      8        1.0222             nan     0.1000    0.0076
##      9        1.0012             nan     0.1000    0.0077
##     10        0.9880             nan     0.1000    0.0035
##     20        0.8827             nan     0.1000    0.0016
##     40        0.7743             nan     0.1000   -0.0025
##     60        0.7141             nan     0.1000    0.0001
##     80        0.6739             nan     0.1000   -0.0008
##    100        0.6362             nan     0.1000   -0.0016
##    120        0.5956             nan     0.1000   -0.0010
##    140        0.5698             nan     0.1000   -0.0013
##    160        0.5418             nan     0.1000   -0.0011
##    180        0.5154             nan     0.1000   -0.0010
##    200        0.4937             nan     0.1000   -0.0023
##    220        0.4702             nan     0.1000   -0.0003
##    240        0.4447             nan     0.1000   -0.0014
##    260        0.4232             nan     0.1000   -0.0009
##    280        0.4008             nan     0.1000   -0.0008
##    300        0.3794             nan     0.1000   -0.0008
##    320        0.3637             nan     0.1000   -0.0006
##    340        0.3500             nan     0.1000   -0.0017
##    360        0.3331             nan     0.1000   -0.0006
##    380        0.3207             nan     0.1000   -0.0010
##    400        0.3072             nan     0.1000   -0.0008
##    420        0.2935             nan     0.1000   -0.0010
##    440        0.2831             nan     0.1000   -0.0001
##    460        0.2720             nan     0.1000   -0.0005
##    480        0.2628             nan     0.1000   -0.0001
##    500        0.2523             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2359             nan     0.1000    0.0226
##      2        1.1909             nan     0.1000    0.0223
##      3        1.1599             nan     0.1000    0.0144
##      4        1.1267             nan     0.1000    0.0134
##      5        1.0982             nan     0.1000    0.0122
##      6        1.0759             nan     0.1000    0.0093
##      7        1.0495             nan     0.1000    0.0133
##      8        1.0325             nan     0.1000    0.0071
##      9        1.0135             nan     0.1000    0.0057
##     10        0.9918             nan     0.1000    0.0074
##     20        0.8869             nan     0.1000    0.0016
##     40        0.7836             nan     0.1000    0.0001
##     60        0.7320             nan     0.1000   -0.0007
##     80        0.6767             nan     0.1000   -0.0022
##    100        0.6314             nan     0.1000   -0.0006
##    120        0.5941             nan     0.1000   -0.0005
##    140        0.5699             nan     0.1000   -0.0010
##    160        0.5411             nan     0.1000   -0.0014
##    180        0.5140             nan     0.1000   -0.0006
##    200        0.4909             nan     0.1000    0.0003
##    220        0.4650             nan     0.1000   -0.0003
##    240        0.4423             nan     0.1000   -0.0015
##    260        0.4235             nan     0.1000   -0.0012
##    280        0.4050             nan     0.1000   -0.0013
##    300        0.3859             nan     0.1000   -0.0008
##    320        0.3663             nan     0.1000   -0.0011
##    340        0.3519             nan     0.1000   -0.0002
##    360        0.3348             nan     0.1000   -0.0003
##    380        0.3212             nan     0.1000   -0.0003
##    400        0.3092             nan     0.1000   -0.0006
##    420        0.2962             nan     0.1000   -0.0006
##    440        0.2832             nan     0.1000   -0.0012
##    460        0.2715             nan     0.1000   -0.0006
##    480        0.2618             nan     0.1000   -0.0007
##    500        0.2523             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2423             nan     0.1000    0.0238
##      2        1.1884             nan     0.1000    0.0242
##      3        1.1460             nan     0.1000    0.0165
##      4        1.1198             nan     0.1000    0.0083
##      5        1.0947             nan     0.1000    0.0110
##      6        1.0713             nan     0.1000    0.0101
##      7        1.0505             nan     0.1000    0.0077
##      8        1.0291             nan     0.1000    0.0062
##      9        1.0111             nan     0.1000    0.0059
##     10        0.9947             nan     0.1000    0.0052
##     20        0.8912             nan     0.1000    0.0025
##     40        0.7845             nan     0.1000   -0.0022
##     60        0.7307             nan     0.1000   -0.0023
##     80        0.6831             nan     0.1000   -0.0000
##    100        0.6517             nan     0.1000   -0.0006
##    120        0.6152             nan     0.1000   -0.0011
##    140        0.5829             nan     0.1000   -0.0017
##    160        0.5554             nan     0.1000   -0.0025
##    180        0.5291             nan     0.1000   -0.0008
##    200        0.5067             nan     0.1000   -0.0014
##    220        0.4805             nan     0.1000   -0.0004
##    240        0.4606             nan     0.1000   -0.0014
##    260        0.4425             nan     0.1000   -0.0010
##    280        0.4225             nan     0.1000   -0.0015
##    300        0.4080             nan     0.1000   -0.0015
##    320        0.3932             nan     0.1000   -0.0017
##    340        0.3741             nan     0.1000   -0.0014
##    360        0.3579             nan     0.1000   -0.0006
##    380        0.3433             nan     0.1000   -0.0006
##    400        0.3312             nan     0.1000   -0.0009
##    420        0.3178             nan     0.1000   -0.0003
##    440        0.3062             nan     0.1000   -0.0012
##    460        0.2932             nan     0.1000   -0.0015
##    480        0.2828             nan     0.1000   -0.0004
##    500        0.2723             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2328             nan     0.2000    0.0322
##      2        1.1820             nan     0.2000    0.0226
##      3        1.1486             nan     0.2000    0.0126
##      4        1.1160             nan     0.2000    0.0150
##      5        1.0895             nan     0.2000    0.0130
##      6        1.0654             nan     0.2000    0.0098
##      7        1.0442             nan     0.2000    0.0064
##      8        1.0275             nan     0.2000    0.0037
##      9        1.0148             nan     0.2000    0.0049
##     10        0.9939             nan     0.2000    0.0076
##     20        0.9055             nan     0.2000    0.0008
##     40        0.8415             nan     0.2000   -0.0009
##     60        0.8060             nan     0.2000   -0.0020
##     80        0.7840             nan     0.2000   -0.0033
##    100        0.7691             nan     0.2000   -0.0011
##    120        0.7482             nan     0.2000   -0.0009
##    140        0.7341             nan     0.2000   -0.0017
##    160        0.7232             nan     0.2000   -0.0009
##    180        0.7138             nan     0.2000   -0.0003
##    200        0.7057             nan     0.2000   -0.0021
##    220        0.6969             nan     0.2000   -0.0021
##    240        0.6892             nan     0.2000   -0.0002
##    260        0.6804             nan     0.2000   -0.0011
##    280        0.6725             nan     0.2000   -0.0035
##    300        0.6619             nan     0.2000   -0.0011
##    320        0.6523             nan     0.2000   -0.0013
##    340        0.6487             nan     0.2000   -0.0029
##    360        0.6439             nan     0.2000   -0.0036
##    380        0.6380             nan     0.2000   -0.0002
##    400        0.6339             nan     0.2000   -0.0013
##    420        0.6267             nan     0.2000   -0.0027
##    440        0.6183             nan     0.2000   -0.0005
##    460        0.6118             nan     0.2000   -0.0020
##    480        0.6068             nan     0.2000   -0.0025
##    500        0.6006             nan     0.2000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2276             nan     0.2000    0.0305
##      2        1.1775             nan     0.2000    0.0169
##      3        1.1504             nan     0.2000    0.0133
##      4        1.1131             nan     0.2000    0.0133
##      5        1.0854             nan     0.2000    0.0119
##      6        1.0631             nan     0.2000    0.0083
##      7        1.0390             nan     0.2000    0.0085
##      8        1.0211             nan     0.2000    0.0080
##      9        1.0109             nan     0.2000    0.0027
##     10        0.9986             nan     0.2000    0.0030
##     20        0.9104             nan     0.2000    0.0020
##     40        0.8485             nan     0.2000   -0.0009
##     60        0.8176             nan     0.2000   -0.0007
##     80        0.7899             nan     0.2000   -0.0008
##    100        0.7743             nan     0.2000   -0.0017
##    120        0.7546             nan     0.2000   -0.0022
##    140        0.7416             nan     0.2000   -0.0035
##    160        0.7292             nan     0.2000   -0.0019
##    180        0.7190             nan     0.2000   -0.0020
##    200        0.7055             nan     0.2000   -0.0021
##    220        0.6994             nan     0.2000   -0.0011
##    240        0.6900             nan     0.2000   -0.0007
##    260        0.6787             nan     0.2000   -0.0002
##    280        0.6723             nan     0.2000   -0.0023
##    300        0.6663             nan     0.2000   -0.0016
##    320        0.6612             nan     0.2000   -0.0025
##    340        0.6537             nan     0.2000   -0.0014
##    360        0.6465             nan     0.2000   -0.0016
##    380        0.6392             nan     0.2000   -0.0015
##    400        0.6338             nan     0.2000   -0.0029
##    420        0.6277             nan     0.2000   -0.0016
##    440        0.6208             nan     0.2000   -0.0022
##    460        0.6142             nan     0.2000   -0.0020
##    480        0.6087             nan     0.2000   -0.0012
##    500        0.6054             nan     0.2000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2216             nan     0.2000    0.0335
##      2        1.1764             nan     0.2000    0.0214
##      3        1.1395             nan     0.2000    0.0129
##      4        1.1101             nan     0.2000    0.0140
##      5        1.0850             nan     0.2000    0.0092
##      6        1.0636             nan     0.2000    0.0085
##      7        1.0377             nan     0.2000    0.0109
##      8        1.0231             nan     0.2000    0.0035
##      9        1.0050             nan     0.2000    0.0066
##     10        0.9961             nan     0.2000    0.0013
##     20        0.9105             nan     0.2000    0.0038
##     40        0.8449             nan     0.2000   -0.0017
##     60        0.8114             nan     0.2000   -0.0037
##     80        0.7880             nan     0.2000   -0.0042
##    100        0.7716             nan     0.2000   -0.0011
##    120        0.7591             nan     0.2000   -0.0022
##    140        0.7476             nan     0.2000   -0.0010
##    160        0.7334             nan     0.2000   -0.0015
##    180        0.7261             nan     0.2000   -0.0020
##    200        0.7167             nan     0.2000   -0.0001
##    220        0.7091             nan     0.2000   -0.0016
##    240        0.7000             nan     0.2000   -0.0022
##    260        0.6934             nan     0.2000   -0.0017
##    280        0.6876             nan     0.2000   -0.0014
##    300        0.6773             nan     0.2000   -0.0026
##    320        0.6684             nan     0.2000   -0.0015
##    340        0.6586             nan     0.2000   -0.0021
##    360        0.6526             nan     0.2000   -0.0038
##    380        0.6467             nan     0.2000   -0.0025
##    400        0.6376             nan     0.2000   -0.0013
##    420        0.6337             nan     0.2000   -0.0015
##    440        0.6253             nan     0.2000   -0.0010
##    460        0.6202             nan     0.2000   -0.0011
##    480        0.6175             nan     0.2000   -0.0011
##    500        0.6087             nan     0.2000   -0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2140             nan     0.2000    0.0398
##      2        1.1406             nan     0.2000    0.0288
##      3        1.0914             nan     0.2000    0.0212
##      4        1.0596             nan     0.2000    0.0097
##      5        1.0254             nan     0.2000    0.0139
##      6        1.0042             nan     0.2000    0.0057
##      7        0.9798             nan     0.2000    0.0059
##      8        0.9650             nan     0.2000    0.0030
##      9        0.9545             nan     0.2000   -0.0006
##     10        0.9415             nan     0.2000    0.0002
##     20        0.8396             nan     0.2000    0.0003
##     40        0.7548             nan     0.2000   -0.0001
##     60        0.7095             nan     0.2000   -0.0058
##     80        0.6610             nan     0.2000   -0.0036
##    100        0.6228             nan     0.2000   -0.0039
##    120        0.5881             nan     0.2000   -0.0010
##    140        0.5577             nan     0.2000   -0.0029
##    160        0.5236             nan     0.2000   -0.0013
##    180        0.4983             nan     0.2000   -0.0021
##    200        0.4743             nan     0.2000   -0.0023
##    220        0.4499             nan     0.2000   -0.0009
##    240        0.4252             nan     0.2000   -0.0005
##    260        0.4037             nan     0.2000   -0.0010
##    280        0.3894             nan     0.2000   -0.0020
##    300        0.3760             nan     0.2000   -0.0012
##    320        0.3592             nan     0.2000   -0.0004
##    340        0.3429             nan     0.2000   -0.0011
##    360        0.3289             nan     0.2000   -0.0015
##    380        0.3098             nan     0.2000   -0.0004
##    400        0.2982             nan     0.2000   -0.0022
##    420        0.2873             nan     0.2000   -0.0019
##    440        0.2759             nan     0.2000   -0.0011
##    460        0.2645             nan     0.2000    0.0002
##    480        0.2530             nan     0.2000   -0.0008
##    500        0.2422             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2017             nan     0.2000    0.0365
##      2        1.1352             nan     0.2000    0.0280
##      3        1.0792             nan     0.2000    0.0225
##      4        1.0467             nan     0.2000    0.0117
##      5        1.0168             nan     0.2000    0.0070
##      6        0.9964             nan     0.2000    0.0065
##      7        0.9748             nan     0.2000    0.0052
##      8        0.9580             nan     0.2000    0.0035
##      9        0.9390             nan     0.2000    0.0036
##     10        0.9265             nan     0.2000    0.0039
##     20        0.8365             nan     0.2000   -0.0025
##     40        0.7710             nan     0.2000   -0.0046
##     60        0.7146             nan     0.2000   -0.0037
##     80        0.6629             nan     0.2000   -0.0010
##    100        0.6208             nan     0.2000   -0.0016
##    120        0.5867             nan     0.2000   -0.0031
##    140        0.5542             nan     0.2000   -0.0021
##    160        0.5253             nan     0.2000   -0.0003
##    180        0.5020             nan     0.2000   -0.0035
##    200        0.4751             nan     0.2000   -0.0007
##    220        0.4486             nan     0.2000   -0.0015
##    240        0.4301             nan     0.2000   -0.0027
##    260        0.4116             nan     0.2000   -0.0004
##    280        0.3914             nan     0.2000   -0.0003
##    300        0.3720             nan     0.2000   -0.0009
##    320        0.3538             nan     0.2000   -0.0026
##    340        0.3329             nan     0.2000   -0.0021
##    360        0.3212             nan     0.2000   -0.0016
##    380        0.3058             nan     0.2000   -0.0019
##    400        0.2967             nan     0.2000   -0.0010
##    420        0.2860             nan     0.2000   -0.0007
##    440        0.2713             nan     0.2000   -0.0002
##    460        0.2584             nan     0.2000   -0.0010
##    480        0.2497             nan     0.2000   -0.0015
##    500        0.2393             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2119             nan     0.2000    0.0358
##      2        1.1490             nan     0.2000    0.0277
##      3        1.0875             nan     0.2000    0.0212
##      4        1.0589             nan     0.2000    0.0100
##      5        1.0237             nan     0.2000    0.0130
##      6        1.0011             nan     0.2000    0.0073
##      7        0.9777             nan     0.2000    0.0082
##      8        0.9601             nan     0.2000    0.0044
##      9        0.9458             nan     0.2000    0.0019
##     10        0.9355             nan     0.2000    0.0008
##     20        0.8485             nan     0.2000   -0.0004
##     40        0.7628             nan     0.2000   -0.0033
##     60        0.7031             nan     0.2000   -0.0032
##     80        0.6585             nan     0.2000   -0.0028
##    100        0.6306             nan     0.2000   -0.0020
##    120        0.6015             nan     0.2000   -0.0034
##    140        0.5578             nan     0.2000   -0.0027
##    160        0.5227             nan     0.2000   -0.0007
##    180        0.4949             nan     0.2000   -0.0011
##    200        0.4681             nan     0.2000   -0.0012
##    220        0.4448             nan     0.2000   -0.0010
##    240        0.4265             nan     0.2000   -0.0013
##    260        0.4108             nan     0.2000   -0.0016
##    280        0.3960             nan     0.2000   -0.0007
##    300        0.3819             nan     0.2000   -0.0013
##    320        0.3625             nan     0.2000   -0.0016
##    340        0.3463             nan     0.2000   -0.0024
##    360        0.3393             nan     0.2000   -0.0022
##    380        0.3258             nan     0.2000   -0.0023
##    400        0.3129             nan     0.2000   -0.0026
##    420        0.3027             nan     0.2000   -0.0008
##    440        0.2898             nan     0.2000   -0.0008
##    460        0.2768             nan     0.2000   -0.0007
##    480        0.2649             nan     0.2000   -0.0019
##    500        0.2556             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1965             nan     0.2000    0.0443
##      2        1.1149             nan     0.2000    0.0352
##      3        1.0625             nan     0.2000    0.0234
##      4        1.0270             nan     0.2000    0.0103
##      5        0.9949             nan     0.2000    0.0081
##      6        0.9712             nan     0.2000    0.0043
##      7        0.9454             nan     0.2000    0.0043
##      8        0.9224             nan     0.2000    0.0033
##      9        0.9038             nan     0.2000    0.0037
##     10        0.8835             nan     0.2000    0.0045
##     20        0.7866             nan     0.2000   -0.0004
##     40        0.6776             nan     0.2000   -0.0015
##     60        0.6258             nan     0.2000   -0.0129
##     80        0.5652             nan     0.2000   -0.0016
##    100        0.5092             nan     0.2000   -0.0047
##    120        0.4501             nan     0.2000   -0.0017
##    140        0.4145             nan     0.2000   -0.0034
##    160        0.3751             nan     0.2000   -0.0012
##    180        0.3460             nan     0.2000   -0.0011
##    200        0.3192             nan     0.2000   -0.0009
##    220        0.2991             nan     0.2000   -0.0022
##    240        0.2726             nan     0.2000   -0.0005
##    260        0.2561             nan     0.2000   -0.0006
##    280        0.2379             nan     0.2000   -0.0014
##    300        0.2209             nan     0.2000   -0.0021
##    320        0.2036             nan     0.2000   -0.0009
##    340        0.1911             nan     0.2000   -0.0015
##    360        0.1785             nan     0.2000   -0.0009
##    380        0.1688             nan     0.2000   -0.0005
##    400        0.1572             nan     0.2000   -0.0005
##    420        0.1461             nan     0.2000   -0.0008
##    440        0.1356             nan     0.2000   -0.0002
##    460        0.1269             nan     0.2000   -0.0010
##    480        0.1197             nan     0.2000   -0.0009
##    500        0.1136             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1858             nan     0.2000    0.0440
##      2        1.1254             nan     0.2000    0.0222
##      3        1.0689             nan     0.2000    0.0216
##      4        1.0167             nan     0.2000    0.0225
##      5        0.9818             nan     0.2000    0.0131
##      6        0.9587             nan     0.2000    0.0056
##      7        0.9351             nan     0.2000    0.0056
##      8        0.9122             nan     0.2000    0.0057
##      9        0.8957             nan     0.2000   -0.0000
##     10        0.8815             nan     0.2000    0.0003
##     20        0.7925             nan     0.2000   -0.0028
##     40        0.7004             nan     0.2000   -0.0010
##     60        0.6306             nan     0.2000   -0.0042
##     80        0.5754             nan     0.2000   -0.0024
##    100        0.5265             nan     0.2000   -0.0017
##    120        0.4842             nan     0.2000   -0.0049
##    140        0.4371             nan     0.2000   -0.0014
##    160        0.3956             nan     0.2000   -0.0018
##    180        0.3671             nan     0.2000   -0.0035
##    200        0.3362             nan     0.2000   -0.0047
##    220        0.3086             nan     0.2000   -0.0026
##    240        0.2860             nan     0.2000   -0.0014
##    260        0.2641             nan     0.2000   -0.0008
##    280        0.2449             nan     0.2000   -0.0006
##    300        0.2266             nan     0.2000   -0.0014
##    320        0.2100             nan     0.2000   -0.0014
##    340        0.1969             nan     0.2000   -0.0006
##    360        0.1834             nan     0.2000   -0.0007
##    380        0.1726             nan     0.2000   -0.0011
##    400        0.1619             nan     0.2000   -0.0008
##    420        0.1526             nan     0.2000   -0.0009
##    440        0.1419             nan     0.2000   -0.0008
##    460        0.1331             nan     0.2000   -0.0005
##    480        0.1243             nan     0.2000   -0.0004
##    500        0.1154             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1916             nan     0.2000    0.0477
##      2        1.1086             nan     0.2000    0.0367
##      3        1.0632             nan     0.2000    0.0174
##      4        1.0147             nan     0.2000    0.0213
##      5        0.9889             nan     0.2000    0.0044
##      6        0.9571             nan     0.2000    0.0085
##      7        0.9300             nan     0.2000    0.0064
##      8        0.9099             nan     0.2000    0.0039
##      9        0.8933             nan     0.2000    0.0027
##     10        0.8799             nan     0.2000    0.0020
##     20        0.7874             nan     0.2000   -0.0030
##     40        0.6911             nan     0.2000   -0.0025
##     60        0.6141             nan     0.2000   -0.0043
##     80        0.5606             nan     0.2000   -0.0027
##    100        0.5110             nan     0.2000   -0.0020
##    120        0.4665             nan     0.2000   -0.0013
##    140        0.4282             nan     0.2000   -0.0033
##    160        0.3899             nan     0.2000   -0.0036
##    180        0.3622             nan     0.2000   -0.0020
##    200        0.3220             nan     0.2000   -0.0015
##    220        0.2947             nan     0.2000   -0.0006
##    240        0.2724             nan     0.2000   -0.0007
##    260        0.2558             nan     0.2000   -0.0008
##    280        0.2367             nan     0.2000   -0.0023
##    300        0.2188             nan     0.2000   -0.0008
##    320        0.2051             nan     0.2000   -0.0009
##    340        0.1907             nan     0.2000   -0.0009
##    360        0.1774             nan     0.2000   -0.0010
##    380        0.1674             nan     0.2000   -0.0019
##    400        0.1546             nan     0.2000   -0.0014
##    420        0.1447             nan     0.2000   -0.0013
##    440        0.1349             nan     0.2000   -0.0008
##    460        0.1265             nan     0.2000   -0.0004
##    480        0.1189             nan     0.2000   -0.0005
##    500        0.1118             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2064             nan     0.3000    0.0455
##      2        1.1425             nan     0.3000    0.0188
##      3        1.0973             nan     0.3000    0.0187
##      4        1.0530             nan     0.3000    0.0157
##      5        1.0217             nan     0.3000    0.0140
##      6        1.0028             nan     0.3000    0.0044
##      7        0.9855             nan     0.3000    0.0043
##      8        0.9626             nan     0.3000    0.0078
##      9        0.9512             nan     0.3000    0.0026
##     10        0.9402             nan     0.3000    0.0012
##     20        0.8655             nan     0.3000   -0.0047
##     40        0.7982             nan     0.3000   -0.0009
##     60        0.7697             nan     0.3000   -0.0047
##     80        0.7388             nan     0.3000   -0.0014
##    100        0.7236             nan     0.3000   -0.0027
##    120        0.7015             nan     0.3000   -0.0036
##    140        0.6866             nan     0.3000   -0.0027
##    160        0.6746             nan     0.3000   -0.0016
##    180        0.6649             nan     0.3000   -0.0072
##    200        0.6523             nan     0.3000   -0.0024
##    220        0.6382             nan     0.3000   -0.0005
##    240        0.6307             nan     0.3000   -0.0045
##    260        0.6226             nan     0.3000   -0.0038
##    280        0.6172             nan     0.3000   -0.0039
##    300        0.6097             nan     0.3000   -0.0029
##    320        0.6014             nan     0.3000   -0.0038
##    340        0.5902             nan     0.3000   -0.0045
##    360        0.5845             nan     0.3000   -0.0062
##    380        0.5770             nan     0.3000   -0.0015
##    400        0.5689             nan     0.3000   -0.0016
##    420        0.5654             nan     0.3000   -0.0029
##    440        0.5585             nan     0.3000   -0.0021
##    460        0.5520             nan     0.3000   -0.0002
##    480        0.5491             nan     0.3000   -0.0020
##    500        0.5447             nan     0.3000   -0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1987             nan     0.3000    0.0409
##      2        1.1286             nan     0.3000    0.0241
##      3        1.0880             nan     0.3000    0.0179
##      4        1.0539             nan     0.3000    0.0069
##      5        1.0187             nan     0.3000    0.0152
##      6        1.0030             nan     0.3000    0.0062
##      7        0.9793             nan     0.3000    0.0091
##      8        0.9666             nan     0.3000    0.0030
##      9        0.9551             nan     0.3000    0.0014
##     10        0.9396             nan     0.3000    0.0030
##     20        0.8713             nan     0.3000   -0.0000
##     40        0.8172             nan     0.3000   -0.0058
##     60        0.7865             nan     0.3000   -0.0036
##     80        0.7667             nan     0.3000   -0.0045
##    100        0.7444             nan     0.3000   -0.0072
##    120        0.7216             nan     0.3000   -0.0024
##    140        0.7056             nan     0.3000   -0.0039
##    160        0.6936             nan     0.3000   -0.0052
##    180        0.6748             nan     0.3000   -0.0020
##    200        0.6645             nan     0.3000   -0.0012
##    220        0.6645             nan     0.3000   -0.0029
##    240        0.6508             nan     0.3000   -0.0056
##    260        0.6400             nan     0.3000   -0.0027
##    280        0.6309             nan     0.3000   -0.0015
##    300        0.6223             nan     0.3000   -0.0036
##    320        0.6104             nan     0.3000   -0.0030
##    340        0.6032             nan     0.3000   -0.0012
##    360        0.6018             nan     0.3000   -0.0027
##    380        0.5932             nan     0.3000   -0.0041
##    400        0.5822             nan     0.3000   -0.0012
##    420        0.5760             nan     0.3000   -0.0012
##    440        0.5717             nan     0.3000   -0.0010
##    460        0.5644             nan     0.3000   -0.0024
##    480        0.5540             nan     0.3000   -0.0039
##    500        0.5473             nan     0.3000   -0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2097             nan     0.3000    0.0404
##      2        1.1461             nan     0.3000    0.0222
##      3        1.1006             nan     0.3000    0.0172
##      4        1.0622             nan     0.3000    0.0180
##      5        1.0277             nan     0.3000    0.0127
##      6        1.0089             nan     0.3000    0.0023
##      7        0.9871             nan     0.3000    0.0079
##      8        0.9651             nan     0.3000    0.0097
##      9        0.9490             nan     0.3000    0.0053
##     10        0.9334             nan     0.3000    0.0053
##     20        0.8628             nan     0.3000   -0.0007
##     40        0.8070             nan     0.3000   -0.0025
##     60        0.7791             nan     0.3000   -0.0081
##     80        0.7575             nan     0.3000   -0.0033
##    100        0.7331             nan     0.3000   -0.0017
##    120        0.7207             nan     0.3000   -0.0021
##    140        0.7050             nan     0.3000   -0.0028
##    160        0.6979             nan     0.3000   -0.0006
##    180        0.6843             nan     0.3000   -0.0002
##    200        0.6691             nan     0.3000   -0.0002
##    220        0.6590             nan     0.3000   -0.0024
##    240        0.6472             nan     0.3000   -0.0029
##    260        0.6368             nan     0.3000   -0.0006
##    280        0.6314             nan     0.3000   -0.0071
##    300        0.6205             nan     0.3000   -0.0041
##    320        0.6100             nan     0.3000   -0.0043
##    340        0.6010             nan     0.3000   -0.0023
##    360        0.5942             nan     0.3000   -0.0027
##    380        0.5897             nan     0.3000   -0.0073
##    400        0.5817             nan     0.3000   -0.0063
##    420        0.5739             nan     0.3000   -0.0009
##    440        0.5656             nan     0.3000   -0.0027
##    460        0.5609             nan     0.3000   -0.0032
##    480        0.5522             nan     0.3000   -0.0018
##    500        0.5436             nan     0.3000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1618             nan     0.3000    0.0570
##      2        1.0812             nan     0.3000    0.0415
##      3        1.0274             nan     0.3000    0.0258
##      4        0.9821             nan     0.3000    0.0150
##      5        0.9561             nan     0.3000    0.0069
##      6        0.9352             nan     0.3000    0.0019
##      7        0.9176             nan     0.3000    0.0033
##      8        0.8980             nan     0.3000    0.0035
##      9        0.8813             nan     0.3000   -0.0003
##     10        0.8681             nan     0.3000    0.0006
##     20        0.7888             nan     0.3000   -0.0007
##     40        0.7282             nan     0.3000   -0.0062
##     60        0.6635             nan     0.3000   -0.0036
##     80        0.6154             nan     0.3000   -0.0040
##    100        0.5653             nan     0.3000   -0.0010
##    120        0.5361             nan     0.3000   -0.0036
##    140        0.5021             nan     0.3000   -0.0089
##    160        0.4578             nan     0.3000   -0.0041
##    180        0.4175             nan     0.3000   -0.0034
##    200        0.3890             nan     0.3000   -0.0025
##    220        0.3655             nan     0.3000   -0.0033
##    240        0.3422             nan     0.3000   -0.0034
##    260        0.3265             nan     0.3000   -0.0018
##    280        0.3063             nan     0.3000   -0.0012
##    300        0.2925             nan     0.3000   -0.0007
##    320        0.2816             nan     0.3000   -0.0029
##    340        0.2673             nan     0.3000   -0.0020
##    360        0.2523             nan     0.3000   -0.0030
##    380        0.2359             nan     0.3000   -0.0025
##    400        0.2252             nan     0.3000   -0.0016
##    420        0.2131             nan     0.3000   -0.0012
##    440        0.2023             nan     0.3000   -0.0024
##    460        0.1873             nan     0.3000   -0.0008
##    480        0.1813             nan     0.3000   -0.0011
##    500        0.1738             nan     0.3000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1675             nan     0.3000    0.0529
##      2        1.0900             nan     0.3000    0.0390
##      3        1.0356             nan     0.3000    0.0235
##      4        0.9977             nan     0.3000    0.0118
##      5        0.9806             nan     0.3000    0.0002
##      6        0.9501             nan     0.3000    0.0069
##      7        0.9308             nan     0.3000    0.0016
##      8        0.9124             nan     0.3000    0.0055
##      9        0.9030             nan     0.3000   -0.0041
##     10        0.8902             nan     0.3000   -0.0007
##     20        0.8104             nan     0.3000   -0.0038
##     40        0.7183             nan     0.3000   -0.0013
##     60        0.6583             nan     0.3000   -0.0058
##     80        0.6146             nan     0.3000   -0.0037
##    100        0.5605             nan     0.3000   -0.0042
##    120        0.5111             nan     0.3000   -0.0048
##    140        0.4769             nan     0.3000   -0.0005
##    160        0.4441             nan     0.3000   -0.0040
##    180        0.4073             nan     0.3000   -0.0039
##    200        0.3742             nan     0.3000   -0.0037
##    220        0.3470             nan     0.3000   -0.0029
##    240        0.3276             nan     0.3000   -0.0018
##    260        0.3013             nan     0.3000   -0.0032
##    280        0.2846             nan     0.3000   -0.0024
##    300        0.2648             nan     0.3000   -0.0011
##    320        0.2505             nan     0.3000   -0.0020
##    340        0.2343             nan     0.3000   -0.0024
##    360        0.2165             nan     0.3000   -0.0032
##    380        0.2047             nan     0.3000   -0.0010
##    400        0.1915             nan     0.3000   -0.0018
##    420        0.1852             nan     0.3000   -0.0012
##    440        0.1727             nan     0.3000   -0.0012
##    460        0.1658             nan     0.3000   -0.0016
##    480        0.1561             nan     0.3000   -0.0014
##    500        0.1489             nan     0.3000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1605             nan     0.3000    0.0508
##      2        1.0959             nan     0.3000    0.0283
##      3        1.0350             nan     0.3000    0.0218
##      4        0.9972             nan     0.3000    0.0111
##      5        0.9721             nan     0.3000    0.0070
##      6        0.9519             nan     0.3000    0.0036
##      7        0.9271             nan     0.3000    0.0084
##      8        0.9082             nan     0.3000    0.0037
##      9        0.8941             nan     0.3000   -0.0001
##     10        0.8789             nan     0.3000   -0.0003
##     20        0.8039             nan     0.3000   -0.0041
##     40        0.7208             nan     0.3000   -0.0016
##     60        0.6485             nan     0.3000   -0.0017
##     80        0.5941             nan     0.3000   -0.0045
##    100        0.5610             nan     0.3000   -0.0065
##    120        0.5240             nan     0.3000   -0.0017
##    140        0.4929             nan     0.3000   -0.0077
##    160        0.4618             nan     0.3000   -0.0025
##    180        0.4308             nan     0.3000   -0.0032
##    200        0.3941             nan     0.3000   -0.0018
##    220        0.3757             nan     0.3000   -0.0005
##    240        0.3556             nan     0.3000   -0.0023
##    260        0.3337             nan     0.3000   -0.0025
##    280        0.3188             nan     0.3000   -0.0019
##    300        0.2979             nan     0.3000   -0.0019
##    320        0.2776             nan     0.3000   -0.0022
##    340        0.2550             nan     0.3000   -0.0033
##    360        0.2419             nan     0.3000   -0.0014
##    380        0.2298             nan     0.3000   -0.0020
##    400        0.2163             nan     0.3000   -0.0022
##    420        0.2021             nan     0.3000   -0.0016
##    440        0.1929             nan     0.3000   -0.0026
##    460        0.1826             nan     0.3000   -0.0023
##    480        0.1728             nan     0.3000   -0.0017
##    500        0.1632             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1549             nan     0.3000    0.0478
##      2        1.0744             nan     0.3000    0.0363
##      3        1.0121             nan     0.3000    0.0239
##      4        0.9755             nan     0.3000    0.0043
##      5        0.9289             nan     0.3000    0.0091
##      6        0.9072             nan     0.3000   -0.0011
##      7        0.8941             nan     0.3000   -0.0098
##      8        0.8757             nan     0.3000   -0.0014
##      9        0.8551             nan     0.3000    0.0024
##     10        0.8385             nan     0.3000   -0.0029
##     20        0.7426             nan     0.3000   -0.0085
##     40        0.6317             nan     0.3000   -0.0003
##     60        0.5423             nan     0.3000   -0.0008
##     80        0.4808             nan     0.3000   -0.0054
##    100        0.4165             nan     0.3000    0.0001
##    120        0.3673             nan     0.3000   -0.0031
##    140        0.3264             nan     0.3000   -0.0004
##    160        0.2823             nan     0.3000   -0.0035
##    180        0.2515             nan     0.3000   -0.0011
##    200        0.2283             nan     0.3000   -0.0011
##    220        0.2035             nan     0.3000   -0.0018
##    240        0.1833             nan     0.3000   -0.0009
##    260        0.1660             nan     0.3000   -0.0020
##    280        0.1516             nan     0.3000   -0.0002
##    300        0.1381             nan     0.3000   -0.0004
##    320        0.1243             nan     0.3000   -0.0010
##    340        0.1141             nan     0.3000   -0.0010
##    360        0.1063             nan     0.3000   -0.0006
##    380        0.0947             nan     0.3000   -0.0009
##    400        0.0877             nan     0.3000   -0.0005
##    420        0.0809             nan     0.3000   -0.0013
##    440        0.0747             nan     0.3000   -0.0007
##    460        0.0702             nan     0.3000   -0.0011
##    480        0.0643             nan     0.3000   -0.0003
##    500        0.0589             nan     0.3000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1524             nan     0.3000    0.0626
##      2        1.0677             nan     0.3000    0.0343
##      3        1.0018             nan     0.3000    0.0244
##      4        0.9584             nan     0.3000    0.0144
##      5        0.9309             nan     0.3000    0.0031
##      6        0.9105             nan     0.3000    0.0016
##      7        0.8868             nan     0.3000    0.0056
##      8        0.8696             nan     0.3000   -0.0003
##      9        0.8517             nan     0.3000    0.0014
##     10        0.8377             nan     0.3000   -0.0018
##     20        0.7487             nan     0.3000   -0.0069
##     40        0.6154             nan     0.3000   -0.0051
##     60        0.5268             nan     0.3000   -0.0023
##     80        0.4567             nan     0.3000   -0.0032
##    100        0.4051             nan     0.3000   -0.0053
##    120        0.3599             nan     0.3000   -0.0021
##    140        0.3184             nan     0.3000    0.0005
##    160        0.2808             nan     0.3000   -0.0029
##    180        0.2491             nan     0.3000   -0.0012
##    200        0.2252             nan     0.3000   -0.0029
##    220        0.2021             nan     0.3000   -0.0018
##    240        0.1787             nan     0.3000   -0.0003
##    260        0.1621             nan     0.3000   -0.0011
##    280        0.1446             nan     0.3000   -0.0017
##    300        0.1312             nan     0.3000   -0.0019
##    320        0.1201             nan     0.3000   -0.0013
##    340        0.1102             nan     0.3000   -0.0009
##    360        0.0985             nan     0.3000   -0.0005
##    380        0.0922             nan     0.3000   -0.0014
##    400        0.0845             nan     0.3000   -0.0012
##    420        0.0777             nan     0.3000   -0.0012
##    440        0.0719             nan     0.3000   -0.0008
##    460        0.0661             nan     0.3000   -0.0004
##    480        0.0616             nan     0.3000   -0.0002
##    500        0.0571             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1539             nan     0.3000    0.0614
##      2        1.0709             nan     0.3000    0.0286
##      3        1.0019             nan     0.3000    0.0297
##      4        0.9596             nan     0.3000    0.0148
##      5        0.9279             nan     0.3000    0.0082
##      6        0.9008             nan     0.3000    0.0036
##      7        0.8787             nan     0.3000    0.0023
##      8        0.8608             nan     0.3000    0.0020
##      9        0.8423             nan     0.3000    0.0009
##     10        0.8330             nan     0.3000   -0.0007
##     20        0.7427             nan     0.3000   -0.0039
##     40        0.6267             nan     0.3000   -0.0054
##     60        0.5444             nan     0.3000   -0.0023
##     80        0.4736             nan     0.3000   -0.0056
##    100        0.4109             nan     0.3000   -0.0063
##    120        0.3597             nan     0.3000   -0.0047
##    140        0.3253             nan     0.3000   -0.0040
##    160        0.2832             nan     0.3000   -0.0016
##    180        0.2534             nan     0.3000   -0.0031
##    200        0.2259             nan     0.3000   -0.0022
##    220        0.2051             nan     0.3000   -0.0018
##    240        0.1791             nan     0.3000   -0.0024
##    260        0.1581             nan     0.3000   -0.0010
##    280        0.1442             nan     0.3000   -0.0012
##    300        0.1307             nan     0.3000   -0.0009
##    320        0.1183             nan     0.3000   -0.0002
##    340        0.1068             nan     0.3000   -0.0008
##    360        0.0977             nan     0.3000   -0.0015
##    380        0.0900             nan     0.3000   -0.0003
##    400        0.0824             nan     0.3000   -0.0002
##    420        0.0746             nan     0.3000   -0.0009
##    440        0.0690             nan     0.3000   -0.0003
##    460        0.0624             nan     0.3000   -0.0002
##    480        0.0570             nan     0.3000   -0.0001
##    500        0.0530             nan     0.3000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1573             nan     0.5000    0.0644
##      2        1.0905             nan     0.5000    0.0262
##      3        1.0387             nan     0.5000    0.0049
##      4        1.0085             nan     0.5000    0.0035
##      5        0.9883             nan     0.5000    0.0006
##      6        0.9547             nan     0.5000    0.0132
##      7        0.9438             nan     0.5000    0.0009
##      8        0.9299             nan     0.5000   -0.0003
##      9        0.9191             nan     0.5000    0.0003
##     10        0.9079             nan     0.5000    0.0001
##     20        0.8560             nan     0.5000   -0.0038
##     40        0.8026             nan     0.5000   -0.0029
##     60        0.7637             nan     0.5000    0.0001
##     80        0.7358             nan     0.5000   -0.0022
##    100        0.7057             nan     0.5000    0.0005
##    120        0.6823             nan     0.5000   -0.0107
##    140        0.6660             nan     0.5000   -0.0010
##    160        0.6461             nan     0.5000   -0.0043
##    180        0.6303             nan     0.5000   -0.0101
##    200        0.6118             nan     0.5000   -0.0119
##    220        0.6030             nan     0.5000   -0.0064
##    240        0.5845             nan     0.5000   -0.0067
##    260        0.5765             nan     0.5000   -0.0086
##    280        0.5568             nan     0.5000   -0.0040
##    300        0.5449             nan     0.5000   -0.0017
##    320        0.5343             nan     0.5000   -0.0049
##    340        0.5235             nan     0.5000   -0.0034
##    360        0.5159             nan     0.5000   -0.0028
##    380        0.4963             nan     0.5000   -0.0048
##    400        0.4908             nan     0.5000   -0.0024
##    420        0.4820             nan     0.5000   -0.0023
##    440        0.4784             nan     0.5000   -0.0028
##    460        0.4647             nan     0.5000   -0.0015
##    480        0.4599             nan     0.5000   -0.0057
##    500        0.4577             nan     0.5000   -0.0077
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1582             nan     0.5000    0.0561
##      2        1.0825             nan     0.5000    0.0352
##      3        1.0240             nan     0.5000    0.0205
##      4        0.9615             nan     0.5000    0.0179
##      5        0.9499             nan     0.5000   -0.0024
##      6        0.9366             nan     0.5000    0.0015
##      7        0.9250             nan     0.5000   -0.0035
##      8        0.9128             nan     0.5000    0.0023
##      9        0.8990             nan     0.5000   -0.0027
##     10        0.8939             nan     0.5000   -0.0046
##     20        0.8423             nan     0.5000    0.0068
##     40        0.7838             nan     0.5000   -0.0030
##     60        0.7433             nan     0.5000   -0.0089
##     80        0.7148             nan     0.5000   -0.0037
##    100        0.7046             nan     0.5000   -0.0164
##    120        0.6764             nan     0.5000   -0.0053
##    140        0.6573             nan     0.5000   -0.0075
##    160        0.6444             nan     0.5000   -0.0041
##    180        0.6305             nan     0.5000   -0.0055
##    200        0.6205             nan     0.5000   -0.0143
##    220        0.6056             nan     0.5000   -0.0061
##    240        0.5935             nan     0.5000   -0.0036
##    260        0.5837             nan     0.5000   -0.0020
##    280        0.5928             nan     0.5000   -0.0015
##    300        4.5850             nan     0.5000   -0.0004
##    320        4.4201             nan     0.5000   -0.0000
##    340        3.2309             nan     0.5000    0.0035
##    360        3.2192             nan     0.5000   -0.0022
##    380        3.2073             nan     0.5000   -0.0009
##    400        3.2288             nan     0.5000   -0.0049
##    420        3.2298             nan     0.5000   -0.0117
##    440        3.2256             nan     0.5000   -0.0026
##    460        3.2124             nan     0.5000   -0.0006
##    480        3.0565             nan     0.5000   -0.0056
##    500        3.0597             nan     0.5000   -0.0034
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1571             nan     0.5000    0.0656
##      2        1.0917             nan     0.5000    0.0236
##      3        1.0316             nan     0.5000    0.0248
##      4        0.9849             nan     0.5000    0.0185
##      5        0.9699             nan     0.5000    0.0004
##      6        0.9504             nan     0.5000   -0.0007
##      7        0.9411             nan     0.5000   -0.0022
##      8        0.9173             nan     0.5000    0.0048
##      9        0.9180             nan     0.5000   -0.0073
##     10        0.9125             nan     0.5000   -0.0071
##     20        0.8430             nan     0.5000   -0.0026
##     40        0.7887             nan     0.5000   -0.0020
##     60        0.7642             nan     0.5000    0.0005
##     80        0.7499             nan     0.5000   -0.0113
##    100        0.7126             nan     0.5000   -0.0047
##    120        0.6929             nan     0.5000   -0.0034
##    140        0.6796             nan     0.5000   -0.0029
##    160        0.6627             nan     0.5000   -0.0094
##    180        0.6366             nan     0.5000   -0.0007
##    200        0.6281             nan     0.5000   -0.0033
##    220        0.6046             nan     0.5000    0.0006
##    240        0.6017             nan     0.5000   -0.0057
##    260        0.5800             nan     0.5000   -0.0034
##    280        0.5777             nan     0.5000   -0.0071
##    300        0.5616             nan     0.5000   -0.0075
##    320        0.5497             nan     0.5000   -0.0033
##    340        0.5438             nan     0.5000   -0.0079
##    360        0.5307             nan     0.5000   -0.0083
##    380        0.5183             nan     0.5000   -0.0029
##    400        0.5085             nan     0.5000   -0.0018
##    420        0.5022             nan     0.5000   -0.0019
##    440        0.5028             nan     0.5000   -0.0067
##    460        0.5108             nan     0.5000   -0.0048
##    480        0.4987             nan     0.5000   -0.0041
##    500        0.4975             nan     0.5000   -0.0073
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1269             nan     0.5000    0.0629
##      2        1.0298             nan     0.5000    0.0316
##      3        0.9771             nan     0.5000    0.0092
##      4        0.9309             nan     0.5000    0.0096
##      5        0.9054             nan     0.5000    0.0081
##      6        0.8939             nan     0.5000   -0.0130
##      7        0.8749             nan     0.5000   -0.0020
##      8        0.8589             nan     0.5000   -0.0011
##      9        0.8462             nan     0.5000   -0.0083
##     10        0.8405             nan     0.5000   -0.0087
##     20        0.8078             nan     0.5000   -0.0125
##     40           inf             nan     0.5000       nan
##     60           inf             nan     0.5000   -0.0002
##     80           inf             nan     0.5000   -0.0146
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000   -0.0066
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000   -0.0008
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1248             nan     0.5000    0.0629
##      2        1.0353             nan     0.5000    0.0378
##      3        0.9813             nan     0.5000    0.0262
##      4        0.9417             nan     0.5000    0.0077
##      5        0.9228             nan     0.5000   -0.0095
##      6        0.9040             nan     0.5000   -0.0069
##      7        0.8857             nan     0.5000    0.0005
##      8        0.8719             nan     0.5000   -0.0040
##      9        0.8497             nan     0.5000   -0.0047
##     10        0.8379             nan     0.5000    0.0003
##     20        0.7621             nan     0.5000   -0.0186
##     40        0.6666             nan     0.5000   -0.0149
##     60        0.5674             nan     0.5000   -0.0035
##     80        0.5019             nan     0.5000   -0.0097
##    100        0.4435             nan     0.5000   -0.0038
##    120        0.4013             nan     0.5000   -0.0013
##    140        0.3580             nan     0.5000   -0.0026
##    160        0.3130             nan     0.5000   -0.0061
##    180        0.2672             nan     0.5000   -0.0001
##    200        0.2373             nan     0.5000   -0.0009
##    220        0.2193             nan     0.5000   -0.0042
##    240        0.1938             nan     0.5000   -0.0031
##    260        0.1805             nan     0.5000   -0.0028
##    280        0.1611             nan     0.5000   -0.0002
##    300        0.1497             nan     0.5000   -0.0016
##    320        0.1336             nan     0.5000   -0.0010
##    340        0.1196             nan     0.5000   -0.0012
##    360        0.1121             nan     0.5000   -0.0027
##    380        0.1004             nan     0.5000   -0.0017
##    400        0.0899             nan     0.5000   -0.0005
##    420        0.0846             nan     0.5000   -0.0016
##    440        0.0797             nan     0.5000   -0.0004
##    460        0.0743             nan     0.5000   -0.0011
##    480        0.0689             nan     0.5000   -0.0008
##    500        0.0623             nan     0.5000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1189             nan     0.5000    0.0628
##      2        1.0290             nan     0.5000    0.0325
##      3        0.9552             nan     0.5000    0.0187
##      4        0.9360             nan     0.5000   -0.0108
##      5        0.9202             nan     0.5000   -0.0000
##      6        0.8888             nan     0.5000    0.0059
##      7        0.8765             nan     0.5000   -0.0012
##      8        0.8709             nan     0.5000   -0.0077
##      9        0.8613             nan     0.5000   -0.0039
##     10        0.8509             nan     0.5000   -0.0034
##     20        0.7690             nan     0.5000   -0.0108
##     40        0.6755             nan     0.5000   -0.0006
##     60        0.6028             nan     0.5000   -0.0041
##     80        0.5383             nan     0.5000   -0.0057
##    100        0.4842             nan     0.5000   -0.0070
##    120        0.4377             nan     0.5000   -0.0041
##    140        0.3753             nan     0.5000   -0.0050
##    160        0.3429             nan     0.5000   -0.0023
##    180        0.3148             nan     0.5000   -0.0101
##    200        0.2705             nan     0.5000   -0.0046
##    220        0.2512             nan     0.5000   -0.0073
##    240        0.2245             nan     0.5000   -0.0053
##    260        0.1997             nan     0.5000   -0.0026
##    280        0.1834             nan     0.5000   -0.0011
##    300        0.1715             nan     0.5000   -0.0021
##    320        0.1576             nan     0.5000   -0.0029
##    340        0.1507             nan     0.5000   -0.0005
##    360        0.1354             nan     0.5000   -0.0021
##    380        0.1273             nan     0.5000   -0.0008
##    400        0.1148             nan     0.5000   -0.0014
##    420        0.1067             nan     0.5000   -0.0017
##    440        0.0966             nan     0.5000   -0.0017
##    460        0.0897             nan     0.5000   -0.0011
##    480        0.0834             nan     0.5000   -0.0003
##    500        0.0771             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0963             nan     0.5000    0.0837
##      2        1.0021             nan     0.5000    0.0344
##      3        0.9407             nan     0.5000    0.0253
##      4        0.9107             nan     0.5000   -0.0076
##      5        0.8786             nan     0.5000    0.0078
##      6        0.8486             nan     0.5000    0.0041
##      7        0.8420             nan     0.5000   -0.0252
##      8        0.8498             nan     0.5000   -0.0377
##      9        0.8061             nan     0.5000    0.0042
##     10        0.8046             nan     0.5000   -0.0165
##     20        4.2210             nan     0.5000   -0.0190
##     40           inf             nan     0.5000       nan
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0865             nan     0.5000    0.0719
##      2        0.9784             nan     0.5000    0.0440
##      3        0.9350             nan     0.5000    0.0043
##      4        0.9016             nan     0.5000    0.0026
##      5        0.8729             nan     0.5000    0.0010
##      6        0.8461             nan     0.5000    0.0023
##      7        0.8296             nan     0.5000   -0.0020
##      8        0.8145             nan     0.5000   -0.0075
##      9        0.8094             nan     0.5000   -0.0162
##     10        0.7949             nan     0.5000   -0.0071
##     20        0.6954             nan     0.5000   -0.0138
##     40        0.5565             nan     0.5000   -0.0077
##     60        0.4544             nan     0.5000   -0.0036
##     80        0.3758             nan     0.5000   -0.0068
##    100        0.3066             nan     0.5000   -0.0084
##    120        0.2506             nan     0.5000   -0.0053
##    140        0.2056             nan     0.5000   -0.0018
##    160        0.1805             nan     0.5000   -0.0033
##    180        0.1578             nan     0.5000   -0.0004
##    200        0.1391             nan     0.5000   -0.0051
##    220        0.1170             nan     0.5000   -0.0005
##    240        0.1030             nan     0.5000   -0.0001
##    260        0.0850             nan     0.5000   -0.0004
##    280        0.0717             nan     0.5000   -0.0005
##    300        0.0635             nan     0.5000   -0.0012
##    320        0.0553             nan     0.5000   -0.0012
##    340        0.0474             nan     0.5000   -0.0003
##    360        0.0411             nan     0.5000   -0.0006
##    380        0.0351             nan     0.5000   -0.0004
##    400        0.0316             nan     0.5000   -0.0004
##    420        0.0277             nan     0.5000   -0.0001
##    440        0.0238             nan     0.5000   -0.0000
##    460        0.0209             nan     0.5000   -0.0003
##    480        0.0185             nan     0.5000   -0.0003
##    500        0.0160             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0889             nan     0.5000    0.0896
##      2        0.9933             nan     0.5000    0.0281
##      3        0.9341             nan     0.5000    0.0243
##      4        0.8908             nan     0.5000    0.0117
##      5        0.8537             nan     0.5000    0.0079
##      6        0.8351             nan     0.5000   -0.0093
##      7        0.8196             nan     0.5000   -0.0041
##      8        0.8015             nan     0.5000   -0.0029
##      9        0.8001             nan     0.5000   -0.0172
##     10        0.7872             nan     0.5000   -0.0098
##     20        0.7132             nan     0.5000   -0.0143
##     40        0.5931             nan     0.5000   -0.0193
##     60        0.4690             nan     0.5000   -0.0120
##     80        0.3797             nan     0.5000   -0.0015
##    100        0.3143             nan     0.5000   -0.0043
##    120        0.2666             nan     0.5000   -0.0077
##    140        0.2350             nan     0.5000   -0.0095
##    160        0.1756             nan     0.5000   -0.0011
##    180        0.1448             nan     0.5000   -0.0005
##    200        0.1225             nan     0.5000   -0.0016
##    220        0.1054             nan     0.5000   -0.0016
##    240        0.0889             nan     0.5000   -0.0004
##    260        0.0760             nan     0.5000   -0.0005
##    280        0.0647             nan     0.5000   -0.0005
##    300        0.0576             nan     0.5000   -0.0008
##    320        0.0492             nan     0.5000   -0.0022
##    340        0.0416             nan     0.5000   -0.0018
##    360        0.0363             nan     0.5000   -0.0011
##    380        0.0316             nan     0.5000   -0.0001
##    400        0.0272             nan     0.5000   -0.0003
##    420        0.0231             nan     0.5000   -0.0002
##    440        0.0211             nan     0.5000   -0.0001
##    460        0.0186             nan     0.5000   -0.0001
##    480        0.0162             nan     0.5000   -0.0001
##    500        0.0145             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1207             nan     1.0000    0.0569
##      2        1.0805             nan     1.0000   -0.0164
##      3        1.0173             nan     1.0000    0.0313
##      4        1.2356             nan     1.0000   -0.2446
##      5        1.2268             nan     1.0000   -0.0308
##      6   179180.4410             nan     1.0000 -119280.2570
##      7   179180.4340             nan     1.0000   -0.0279
##      8   179180.4321             nan     1.0000   -0.0270
##      9   179180.4124             nan     1.0000   -0.0038
##     10   179180.4034             nan     1.0000   -0.0103
##     20   179181.9246             nan     1.0000   -0.0324
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1243             nan     1.0000    0.0765
##      2        1.0619             nan     1.0000    0.0149
##      3        0.9989             nan     1.0000    0.0121
##      4        1.0297             nan     1.0000   -0.0496
##      5        0.9959             nan     1.0000    0.0128
##      6        0.9916             nan     1.0000   -0.0196
##      7        0.9714             nan     1.0000    0.0034
##      8        0.9544             nan     1.0000   -0.0042
##      9        0.9504             nan     1.0000   -0.0248
##     10        0.9248             nan     1.0000   -0.0009
##     20        0.8813             nan     1.0000   -0.0240
##     40    38240.1096             nan     1.0000   -0.0043
##     60    38240.0572             nan     1.0000   -0.0092
##     80    38240.0558             nan     1.0000   -0.0078
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1225             nan     1.0000    0.0605
##      2        1.0468             nan     1.0000    0.0277
##      3        0.9953             nan     1.0000    0.0261
##      4        0.9714             nan     1.0000    0.0020
##      5        0.9612             nan     1.0000    0.0008
##      6        0.9510             nan     1.0000   -0.0112
##      7        0.9640             nan     1.0000   -0.0310
##      8        0.9599             nan     1.0000   -0.0342
##      9        0.9431             nan     1.0000    0.0061
##     10        0.9562             nan     1.0000   -0.0480
##     20        0.8570             nan     1.0000   -0.0117
##     40        0.8162             nan     1.0000   -0.0353
##     60        0.8376             nan     1.0000   -0.0307
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140    96295.9951             nan     1.0000   -0.0061
##    160    96296.0082             nan     1.0000   -0.0399
##    180    96295.7469             nan     1.0000   -0.0153
##    200    96295.7446             nan     1.0000   -0.0144
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0480             nan     1.0000    0.0919
##      2        0.9294             nan     1.0000    0.0405
##      3        0.9028             nan     1.0000   -0.0113
##      4        0.8962             nan     1.0000   -0.0337
##      5        0.8764             nan     1.0000    0.0016
##      6        0.8690             nan     1.0000   -0.0271
##      7        0.9010             nan     1.0000   -0.0553
##      8        1.3792             nan     1.0000   -0.5590
##      9        1.3751             nan     1.0000   -0.0055
##     10           inf             nan     1.0000   -0.0210
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000   -0.0005
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0706             nan     1.0000    0.0802
##      2        1.0014             nan     1.0000   -0.0144
##      3        0.9670             nan     1.0000   -0.0027
##      4        0.9649             nan     1.0000   -0.0368
##      5        0.9514             nan     1.0000   -0.0211
##      6        0.9263             nan     1.0000   -0.0055
##      7        0.9259             nan     1.0000   -0.0213
##      8        0.9091             nan     1.0000   -0.0122
##      9        0.9157             nan     1.0000   -0.0324
##     10        0.8885             nan     1.0000   -0.0131
##     20        0.8847             nan     1.0000   -0.0498
##     40        0.8151             nan     1.0000   -0.0830
##     60        0.6858             nan     1.0000   -0.0414
##     80        0.7143             nan     1.0000   -0.0081
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0715             nan     1.0000    0.0771
##      2        0.9910             nan     1.0000    0.0196
##      3        0.9958             nan     1.0000   -0.0212
##      4        0.9909             nan     1.0000   -0.0134
##      5        1.0280             nan     1.0000   -0.0765
##      6        1.0700             nan     1.0000   -0.0880
##      7        1.1550             nan     1.0000   -0.1293
##      8        1.0896             nan     1.0000    0.0233
##      9        1.0933             nan     1.0000   -0.0358
##     10        1.0651             nan     1.0000   -0.0239
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0321             nan     1.0000    0.0854
##      2        0.9941             nan     1.0000   -0.0432
##      3        0.9880             nan     1.0000   -0.0320
##      4        0.9327             nan     1.0000   -0.0048
##      5        0.8900             nan     1.0000   -0.0159
##      6        0.8561             nan     1.0000   -0.0066
##      7        0.9030             nan     1.0000   -0.0808
##      8        0.9485             nan     1.0000   -0.0888
##      9        0.9278             nan     1.0000   -0.0117
##     10        0.9544             nan     1.0000   -0.0597
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0661             nan     1.0000    0.0452
##      2        1.0255             nan     1.0000   -0.0243
##      3        0.9304             nan     1.0000    0.0408
##      4        0.8814             nan     1.0000   -0.0097
##      5        0.8512             nan     1.0000   -0.0067
##      6        0.8787             nan     1.0000   -0.0644
##      7        0.8999             nan     1.0000   -0.0632
##      8        0.8778             nan     1.0000   -0.0090
##      9        0.9325             nan     1.0000   -0.1167
##     10        0.9023             nan     1.0000   -0.0079
##     20           inf             nan     1.0000      -inf
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1077             nan     1.0000    0.0518
##      2        1.0641             nan     1.0000   -0.0350
##      3        1.0998             nan     1.0000   -0.0680
##      4        1.0450             nan     1.0000   -0.0134
##      5        0.9829             nan     1.0000   -0.0041
##      6        1.0006             nan     1.0000   -0.0671
##      7        0.9956             nan     1.0000   -0.0557
##      8        0.9968             nan     1.0000   -0.0686
##      9        0.9328             nan     1.0000    0.0124
##     10        0.9480             nan     1.0000   -0.0537
##     20        1.5518             nan     1.0000   -0.1314
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0001
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2856             nan     0.0010    0.0002
##     40        1.2783             nan     0.0010    0.0002
##     60        1.2715             nan     0.0010    0.0002
##     80        1.2647             nan     0.0010    0.0002
##    100        1.2579             nan     0.0010    0.0001
##    120        1.2513             nan     0.0010    0.0001
##    140        1.2451             nan     0.0010    0.0001
##    160        1.2390             nan     0.0010    0.0001
##    180        1.2333             nan     0.0010    0.0001
##    200        1.2275             nan     0.0010    0.0001
##    220        1.2218             nan     0.0010    0.0001
##    240        1.2167             nan     0.0010    0.0001
##    260        1.2114             nan     0.0010    0.0001
##    280        1.2063             nan     0.0010    0.0001
##    300        1.2015             nan     0.0010    0.0001
##    320        1.1969             nan     0.0010    0.0001
##    340        1.1922             nan     0.0010    0.0001
##    360        1.1877             nan     0.0010    0.0001
##    380        1.1833             nan     0.0010    0.0001
##    400        1.1792             nan     0.0010    0.0001
##    420        1.1751             nan     0.0010    0.0001
##    440        1.1709             nan     0.0010    0.0001
##    460        1.1669             nan     0.0010    0.0001
##    480        1.1630             nan     0.0010    0.0001
##    500        1.1592             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2906             nan     0.0010    0.0002
##      8        1.2902             nan     0.0010    0.0002
##      9        1.2898             nan     0.0010    0.0002
##     10        1.2894             nan     0.0010    0.0002
##     20        1.2855             nan     0.0010    0.0002
##     40        1.2781             nan     0.0010    0.0002
##     60        1.2708             nan     0.0010    0.0001
##     80        1.2640             nan     0.0010    0.0002
##    100        1.2575             nan     0.0010    0.0002
##    120        1.2510             nan     0.0010    0.0001
##    140        1.2448             nan     0.0010    0.0001
##    160        1.2389             nan     0.0010    0.0002
##    180        1.2329             nan     0.0010    0.0001
##    200        1.2272             nan     0.0010    0.0001
##    220        1.2216             nan     0.0010    0.0001
##    240        1.2165             nan     0.0010    0.0001
##    260        1.2114             nan     0.0010    0.0001
##    280        1.2065             nan     0.0010    0.0001
##    300        1.2014             nan     0.0010    0.0001
##    320        1.1967             nan     0.0010    0.0001
##    340        1.1921             nan     0.0010    0.0001
##    360        1.1874             nan     0.0010    0.0001
##    380        1.1831             nan     0.0010    0.0001
##    400        1.1789             nan     0.0010    0.0001
##    420        1.1747             nan     0.0010    0.0001
##    440        1.1708             nan     0.0010    0.0001
##    460        1.1668             nan     0.0010    0.0001
##    480        1.1629             nan     0.0010    0.0001
##    500        1.1589             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2860             nan     0.0010    0.0002
##     40        1.2785             nan     0.0010    0.0002
##     60        1.2715             nan     0.0010    0.0001
##     80        1.2647             nan     0.0010    0.0001
##    100        1.2580             nan     0.0010    0.0001
##    120        1.2515             nan     0.0010    0.0002
##    140        1.2452             nan     0.0010    0.0001
##    160        1.2389             nan     0.0010    0.0001
##    180        1.2330             nan     0.0010    0.0001
##    200        1.2271             nan     0.0010    0.0001
##    220        1.2216             nan     0.0010    0.0001
##    240        1.2164             nan     0.0010    0.0001
##    260        1.2112             nan     0.0010    0.0001
##    280        1.2063             nan     0.0010    0.0001
##    300        1.2015             nan     0.0010    0.0001
##    320        1.1969             nan     0.0010    0.0001
##    340        1.1924             nan     0.0010    0.0001
##    360        1.1879             nan     0.0010    0.0001
##    380        1.1836             nan     0.0010    0.0001
##    400        1.1792             nan     0.0010    0.0001
##    420        1.1750             nan     0.0010    0.0001
##    440        1.1709             nan     0.0010    0.0001
##    460        1.1670             nan     0.0010    0.0001
##    480        1.1632             nan     0.0010    0.0001
##    500        1.1594             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0003
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2903             nan     0.0010    0.0002
##      7        1.2898             nan     0.0010    0.0002
##      8        1.2893             nan     0.0010    0.0002
##      9        1.2888             nan     0.0010    0.0002
##     10        1.2883             nan     0.0010    0.0003
##     20        1.2832             nan     0.0010    0.0002
##     40        1.2734             nan     0.0010    0.0002
##     60        1.2642             nan     0.0010    0.0002
##     80        1.2551             nan     0.0010    0.0002
##    100        1.2465             nan     0.0010    0.0002
##    120        1.2381             nan     0.0010    0.0002
##    140        1.2298             nan     0.0010    0.0002
##    160        1.2220             nan     0.0010    0.0002
##    180        1.2142             nan     0.0010    0.0002
##    200        1.2065             nan     0.0010    0.0002
##    220        1.1993             nan     0.0010    0.0002
##    240        1.1921             nan     0.0010    0.0002
##    260        1.1851             nan     0.0010    0.0001
##    280        1.1783             nan     0.0010    0.0002
##    300        1.1714             nan     0.0010    0.0002
##    320        1.1650             nan     0.0010    0.0001
##    340        1.1588             nan     0.0010    0.0001
##    360        1.1527             nan     0.0010    0.0001
##    380        1.1470             nan     0.0010    0.0001
##    400        1.1412             nan     0.0010    0.0001
##    420        1.1356             nan     0.0010    0.0001
##    440        1.1303             nan     0.0010    0.0001
##    460        1.1248             nan     0.0010    0.0001
##    480        1.1194             nan     0.0010    0.0001
##    500        1.1144             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2897             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2886             nan     0.0010    0.0002
##     10        1.2881             nan     0.0010    0.0002
##     20        1.2832             nan     0.0010    0.0002
##     40        1.2735             nan     0.0010    0.0002
##     60        1.2640             nan     0.0010    0.0002
##     80        1.2550             nan     0.0010    0.0002
##    100        1.2461             nan     0.0010    0.0002
##    120        1.2375             nan     0.0010    0.0002
##    140        1.2292             nan     0.0010    0.0002
##    160        1.2211             nan     0.0010    0.0002
##    180        1.2132             nan     0.0010    0.0002
##    200        1.2058             nan     0.0010    0.0002
##    220        1.1985             nan     0.0010    0.0001
##    240        1.1913             nan     0.0010    0.0002
##    260        1.1846             nan     0.0010    0.0001
##    280        1.1778             nan     0.0010    0.0001
##    300        1.1713             nan     0.0010    0.0001
##    320        1.1650             nan     0.0010    0.0001
##    340        1.1588             nan     0.0010    0.0001
##    360        1.1529             nan     0.0010    0.0001
##    380        1.1469             nan     0.0010    0.0001
##    400        1.1412             nan     0.0010    0.0001
##    420        1.1355             nan     0.0010    0.0001
##    440        1.1301             nan     0.0010    0.0001
##    460        1.1246             nan     0.0010    0.0001
##    480        1.1194             nan     0.0010    0.0001
##    500        1.1143             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0003
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2908             nan     0.0010    0.0002
##      6        1.2903             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2889             nan     0.0010    0.0002
##     10        1.2884             nan     0.0010    0.0002
##     20        1.2834             nan     0.0010    0.0002
##     40        1.2736             nan     0.0010    0.0002
##     60        1.2641             nan     0.0010    0.0002
##     80        1.2551             nan     0.0010    0.0002
##    100        1.2463             nan     0.0010    0.0002
##    120        1.2376             nan     0.0010    0.0002
##    140        1.2295             nan     0.0010    0.0002
##    160        1.2213             nan     0.0010    0.0002
##    180        1.2134             nan     0.0010    0.0002
##    200        1.2060             nan     0.0010    0.0001
##    220        1.1985             nan     0.0010    0.0002
##    240        1.1913             nan     0.0010    0.0001
##    260        1.1846             nan     0.0010    0.0002
##    280        1.1778             nan     0.0010    0.0001
##    300        1.1713             nan     0.0010    0.0002
##    320        1.1651             nan     0.0010    0.0001
##    340        1.1589             nan     0.0010    0.0001
##    360        1.1528             nan     0.0010    0.0001
##    380        1.1468             nan     0.0010    0.0001
##    400        1.1409             nan     0.0010    0.0001
##    420        1.1354             nan     0.0010    0.0001
##    440        1.1299             nan     0.0010    0.0001
##    460        1.1245             nan     0.0010    0.0001
##    480        1.1190             nan     0.0010    0.0001
##    500        1.1140             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2927             nan     0.0010    0.0002
##      2        1.2921             nan     0.0010    0.0003
##      3        1.2915             nan     0.0010    0.0003
##      4        1.2909             nan     0.0010    0.0003
##      5        1.2903             nan     0.0010    0.0003
##      6        1.2897             nan     0.0010    0.0003
##      7        1.2891             nan     0.0010    0.0003
##      8        1.2886             nan     0.0010    0.0003
##      9        1.2880             nan     0.0010    0.0003
##     10        1.2875             nan     0.0010    0.0003
##     20        1.2815             nan     0.0010    0.0003
##     40        1.2705             nan     0.0010    0.0002
##     60        1.2597             nan     0.0010    0.0002
##     80        1.2492             nan     0.0010    0.0003
##    100        1.2392             nan     0.0010    0.0003
##    120        1.2294             nan     0.0010    0.0002
##    140        1.2198             nan     0.0010    0.0002
##    160        1.2105             nan     0.0010    0.0002
##    180        1.2014             nan     0.0010    0.0002
##    200        1.1926             nan     0.0010    0.0002
##    220        1.1843             nan     0.0010    0.0002
##    240        1.1762             nan     0.0010    0.0002
##    260        1.1682             nan     0.0010    0.0002
##    280        1.1603             nan     0.0010    0.0001
##    300        1.1529             nan     0.0010    0.0001
##    320        1.1455             nan     0.0010    0.0001
##    340        1.1383             nan     0.0010    0.0002
##    360        1.1314             nan     0.0010    0.0001
##    380        1.1247             nan     0.0010    0.0001
##    400        1.1183             nan     0.0010    0.0001
##    420        1.1119             nan     0.0010    0.0001
##    440        1.1055             nan     0.0010    0.0001
##    460        1.0996             nan     0.0010    0.0001
##    480        1.0937             nan     0.0010    0.0001
##    500        1.0878             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2927             nan     0.0010    0.0003
##      2        1.2921             nan     0.0010    0.0003
##      3        1.2915             nan     0.0010    0.0003
##      4        1.2909             nan     0.0010    0.0003
##      5        1.2903             nan     0.0010    0.0003
##      6        1.2898             nan     0.0010    0.0003
##      7        1.2892             nan     0.0010    0.0002
##      8        1.2886             nan     0.0010    0.0003
##      9        1.2881             nan     0.0010    0.0003
##     10        1.2875             nan     0.0010    0.0002
##     20        1.2818             nan     0.0010    0.0003
##     40        1.2704             nan     0.0010    0.0002
##     60        1.2595             nan     0.0010    0.0002
##     80        1.2489             nan     0.0010    0.0002
##    100        1.2386             nan     0.0010    0.0002
##    120        1.2286             nan     0.0010    0.0002
##    140        1.2193             nan     0.0010    0.0002
##    160        1.2099             nan     0.0010    0.0002
##    180        1.2012             nan     0.0010    0.0002
##    200        1.1925             nan     0.0010    0.0002
##    220        1.1840             nan     0.0010    0.0002
##    240        1.1758             nan     0.0010    0.0002
##    260        1.1680             nan     0.0010    0.0001
##    280        1.1602             nan     0.0010    0.0001
##    300        1.1529             nan     0.0010    0.0002
##    320        1.1457             nan     0.0010    0.0002
##    340        1.1388             nan     0.0010    0.0001
##    360        1.1318             nan     0.0010    0.0001
##    380        1.1252             nan     0.0010    0.0001
##    400        1.1188             nan     0.0010    0.0002
##    420        1.1125             nan     0.0010    0.0002
##    440        1.1062             nan     0.0010    0.0001
##    460        1.0999             nan     0.0010    0.0001
##    480        1.0940             nan     0.0010    0.0001
##    500        1.0883             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2927             nan     0.0010    0.0002
##      2        1.2921             nan     0.0010    0.0003
##      3        1.2915             nan     0.0010    0.0003
##      4        1.2908             nan     0.0010    0.0003
##      5        1.2902             nan     0.0010    0.0003
##      6        1.2896             nan     0.0010    0.0003
##      7        1.2890             nan     0.0010    0.0003
##      8        1.2885             nan     0.0010    0.0003
##      9        1.2879             nan     0.0010    0.0003
##     10        1.2872             nan     0.0010    0.0003
##     20        1.2812             nan     0.0010    0.0003
##     40        1.2701             nan     0.0010    0.0002
##     60        1.2592             nan     0.0010    0.0002
##     80        1.2486             nan     0.0010    0.0002
##    100        1.2384             nan     0.0010    0.0002
##    120        1.2283             nan     0.0010    0.0002
##    140        1.2188             nan     0.0010    0.0002
##    160        1.2100             nan     0.0010    0.0002
##    180        1.2011             nan     0.0010    0.0001
##    200        1.1925             nan     0.0010    0.0002
##    220        1.1842             nan     0.0010    0.0002
##    240        1.1759             nan     0.0010    0.0002
##    260        1.1677             nan     0.0010    0.0002
##    280        1.1600             nan     0.0010    0.0002
##    300        1.1525             nan     0.0010    0.0002
##    320        1.1454             nan     0.0010    0.0001
##    340        1.1383             nan     0.0010    0.0002
##    360        1.1312             nan     0.0010    0.0001
##    380        1.1245             nan     0.0010    0.0001
##    400        1.1178             nan     0.0010    0.0001
##    420        1.1115             nan     0.0010    0.0001
##    440        1.1052             nan     0.0010    0.0002
##    460        1.0993             nan     0.0010    0.0001
##    480        1.0935             nan     0.0010    0.0001
##    500        1.0877             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2575             nan     0.1000    0.0171
##      2        1.2271             nan     0.1000    0.0146
##      3        1.1978             nan     0.1000    0.0127
##      4        1.1756             nan     0.1000    0.0103
##      5        1.1523             nan     0.1000    0.0086
##      6        1.1321             nan     0.1000    0.0061
##      7        1.1154             nan     0.1000    0.0073
##      8        1.1017             nan     0.1000    0.0056
##      9        1.0885             nan     0.1000    0.0048
##     10        1.0746             nan     0.1000    0.0064
##     20        0.9860             nan     0.1000    0.0010
##     40        0.9036             nan     0.1000   -0.0010
##     60        0.8612             nan     0.1000   -0.0001
##     80        0.8307             nan     0.1000   -0.0003
##    100        0.8130             nan     0.1000   -0.0014
##    120        0.7960             nan     0.1000   -0.0009
##    140        0.7849             nan     0.1000   -0.0007
##    160        0.7737             nan     0.1000   -0.0013
##    180        0.7668             nan     0.1000   -0.0006
##    200        0.7600             nan     0.1000   -0.0009
##    220        0.7503             nan     0.1000   -0.0010
##    240        0.7439             nan     0.1000   -0.0008
##    260        0.7353             nan     0.1000   -0.0001
##    280        0.7301             nan     0.1000   -0.0010
##    300        0.7255             nan     0.1000   -0.0013
##    320        0.7181             nan     0.1000   -0.0008
##    340        0.7113             nan     0.1000   -0.0011
##    360        0.7050             nan     0.1000   -0.0001
##    380        0.7001             nan     0.1000   -0.0007
##    400        0.6943             nan     0.1000   -0.0017
##    420        0.6888             nan     0.1000   -0.0010
##    440        0.6831             nan     0.1000   -0.0007
##    460        0.6769             nan     0.1000   -0.0006
##    480        0.6749             nan     0.1000   -0.0004
##    500        0.6696             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2582             nan     0.1000    0.0181
##      2        1.2276             nan     0.1000    0.0144
##      3        1.1959             nan     0.1000    0.0117
##      4        1.1704             nan     0.1000    0.0105
##      5        1.1509             nan     0.1000    0.0064
##      6        1.1329             nan     0.1000    0.0067
##      7        1.1183             nan     0.1000    0.0051
##      8        1.1048             nan     0.1000    0.0053
##      9        1.0909             nan     0.1000    0.0033
##     10        1.0768             nan     0.1000    0.0068
##     20        0.9808             nan     0.1000    0.0036
##     40        0.8989             nan     0.1000    0.0003
##     60        0.8556             nan     0.1000   -0.0003
##     80        0.8320             nan     0.1000   -0.0010
##    100        0.8104             nan     0.1000   -0.0005
##    120        0.7949             nan     0.1000    0.0002
##    140        0.7837             nan     0.1000   -0.0011
##    160        0.7716             nan     0.1000    0.0000
##    180        0.7642             nan     0.1000   -0.0005
##    200        0.7548             nan     0.1000   -0.0008
##    220        0.7462             nan     0.1000    0.0000
##    240        0.7389             nan     0.1000   -0.0005
##    260        0.7339             nan     0.1000   -0.0010
##    280        0.7283             nan     0.1000   -0.0008
##    300        0.7230             nan     0.1000   -0.0007
##    320        0.7170             nan     0.1000   -0.0006
##    340        0.7105             nan     0.1000   -0.0003
##    360        0.7047             nan     0.1000   -0.0006
##    380        0.6992             nan     0.1000   -0.0009
##    400        0.6942             nan     0.1000   -0.0017
##    420        0.6865             nan     0.1000   -0.0007
##    440        0.6813             nan     0.1000   -0.0010
##    460        0.6771             nan     0.1000   -0.0008
##    480        0.6720             nan     0.1000   -0.0002
##    500        0.6679             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2538             nan     0.1000    0.0143
##      2        1.2210             nan     0.1000    0.0155
##      3        1.1950             nan     0.1000    0.0106
##      4        1.1768             nan     0.1000    0.0080
##      5        1.1562             nan     0.1000    0.0090
##      6        1.1357             nan     0.1000    0.0082
##      7        1.1179             nan     0.1000    0.0065
##      8        1.1016             nan     0.1000    0.0069
##      9        1.0874             nan     0.1000    0.0063
##     10        1.0758             nan     0.1000    0.0043
##     20        0.9869             nan     0.1000    0.0019
##     40        0.8996             nan     0.1000    0.0006
##     60        0.8559             nan     0.1000    0.0003
##     80        0.8327             nan     0.1000   -0.0001
##    100        0.8127             nan     0.1000   -0.0012
##    120        0.7988             nan     0.1000   -0.0010
##    140        0.7847             nan     0.1000   -0.0002
##    160        0.7730             nan     0.1000   -0.0006
##    180        0.7656             nan     0.1000   -0.0014
##    200        0.7572             nan     0.1000   -0.0012
##    220        0.7493             nan     0.1000   -0.0011
##    240        0.7423             nan     0.1000   -0.0002
##    260        0.7362             nan     0.1000   -0.0005
##    280        0.7286             nan     0.1000   -0.0007
##    300        0.7227             nan     0.1000   -0.0010
##    320        0.7170             nan     0.1000   -0.0012
##    340        0.7108             nan     0.1000   -0.0008
##    360        0.7055             nan     0.1000   -0.0007
##    380        0.7002             nan     0.1000   -0.0009
##    400        0.6969             nan     0.1000   -0.0011
##    420        0.6898             nan     0.1000   -0.0009
##    440        0.6846             nan     0.1000   -0.0011
##    460        0.6810             nan     0.1000   -0.0007
##    480        0.6769             nan     0.1000   -0.0010
##    500        0.6715             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2467             nan     0.1000    0.0221
##      2        1.2026             nan     0.1000    0.0172
##      3        1.1676             nan     0.1000    0.0163
##      4        1.1333             nan     0.1000    0.0150
##      5        1.1094             nan     0.1000    0.0087
##      6        1.0855             nan     0.1000    0.0117
##      7        1.0660             nan     0.1000    0.0100
##      8        1.0446             nan     0.1000    0.0092
##      9        1.0306             nan     0.1000    0.0050
##     10        1.0147             nan     0.1000    0.0074
##     20        0.9222             nan     0.1000    0.0004
##     40        0.8268             nan     0.1000   -0.0003
##     60        0.7808             nan     0.1000   -0.0008
##     80        0.7475             nan     0.1000   -0.0010
##    100        0.7163             nan     0.1000   -0.0013
##    120        0.6904             nan     0.1000   -0.0005
##    140        0.6664             nan     0.1000   -0.0011
##    160        0.6417             nan     0.1000   -0.0005
##    180        0.6220             nan     0.1000   -0.0015
##    200        0.6032             nan     0.1000   -0.0013
##    220        0.5798             nan     0.1000   -0.0011
##    240        0.5654             nan     0.1000   -0.0018
##    260        0.5478             nan     0.1000   -0.0008
##    280        0.5329             nan     0.1000   -0.0008
##    300        0.5192             nan     0.1000   -0.0013
##    320        0.5045             nan     0.1000   -0.0007
##    340        0.4901             nan     0.1000   -0.0012
##    360        0.4781             nan     0.1000   -0.0014
##    380        0.4678             nan     0.1000   -0.0011
##    400        0.4572             nan     0.1000   -0.0006
##    420        0.4454             nan     0.1000   -0.0017
##    440        0.4357             nan     0.1000   -0.0010
##    460        0.4253             nan     0.1000   -0.0006
##    480        0.4174             nan     0.1000   -0.0005
##    500        0.4092             nan     0.1000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2421             nan     0.1000    0.0209
##      2        1.2044             nan     0.1000    0.0163
##      3        1.1631             nan     0.1000    0.0165
##      4        1.1308             nan     0.1000    0.0145
##      5        1.1065             nan     0.1000    0.0116
##      6        1.0843             nan     0.1000    0.0107
##      7        1.0622             nan     0.1000    0.0091
##      8        1.0439             nan     0.1000    0.0065
##      9        1.0268             nan     0.1000    0.0072
##     10        1.0125             nan     0.1000    0.0045
##     20        0.9158             nan     0.1000    0.0005
##     40        0.8301             nan     0.1000   -0.0014
##     60        0.7842             nan     0.1000   -0.0009
##     80        0.7514             nan     0.1000   -0.0013
##    100        0.7207             nan     0.1000   -0.0013
##    120        0.6942             nan     0.1000   -0.0023
##    140        0.6701             nan     0.1000   -0.0009
##    160        0.6438             nan     0.1000   -0.0010
##    180        0.6223             nan     0.1000   -0.0001
##    200        0.6050             nan     0.1000   -0.0002
##    220        0.5883             nan     0.1000   -0.0012
##    240        0.5716             nan     0.1000   -0.0016
##    260        0.5570             nan     0.1000   -0.0011
##    280        0.5424             nan     0.1000   -0.0012
##    300        0.5265             nan     0.1000   -0.0010
##    320        0.5128             nan     0.1000   -0.0018
##    340        0.4992             nan     0.1000   -0.0020
##    360        0.4867             nan     0.1000   -0.0016
##    380        0.4787             nan     0.1000   -0.0016
##    400        0.4673             nan     0.1000   -0.0003
##    420        0.4516             nan     0.1000   -0.0003
##    440        0.4420             nan     0.1000   -0.0007
##    460        0.4326             nan     0.1000   -0.0007
##    480        0.4227             nan     0.1000   -0.0005
##    500        0.4109             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2469             nan     0.1000    0.0212
##      2        1.2043             nan     0.1000    0.0170
##      3        1.1702             nan     0.1000    0.0144
##      4        1.1396             nan     0.1000    0.0128
##      5        1.1135             nan     0.1000    0.0098
##      6        1.0842             nan     0.1000    0.0118
##      7        1.0637             nan     0.1000    0.0080
##      8        1.0454             nan     0.1000    0.0081
##      9        1.0265             nan     0.1000    0.0061
##     10        1.0075             nan     0.1000    0.0070
##     20        0.9138             nan     0.1000    0.0006
##     40        0.8305             nan     0.1000   -0.0022
##     60        0.7845             nan     0.1000   -0.0008
##     80        0.7534             nan     0.1000   -0.0017
##    100        0.7245             nan     0.1000   -0.0005
##    120        0.7003             nan     0.1000   -0.0007
##    140        0.6752             nan     0.1000   -0.0018
##    160        0.6571             nan     0.1000   -0.0009
##    180        0.6378             nan     0.1000   -0.0010
##    200        0.6197             nan     0.1000   -0.0006
##    220        0.6016             nan     0.1000   -0.0011
##    240        0.5873             nan     0.1000   -0.0014
##    260        0.5741             nan     0.1000   -0.0003
##    280        0.5575             nan     0.1000   -0.0002
##    300        0.5423             nan     0.1000   -0.0009
##    320        0.5292             nan     0.1000   -0.0011
##    340        0.5175             nan     0.1000   -0.0013
##    360        0.5072             nan     0.1000   -0.0013
##    380        0.4963             nan     0.1000   -0.0013
##    400        0.4854             nan     0.1000   -0.0012
##    420        0.4684             nan     0.1000   -0.0013
##    440        0.4575             nan     0.1000   -0.0011
##    460        0.4475             nan     0.1000   -0.0004
##    480        0.4367             nan     0.1000   -0.0008
##    500        0.4272             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2369             nan     0.1000    0.0233
##      2        1.1870             nan     0.1000    0.0227
##      3        1.1471             nan     0.1000    0.0164
##      4        1.1096             nan     0.1000    0.0165
##      5        1.0800             nan     0.1000    0.0131
##      6        1.0584             nan     0.1000    0.0086
##      7        1.0410             nan     0.1000    0.0052
##      8        1.0151             nan     0.1000    0.0093
##      9        0.9933             nan     0.1000    0.0080
##     10        0.9776             nan     0.1000    0.0063
##     20        0.8695             nan     0.1000    0.0006
##     40        0.7790             nan     0.1000   -0.0017
##     60        0.7071             nan     0.1000   -0.0004
##     80        0.6591             nan     0.1000   -0.0016
##    100        0.6254             nan     0.1000   -0.0006
##    120        0.5905             nan     0.1000   -0.0001
##    140        0.5637             nan     0.1000   -0.0022
##    160        0.5348             nan     0.1000   -0.0006
##    180        0.5112             nan     0.1000   -0.0016
##    200        0.4900             nan     0.1000   -0.0021
##    220        0.4707             nan     0.1000   -0.0020
##    240        0.4480             nan     0.1000   -0.0001
##    260        0.4265             nan     0.1000    0.0001
##    280        0.4068             nan     0.1000   -0.0007
##    300        0.3910             nan     0.1000   -0.0006
##    320        0.3749             nan     0.1000   -0.0007
##    340        0.3620             nan     0.1000   -0.0005
##    360        0.3475             nan     0.1000   -0.0004
##    380        0.3337             nan     0.1000   -0.0011
##    400        0.3208             nan     0.1000   -0.0010
##    420        0.3101             nan     0.1000   -0.0008
##    440        0.2956             nan     0.1000   -0.0006
##    460        0.2855             nan     0.1000   -0.0004
##    480        0.2743             nan     0.1000   -0.0005
##    500        0.2644             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2353             nan     0.1000    0.0263
##      2        1.1880             nan     0.1000    0.0212
##      3        1.1525             nan     0.1000    0.0164
##      4        1.1113             nan     0.1000    0.0169
##      5        1.0813             nan     0.1000    0.0116
##      6        1.0588             nan     0.1000    0.0104
##      7        1.0335             nan     0.1000    0.0105
##      8        1.0154             nan     0.1000    0.0064
##      9        0.9949             nan     0.1000    0.0076
##     10        0.9791             nan     0.1000    0.0052
##     20        0.8742             nan     0.1000    0.0007
##     40        0.7781             nan     0.1000    0.0000
##     60        0.7225             nan     0.1000   -0.0011
##     80        0.6761             nan     0.1000   -0.0013
##    100        0.6354             nan     0.1000   -0.0010
##    120        0.6018             nan     0.1000   -0.0009
##    140        0.5683             nan     0.1000   -0.0017
##    160        0.5408             nan     0.1000   -0.0006
##    180        0.5162             nan     0.1000   -0.0010
##    200        0.4927             nan     0.1000   -0.0006
##    220        0.4700             nan     0.1000   -0.0004
##    240        0.4522             nan     0.1000   -0.0016
##    260        0.4346             nan     0.1000   -0.0015
##    280        0.4154             nan     0.1000   -0.0015
##    300        0.3976             nan     0.1000   -0.0002
##    320        0.3832             nan     0.1000   -0.0014
##    340        0.3636             nan     0.1000   -0.0010
##    360        0.3500             nan     0.1000   -0.0015
##    380        0.3369             nan     0.1000   -0.0004
##    400        0.3270             nan     0.1000   -0.0012
##    420        0.3134             nan     0.1000   -0.0005
##    440        0.3015             nan     0.1000   -0.0003
##    460        0.2897             nan     0.1000   -0.0010
##    480        0.2793             nan     0.1000   -0.0007
##    500        0.2678             nan     0.1000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2370             nan     0.1000    0.0257
##      2        1.1888             nan     0.1000    0.0210
##      3        1.1471             nan     0.1000    0.0163
##      4        1.1112             nan     0.1000    0.0163
##      5        1.0758             nan     0.1000    0.0115
##      6        1.0488             nan     0.1000    0.0080
##      7        1.0244             nan     0.1000    0.0078
##      8        1.0049             nan     0.1000    0.0082
##      9        0.9902             nan     0.1000    0.0060
##     10        0.9750             nan     0.1000    0.0057
##     20        0.8731             nan     0.1000    0.0014
##     40        0.7803             nan     0.1000   -0.0013
##     60        0.7214             nan     0.1000   -0.0008
##     80        0.6751             nan     0.1000   -0.0008
##    100        0.6343             nan     0.1000   -0.0019
##    120        0.5951             nan     0.1000   -0.0007
##    140        0.5679             nan     0.1000   -0.0003
##    160        0.5405             nan     0.1000   -0.0020
##    180        0.5132             nan     0.1000   -0.0012
##    200        0.4879             nan     0.1000   -0.0006
##    220        0.4641             nan     0.1000   -0.0017
##    240        0.4422             nan     0.1000   -0.0000
##    260        0.4265             nan     0.1000   -0.0017
##    280        0.4131             nan     0.1000   -0.0015
##    300        0.3960             nan     0.1000   -0.0013
##    320        0.3818             nan     0.1000   -0.0011
##    340        0.3696             nan     0.1000   -0.0006
##    360        0.3536             nan     0.1000   -0.0010
##    380        0.3372             nan     0.1000   -0.0007
##    400        0.3243             nan     0.1000   -0.0006
##    420        0.3110             nan     0.1000   -0.0009
##    440        0.2989             nan     0.1000   -0.0004
##    460        0.2869             nan     0.1000   -0.0014
##    480        0.2758             nan     0.1000   -0.0004
##    500        0.2647             nan     0.1000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2354             nan     0.2000    0.0338
##      2        1.1837             nan     0.2000    0.0210
##      3        1.1376             nan     0.2000    0.0194
##      4        1.1085             nan     0.2000    0.0134
##      5        1.0846             nan     0.2000    0.0100
##      6        1.0712             nan     0.2000    0.0008
##      7        1.0440             nan     0.2000    0.0064
##      8        1.0263             nan     0.2000    0.0073
##      9        1.0116             nan     0.2000    0.0048
##     10        0.9842             nan     0.2000    0.0103
##     20        0.9027             nan     0.2000   -0.0008
##     40        0.8334             nan     0.2000    0.0002
##     60        0.7998             nan     0.2000   -0.0014
##     80        0.7763             nan     0.2000   -0.0025
##    100        0.7610             nan     0.2000   -0.0007
##    120        0.7400             nan     0.2000   -0.0037
##    140        0.7263             nan     0.2000   -0.0010
##    160        0.7110             nan     0.2000   -0.0010
##    180        0.7026             nan     0.2000   -0.0015
##    200        0.6914             nan     0.2000   -0.0013
##    220        0.6802             nan     0.2000   -0.0020
##    240        0.6735             nan     0.2000   -0.0004
##    260        0.6639             nan     0.2000   -0.0014
##    280        0.6531             nan     0.2000   -0.0006
##    300        0.6450             nan     0.2000   -0.0035
##    320        0.6365             nan     0.2000   -0.0049
##    340        0.6285             nan     0.2000   -0.0029
##    360        0.6222             nan     0.2000   -0.0012
##    380        0.6162             nan     0.2000   -0.0011
##    400        0.6115             nan     0.2000   -0.0040
##    420        0.6057             nan     0.2000   -0.0028
##    440        0.6033             nan     0.2000   -0.0020
##    460        0.6006             nan     0.2000   -0.0034
##    480        0.5929             nan     0.2000   -0.0012
##    500        0.5872             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2197             nan     0.2000    0.0345
##      2        1.1730             nan     0.2000    0.0217
##      3        1.1366             nan     0.2000    0.0147
##      4        1.1058             nan     0.2000    0.0122
##      5        1.0780             nan     0.2000    0.0100
##      6        1.0523             nan     0.2000    0.0116
##      7        1.0350             nan     0.2000    0.0058
##      8        1.0163             nan     0.2000    0.0067
##      9        1.0022             nan     0.2000    0.0054
##     10        0.9816             nan     0.2000    0.0056
##     20        0.9011             nan     0.2000   -0.0002
##     40        0.8341             nan     0.2000   -0.0001
##     60        0.7917             nan     0.2000   -0.0007
##     80        0.7712             nan     0.2000   -0.0024
##    100        0.7569             nan     0.2000   -0.0037
##    120        0.7378             nan     0.2000   -0.0010
##    140        0.7273             nan     0.2000   -0.0020
##    160        0.7149             nan     0.2000   -0.0005
##    180        0.7031             nan     0.2000   -0.0013
##    200        0.6933             nan     0.2000   -0.0017
##    220        0.6810             nan     0.2000   -0.0016
##    240        0.6747             nan     0.2000   -0.0015
##    260        0.6640             nan     0.2000   -0.0021
##    280        0.6566             nan     0.2000   -0.0020
##    300        0.6441             nan     0.2000   -0.0017
##    320        0.6382             nan     0.2000   -0.0012
##    340        0.6350             nan     0.2000   -0.0019
##    360        0.6278             nan     0.2000   -0.0034
##    380        0.6194             nan     0.2000   -0.0003
##    400        0.6113             nan     0.2000   -0.0014
##    420        0.6055             nan     0.2000   -0.0038
##    440        0.5983             nan     0.2000   -0.0012
##    460        0.5929             nan     0.2000   -0.0009
##    480        0.5882             nan     0.2000   -0.0015
##    500        0.5817             nan     0.2000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2249             nan     0.2000    0.0320
##      2        1.1736             nan     0.2000    0.0208
##      3        1.1315             nan     0.2000    0.0196
##      4        1.1094             nan     0.2000    0.0057
##      5        1.0806             nan     0.2000    0.0127
##      6        1.0559             nan     0.2000    0.0082
##      7        1.0319             nan     0.2000    0.0085
##      8        1.0084             nan     0.2000    0.0077
##      9        0.9926             nan     0.2000    0.0037
##     10        0.9747             nan     0.2000    0.0073
##     20        0.8989             nan     0.2000    0.0014
##     40        0.8327             nan     0.2000   -0.0015
##     60        0.7986             nan     0.2000   -0.0017
##     80        0.7801             nan     0.2000   -0.0013
##    100        0.7611             nan     0.2000   -0.0016
##    120        0.7438             nan     0.2000   -0.0022
##    140        0.7293             nan     0.2000   -0.0019
##    160        0.7178             nan     0.2000   -0.0050
##    180        0.7078             nan     0.2000   -0.0026
##    200        0.6955             nan     0.2000   -0.0013
##    220        0.6866             nan     0.2000   -0.0029
##    240        0.6748             nan     0.2000   -0.0004
##    260        0.6656             nan     0.2000   -0.0018
##    280        0.6585             nan     0.2000   -0.0013
##    300        0.6501             nan     0.2000   -0.0029
##    320        0.6440             nan     0.2000   -0.0024
##    340        0.6351             nan     0.2000   -0.0019
##    360        0.6259             nan     0.2000   -0.0013
##    380        0.6212             nan     0.2000   -0.0012
##    400        0.6158             nan     0.2000   -0.0026
##    420        0.6092             nan     0.2000   -0.0027
##    440        0.6031             nan     0.2000   -0.0015
##    460        0.5973             nan     0.2000   -0.0019
##    480        0.5929             nan     0.2000   -0.0017
##    500        0.5886             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2020             nan     0.2000    0.0380
##      2        1.1426             nan     0.2000    0.0243
##      3        1.0938             nan     0.2000    0.0264
##      4        1.0581             nan     0.2000    0.0133
##      5        1.0202             nan     0.2000    0.0162
##      6        0.9921             nan     0.2000    0.0088
##      7        0.9680             nan     0.2000    0.0086
##      8        0.9522             nan     0.2000    0.0028
##      9        0.9333             nan     0.2000    0.0062
##     10        0.9229             nan     0.2000   -0.0008
##     20        0.8281             nan     0.2000   -0.0012
##     40        0.7477             nan     0.2000    0.0001
##     60        0.6946             nan     0.2000   -0.0022
##     80        0.6523             nan     0.2000   -0.0032
##    100        0.6096             nan     0.2000   -0.0012
##    120        0.5744             nan     0.2000   -0.0024
##    140        0.5523             nan     0.2000   -0.0022
##    160        0.5250             nan     0.2000   -0.0015
##    180        0.5064             nan     0.2000   -0.0041
##    200        0.4790             nan     0.2000   -0.0015
##    220        0.4579             nan     0.2000   -0.0005
##    240        0.4374             nan     0.2000   -0.0015
##    260        0.4180             nan     0.2000   -0.0041
##    280        0.4054             nan     0.2000   -0.0002
##    300        0.3861             nan     0.2000   -0.0006
##    320        0.3607             nan     0.2000   -0.0044
##    340        0.3468             nan     0.2000   -0.0012
##    360        0.3324             nan     0.2000   -0.0006
##    380        0.3124             nan     0.2000   -0.0023
##    400        0.2953             nan     0.2000   -0.0013
##    420        0.2838             nan     0.2000   -0.0023
##    440        0.2691             nan     0.2000   -0.0015
##    460        0.2606             nan     0.2000   -0.0027
##    480        0.2513             nan     0.2000   -0.0021
##    500        0.2448             nan     0.2000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2000             nan     0.2000    0.0410
##      2        1.1422             nan     0.2000    0.0269
##      3        1.0919             nan     0.2000    0.0203
##      4        1.0477             nan     0.2000    0.0205
##      5        1.0213             nan     0.2000    0.0118
##      6        0.9945             nan     0.2000    0.0092
##      7        0.9746             nan     0.2000    0.0009
##      8        0.9529             nan     0.2000    0.0054
##      9        0.9389             nan     0.2000    0.0014
##     10        0.9246             nan     0.2000    0.0031
##     20        0.8326             nan     0.2000   -0.0010
##     40        0.7526             nan     0.2000   -0.0043
##     60        0.7024             nan     0.2000   -0.0011
##     80        0.6441             nan     0.2000   -0.0034
##    100        0.6097             nan     0.2000   -0.0023
##    120        0.5771             nan     0.2000   -0.0012
##    140        0.5443             nan     0.2000   -0.0037
##    160        0.5179             nan     0.2000   -0.0034
##    180        0.4920             nan     0.2000   -0.0033
##    200        0.4647             nan     0.2000   -0.0021
##    220        0.4426             nan     0.2000   -0.0027
##    240        0.4224             nan     0.2000   -0.0011
##    260        0.3989             nan     0.2000   -0.0019
##    280        0.3846             nan     0.2000   -0.0009
##    300        0.3667             nan     0.2000   -0.0000
##    320        0.3508             nan     0.2000   -0.0009
##    340        0.3269             nan     0.2000   -0.0020
##    360        0.3164             nan     0.2000   -0.0016
##    380        0.3020             nan     0.2000   -0.0001
##    400        0.2931             nan     0.2000   -0.0028
##    420        0.2822             nan     0.2000   -0.0021
##    440        0.2705             nan     0.2000   -0.0012
##    460        0.2613             nan     0.2000   -0.0012
##    480        0.2496             nan     0.2000   -0.0008
##    500        0.2399             nan     0.2000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2068             nan     0.2000    0.0387
##      2        1.1403             nan     0.2000    0.0311
##      3        1.0861             nan     0.2000    0.0203
##      4        1.0507             nan     0.2000    0.0116
##      5        1.0127             nan     0.2000    0.0132
##      6        0.9903             nan     0.2000    0.0076
##      7        0.9634             nan     0.2000    0.0061
##      8        0.9418             nan     0.2000    0.0048
##      9        0.9218             nan     0.2000    0.0053
##     10        0.9105             nan     0.2000    0.0019
##     20        0.8340             nan     0.2000   -0.0022
##     40        0.7633             nan     0.2000   -0.0043
##     60        0.7243             nan     0.2000   -0.0045
##     80        0.6804             nan     0.2000   -0.0011
##    100        0.6423             nan     0.2000   -0.0031
##    120        0.6032             nan     0.2000   -0.0001
##    140        0.5777             nan     0.2000   -0.0025
##    160        0.5469             nan     0.2000   -0.0007
##    180        0.5190             nan     0.2000   -0.0017
##    200        0.4940             nan     0.2000   -0.0014
##    220        0.4722             nan     0.2000   -0.0014
##    240        0.4491             nan     0.2000   -0.0044
##    260        0.4285             nan     0.2000   -0.0029
##    280        0.4096             nan     0.2000   -0.0026
##    300        0.3907             nan     0.2000   -0.0014
##    320        0.3747             nan     0.2000   -0.0012
##    340        0.3634             nan     0.2000   -0.0017
##    360        0.3513             nan     0.2000   -0.0013
##    380        0.3376             nan     0.2000   -0.0002
##    400        0.3233             nan     0.2000   -0.0012
##    420        0.3126             nan     0.2000   -0.0019
##    440        0.3001             nan     0.2000   -0.0019
##    460        0.2871             nan     0.2000   -0.0010
##    480        0.2735             nan     0.2000   -0.0009
##    500        0.2616             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1844             nan     0.2000    0.0484
##      2        1.1073             nan     0.2000    0.0296
##      3        1.0550             nan     0.2000    0.0221
##      4        1.0067             nan     0.2000    0.0206
##      5        0.9749             nan     0.2000    0.0049
##      6        0.9460             nan     0.2000    0.0105
##      7        0.9213             nan     0.2000    0.0051
##      8        0.9038             nan     0.2000    0.0027
##      9        0.8864             nan     0.2000    0.0021
##     10        0.8716             nan     0.2000    0.0009
##     20        0.7744             nan     0.2000   -0.0010
##     40        0.6769             nan     0.2000   -0.0003
##     60        0.6107             nan     0.2000   -0.0039
##     80        0.5489             nan     0.2000   -0.0032
##    100        0.4983             nan     0.2000   -0.0009
##    120        0.4536             nan     0.2000   -0.0031
##    140        0.4126             nan     0.2000    0.0000
##    160        0.3787             nan     0.2000   -0.0042
##    180        0.3489             nan     0.2000   -0.0032
##    200        0.3213             nan     0.2000   -0.0007
##    220        0.2958             nan     0.2000   -0.0007
##    240        0.2699             nan     0.2000   -0.0017
##    260        0.2526             nan     0.2000   -0.0016
##    280        0.2371             nan     0.2000   -0.0009
##    300        0.2240             nan     0.2000   -0.0024
##    320        0.2078             nan     0.2000   -0.0010
##    340        0.1950             nan     0.2000   -0.0008
##    360        0.1834             nan     0.2000   -0.0006
##    380        0.1731             nan     0.2000   -0.0008
##    400        0.1617             nan     0.2000   -0.0019
##    420        0.1534             nan     0.2000   -0.0006
##    440        0.1437             nan     0.2000   -0.0009
##    460        0.1369             nan     0.2000   -0.0001
##    480        0.1281             nan     0.2000   -0.0004
##    500        0.1216             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1952             nan     0.2000    0.0501
##      2        1.1144             nan     0.2000    0.0357
##      3        1.0645             nan     0.2000    0.0167
##      4        1.0197             nan     0.2000    0.0180
##      5        0.9862             nan     0.2000    0.0117
##      6        0.9611             nan     0.2000    0.0041
##      7        0.9349             nan     0.2000    0.0076
##      8        0.9139             nan     0.2000    0.0071
##      9        0.8945             nan     0.2000    0.0053
##     10        0.8822             nan     0.2000   -0.0015
##     20        0.7834             nan     0.2000   -0.0013
##     40        0.6824             nan     0.2000   -0.0033
##     60        0.6101             nan     0.2000   -0.0036
##     80        0.5501             nan     0.2000   -0.0016
##    100        0.5070             nan     0.2000   -0.0060
##    120        0.4755             nan     0.2000   -0.0022
##    140        0.4374             nan     0.2000   -0.0025
##    160        0.4015             nan     0.2000   -0.0013
##    180        0.3722             nan     0.2000   -0.0009
##    200        0.3426             nan     0.2000   -0.0019
##    220        0.3145             nan     0.2000   -0.0009
##    240        0.2935             nan     0.2000   -0.0023
##    260        0.2746             nan     0.2000   -0.0009
##    280        0.2539             nan     0.2000   -0.0008
##    300        0.2372             nan     0.2000   -0.0005
##    320        0.2215             nan     0.2000   -0.0011
##    340        0.2071             nan     0.2000   -0.0010
##    360        0.1937             nan     0.2000   -0.0012
##    380        0.1792             nan     0.2000   -0.0006
##    400        0.1696             nan     0.2000   -0.0011
##    420        0.1599             nan     0.2000   -0.0018
##    440        0.1494             nan     0.2000   -0.0009
##    460        0.1418             nan     0.2000   -0.0007
##    480        0.1329             nan     0.2000   -0.0006
##    500        0.1244             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1873             nan     0.2000    0.0490
##      2        1.1016             nan     0.2000    0.0430
##      3        1.0468             nan     0.2000    0.0188
##      4        1.0074             nan     0.2000    0.0175
##      5        0.9670             nan     0.2000    0.0156
##      6        0.9409             nan     0.2000    0.0093
##      7        0.9204             nan     0.2000    0.0008
##      8        0.9064             nan     0.2000    0.0023
##      9        0.8966             nan     0.2000   -0.0013
##     10        0.8772             nan     0.2000    0.0031
##     20        0.7797             nan     0.2000    0.0009
##     40        0.6723             nan     0.2000   -0.0033
##     60        0.6109             nan     0.2000   -0.0006
##     80        0.5510             nan     0.2000   -0.0041
##    100        0.5007             nan     0.2000   -0.0015
##    120        0.4580             nan     0.2000   -0.0018
##    140        0.4223             nan     0.2000   -0.0009
##    160        0.3878             nan     0.2000   -0.0023
##    180        0.3553             nan     0.2000   -0.0020
##    200        0.3299             nan     0.2000   -0.0017
##    220        0.2982             nan     0.2000   -0.0021
##    240        0.2771             nan     0.2000   -0.0009
##    260        0.2538             nan     0.2000   -0.0009
##    280        0.2377             nan     0.2000   -0.0021
##    300        0.2184             nan     0.2000   -0.0013
##    320        0.2042             nan     0.2000   -0.0010
##    340        0.1927             nan     0.2000   -0.0014
##    360        0.1787             nan     0.2000   -0.0007
##    380        0.1684             nan     0.2000   -0.0011
##    400        0.1588             nan     0.2000   -0.0005
##    420        0.1514             nan     0.2000   -0.0008
##    440        0.1442             nan     0.2000   -0.0005
##    460        0.1362             nan     0.2000   -0.0006
##    480        0.1282             nan     0.2000   -0.0003
##    500        0.1209             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1999             nan     0.3000    0.0451
##      2        1.1333             nan     0.3000    0.0231
##      3        1.1014             nan     0.3000    0.0112
##      4        1.0626             nan     0.3000    0.0190
##      5        1.0299             nan     0.3000    0.0106
##      6        1.0061             nan     0.3000    0.0087
##      7        0.9836             nan     0.3000    0.0084
##      8        0.9671             nan     0.3000    0.0044
##      9        0.9483             nan     0.3000    0.0056
##     10        0.9391             nan     0.3000    0.0021
##     20        0.8645             nan     0.3000   -0.0014
##     40        0.8034             nan     0.3000   -0.0024
##     60        0.7740             nan     0.3000   -0.0047
##     80        0.7436             nan     0.3000   -0.0016
##    100        0.7226             nan     0.3000   -0.0035
##    120        0.7056             nan     0.3000   -0.0017
##    140        0.6962             nan     0.3000   -0.0043
##    160        0.6925             nan     0.3000   -0.0011
##    180        0.6759             nan     0.3000   -0.0031
##    200        0.6607             nan     0.3000   -0.0036
##    220        0.6486             nan     0.3000   -0.0017
##    240        0.6343             nan     0.3000   -0.0027
##    260        0.6277             nan     0.3000   -0.0067
##    280        0.6174             nan     0.3000   -0.0051
##    300        0.6125             nan     0.3000   -0.0006
##    320        0.6041             nan     0.3000   -0.0047
##    340        0.5937             nan     0.3000   -0.0008
##    360        0.5818             nan     0.3000   -0.0029
##    380        0.5763             nan     0.3000   -0.0012
##    400        0.5716             nan     0.3000   -0.0014
##    420        0.5651             nan     0.3000   -0.0027
##    440        0.5594             nan     0.3000   -0.0042
##    460        0.5537             nan     0.3000   -0.0051
##    480        0.5399             nan     0.3000   -0.0017
##    500        0.5334             nan     0.3000   -0.0038
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1955             nan     0.3000    0.0448
##      2        1.1322             nan     0.3000    0.0321
##      3        1.0918             nan     0.3000    0.0166
##      4        1.0603             nan     0.3000    0.0127
##      5        1.0232             nan     0.3000    0.0173
##      6        1.0006             nan     0.3000    0.0041
##      7        0.9855             nan     0.3000    0.0018
##      8        0.9578             nan     0.3000    0.0126
##      9        0.9470             nan     0.3000    0.0021
##     10        0.9351             nan     0.3000    0.0019
##     20        0.8535             nan     0.3000   -0.0007
##     40        0.8010             nan     0.3000   -0.0046
##     60        0.7737             nan     0.3000   -0.0019
##     80        0.7488             nan     0.3000   -0.0021
##    100        0.7187             nan     0.3000   -0.0023
##    120        0.7044             nan     0.3000    0.0006
##    140        0.6855             nan     0.3000   -0.0041
##    160        0.6667             nan     0.3000   -0.0070
##    180        0.6574             nan     0.3000   -0.0018
##    200        0.6419             nan     0.3000   -0.0006
##    220        0.6313             nan     0.3000   -0.0026
##    240        0.6217             nan     0.3000   -0.0004
##    260        0.6119             nan     0.3000   -0.0033
##    280        0.6070             nan     0.3000   -0.0038
##    300        0.6025             nan     0.3000   -0.0037
##    320        0.5937             nan     0.3000   -0.0034
##    340        0.5880             nan     0.3000   -0.0015
##    360        0.5821             nan     0.3000   -0.0022
##    380        0.5724             nan     0.3000   -0.0024
##    400        0.5649             nan     0.3000   -0.0025
##    420        0.5545             nan     0.3000   -0.0012
##    440        0.5484             nan     0.3000   -0.0021
##    460        0.5403             nan     0.3000   -0.0026
##    480        0.5366             nan     0.3000   -0.0030
##    500        0.5310             nan     0.3000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1912             nan     0.3000    0.0448
##      2        1.1169             nan     0.3000    0.0288
##      3        1.0743             nan     0.3000    0.0180
##      4        1.0419             nan     0.3000    0.0132
##      5        1.0087             nan     0.3000    0.0086
##      6        0.9870             nan     0.3000    0.0076
##      7        0.9728             nan     0.3000    0.0038
##      8        0.9551             nan     0.3000    0.0072
##      9        0.9349             nan     0.3000    0.0083
##     10        0.9245             nan     0.3000    0.0002
##     20        0.8711             nan     0.3000   -0.0023
##     40        0.8125             nan     0.3000   -0.0013
##     60        0.7813             nan     0.3000   -0.0068
##     80        0.7623             nan     0.3000   -0.0016
##    100        0.7482             nan     0.3000   -0.0023
##    120        0.7227             nan     0.3000   -0.0014
##    140        0.7102             nan     0.3000   -0.0064
##    160        0.6950             nan     0.3000   -0.0050
##    180        0.6753             nan     0.3000   -0.0024
##    200        0.6617             nan     0.3000   -0.0017
##    220        0.6407             nan     0.3000   -0.0028
##    240        0.6334             nan     0.3000   -0.0032
##    260        0.6232             nan     0.3000   -0.0013
##    280        0.6202             nan     0.3000   -0.0019
##    300        0.6061             nan     0.3000   -0.0021
##    320        0.5965             nan     0.3000   -0.0037
##    340        0.5881             nan     0.3000   -0.0030
##    360        0.5791             nan     0.3000   -0.0035
##    380        0.5691             nan     0.3000   -0.0024
##    400        0.5590             nan     0.3000   -0.0002
##    420        0.5556             nan     0.3000   -0.0033
##    440        0.5504             nan     0.3000   -0.0023
##    460        0.5424             nan     0.3000   -0.0036
##    480        0.5387             nan     0.3000   -0.0019
##    500        0.5337             nan     0.3000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1612             nan     0.3000    0.0509
##      2        1.0874             nan     0.3000    0.0293
##      3        1.0309             nan     0.3000    0.0169
##      4        0.9827             nan     0.3000    0.0114
##      5        0.9648             nan     0.3000    0.0004
##      6        0.9359             nan     0.3000    0.0083
##      7        0.9178             nan     0.3000    0.0029
##      8        0.9015             nan     0.3000    0.0001
##      9        0.8874             nan     0.3000   -0.0022
##     10        0.8749             nan     0.3000   -0.0071
##     20        0.7988             nan     0.3000    0.0022
##     40        0.7233             nan     0.3000   -0.0045
##     60        0.6543             nan     0.3000   -0.0040
##     80        0.5880             nan     0.3000    0.0011
##    100        0.5523             nan     0.3000    0.0004
##    120        0.5051             nan     0.3000   -0.0053
##    140        0.4742             nan     0.3000   -0.0043
##    160        0.4347             nan     0.3000   -0.0050
##    180        0.4013             nan     0.3000   -0.0021
##    200        0.3780             nan     0.3000   -0.0020
##    220        0.3530             nan     0.3000   -0.0024
##    240        0.3305             nan     0.3000   -0.0018
##    260        0.3141             nan     0.3000   -0.0037
##    280        0.3009             nan     0.3000   -0.0032
##    300        0.2813             nan     0.3000   -0.0035
##    320        0.2648             nan     0.3000   -0.0004
##    340        0.2500             nan     0.3000   -0.0028
##    360        0.2306             nan     0.3000   -0.0008
##    380        0.2178             nan     0.3000   -0.0026
##    400        0.2098             nan     0.3000   -0.0010
##    420        0.1973             nan     0.3000   -0.0018
##    440        0.1863             nan     0.3000   -0.0012
##    460        0.1739             nan     0.3000   -0.0013
##    480        0.1642             nan     0.3000   -0.0009
##    500        0.1594             nan     0.3000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1960             nan     0.3000    0.0401
##      2        1.1031             nan     0.3000    0.0409
##      3        1.0512             nan     0.3000    0.0186
##      4        1.0014             nan     0.3000    0.0202
##      5        0.9630             nan     0.3000    0.0083
##      6        0.9343             nan     0.3000    0.0060
##      7        0.9147             nan     0.3000    0.0021
##      8        0.8953             nan     0.3000    0.0011
##      9        0.8828             nan     0.3000   -0.0011
##     10        0.8718             nan     0.3000   -0.0009
##     20        0.7911             nan     0.3000   -0.0024
##     40        0.7140             nan     0.3000   -0.0021
##     60        0.6408             nan     0.3000   -0.0014
##     80        0.5924             nan     0.3000   -0.0038
##    100        0.5359             nan     0.3000   -0.0003
##    120        0.5017             nan     0.3000   -0.0048
##    140        0.4671             nan     0.3000   -0.0044
##    160        0.4311             nan     0.3000   -0.0040
##    180        0.4036             nan     0.3000   -0.0007
##    200        0.3728             nan     0.3000   -0.0017
##    220        0.3543             nan     0.3000   -0.0013
##    240        0.3338             nan     0.3000   -0.0036
##    260        0.3137             nan     0.3000   -0.0003
##    280        0.2981             nan     0.3000   -0.0027
##    300        0.2792             nan     0.3000   -0.0010
##    320        0.2622             nan     0.3000   -0.0029
##    340        0.2467             nan     0.3000   -0.0014
##    360        0.2330             nan     0.3000   -0.0006
##    380        0.2171             nan     0.3000   -0.0018
##    400        0.2075             nan     0.3000   -0.0019
##    420        0.1966             nan     0.3000   -0.0016
##    440        0.1892             nan     0.3000   -0.0012
##    460        0.1801             nan     0.3000   -0.0012
##    480        0.1695             nan     0.3000   -0.0012
##    500        0.1611             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1630             nan     0.3000    0.0578
##      2        1.0945             nan     0.3000    0.0194
##      3        1.0276             nan     0.3000    0.0335
##      4        0.9873             nan     0.3000    0.0142
##      5        0.9590             nan     0.3000    0.0069
##      6        0.9323             nan     0.3000    0.0090
##      7        0.9129             nan     0.3000    0.0057
##      8        0.8900             nan     0.3000    0.0071
##      9        0.8780             nan     0.3000   -0.0009
##     10        0.8650             nan     0.3000    0.0006
##     20        0.8029             nan     0.3000   -0.0019
##     40        0.7089             nan     0.3000   -0.0048
##     60        0.6497             nan     0.3000   -0.0041
##     80        0.5993             nan     0.3000   -0.0023
##    100        0.5562             nan     0.3000   -0.0021
##    120        0.5136             nan     0.3000   -0.0029
##    140        0.4841             nan     0.3000   -0.0028
##    160        0.4451             nan     0.3000   -0.0017
##    180        0.4203             nan     0.3000   -0.0024
##    200        0.3900             nan     0.3000   -0.0026
##    220        0.3630             nan     0.3000   -0.0050
##    240        0.3375             nan     0.3000   -0.0017
##    260        0.3228             nan     0.3000   -0.0032
##    280        0.3088             nan     0.3000   -0.0033
##    300        0.2928             nan     0.3000   -0.0022
##    320        0.2757             nan     0.3000   -0.0019
##    340        0.2611             nan     0.3000   -0.0018
##    360        0.2520             nan     0.3000   -0.0009
##    380        0.2355             nan     0.3000   -0.0011
##    400        0.2195             nan     0.3000   -0.0011
##    420        0.2081             nan     0.3000   -0.0021
##    440        0.2007             nan     0.3000   -0.0031
##    460        0.1937             nan     0.3000   -0.0008
##    480        0.1863             nan     0.3000   -0.0027
##    500        0.1759             nan     0.3000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1362             nan     0.3000    0.0683
##      2        1.0572             nan     0.3000    0.0293
##      3        1.0029             nan     0.3000    0.0228
##      4        0.9657             nan     0.3000    0.0058
##      5        0.9326             nan     0.3000    0.0071
##      6        0.9035             nan     0.3000    0.0047
##      7        0.8794             nan     0.3000    0.0006
##      8        0.8651             nan     0.3000   -0.0008
##      9        0.8498             nan     0.3000   -0.0028
##     10        0.8291             nan     0.3000    0.0029
##     20        0.7420             nan     0.3000   -0.0005
##     40        0.6418             nan     0.3000   -0.0020
##     60        0.5847             nan     0.3000   -0.0036
##     80           inf             nan     0.3000       nan
##    100           inf             nan     0.3000       nan
##    120           inf             nan     0.3000       nan
##    140           inf             nan     0.3000       nan
##    160           inf             nan     0.3000   -0.0012
##    180           inf             nan     0.3000       nan
##    200           inf             nan     0.3000       nan
##    220           inf             nan     0.3000   -0.0010
##    240           inf             nan     0.3000   -0.0007
##    260           inf             nan     0.3000   -0.0012
##    280           inf             nan     0.3000   -0.0005
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000   -0.0003
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000   -0.0002
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000   -0.0004
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000   -0.0003
##    500           inf             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1487             nan     0.3000    0.0626
##      2        1.0614             nan     0.3000    0.0384
##      3        1.0079             nan     0.3000    0.0159
##      4        0.9499             nan     0.3000    0.0262
##      5        0.9183             nan     0.3000    0.0057
##      6        0.8823             nan     0.3000    0.0116
##      7        0.8564             nan     0.3000    0.0052
##      8        0.8459             nan     0.3000   -0.0042
##      9        0.8286             nan     0.3000    0.0004
##     10        0.8165             nan     0.3000   -0.0023
##     20        0.7389             nan     0.3000   -0.0042
##     40        0.6231             nan     0.3000    0.0010
##     60        0.5371             nan     0.3000   -0.0061
##     80        0.4647             nan     0.3000   -0.0033
##    100        0.4077             nan     0.3000   -0.0040
##    120        0.3523             nan     0.3000   -0.0029
##    140        0.3169             nan     0.3000   -0.0051
##    160        0.2807             nan     0.3000   -0.0028
##    180        0.2518             nan     0.3000   -0.0033
##    200        0.2214             nan     0.3000   -0.0011
##    220        0.1977             nan     0.3000   -0.0024
##    240        0.1770             nan     0.3000    0.0003
##    260        0.1621             nan     0.3000   -0.0002
##    280        0.1476             nan     0.3000   -0.0012
##    300        0.1371             nan     0.3000   -0.0013
##    320        0.1242             nan     0.3000   -0.0015
##    340        0.1133             nan     0.3000   -0.0007
##    360        0.1046             nan     0.3000   -0.0009
##    380        0.0933             nan     0.3000    0.0000
##    400        0.0863             nan     0.3000   -0.0009
##    420        0.0806             nan     0.3000   -0.0010
##    440        0.0742             nan     0.3000   -0.0010
##    460        0.0670             nan     0.3000   -0.0002
##    480        0.0619             nan     0.3000   -0.0006
##    500        0.0567             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1462             nan     0.3000    0.0677
##      2        1.0644             nan     0.3000    0.0326
##      3        1.0106             nan     0.3000    0.0103
##      4        0.9629             nan     0.3000    0.0061
##      5        0.9320             nan     0.3000    0.0042
##      6        0.9019             nan     0.3000    0.0027
##      7        0.8749             nan     0.3000    0.0044
##      8        0.8541             nan     0.3000   -0.0034
##      9        0.8345             nan     0.3000   -0.0012
##     10        0.8245             nan     0.3000   -0.0030
##     20        0.7302             nan     0.3000   -0.0058
##     40        0.6255             nan     0.3000   -0.0085
##     60        0.5372             nan     0.3000   -0.0065
##     80        0.4601             nan     0.3000   -0.0080
##    100        0.4068             nan     0.3000   -0.0050
##    120        0.3663             nan     0.3000   -0.0025
##    140        0.3216             nan     0.3000   -0.0033
##    160        0.2862             nan     0.3000   -0.0024
##    180        0.2572             nan     0.3000   -0.0012
##    200        0.2305             nan     0.3000   -0.0014
##    220        0.2076             nan     0.3000   -0.0009
##    240        0.1907             nan     0.3000   -0.0009
##    260        0.1731             nan     0.3000   -0.0017
##    280        0.1550             nan     0.3000   -0.0009
##    300        0.1423             nan     0.3000   -0.0018
##    320        0.1295             nan     0.3000   -0.0011
##    340        0.1179             nan     0.3000   -0.0014
##    360        0.1061             nan     0.3000   -0.0009
##    380        0.0961             nan     0.3000   -0.0005
##    400        0.0884             nan     0.3000   -0.0010
##    420        0.0821             nan     0.3000   -0.0010
##    440        0.0752             nan     0.3000   -0.0003
##    460        0.0694             nan     0.3000   -0.0004
##    480        0.0646             nan     0.3000   -0.0006
##    500        0.0588             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1571             nan     0.5000    0.0587
##      2        1.0963             nan     0.5000    0.0265
##      3        1.0360             nan     0.5000    0.0213
##      4        0.9997             nan     0.5000    0.0133
##      5        0.9651             nan     0.5000    0.0127
##      6        0.9445             nan     0.5000    0.0020
##      7        0.9343             nan     0.5000   -0.0015
##      8        0.9159             nan     0.5000    0.0031
##      9        0.8996             nan     0.5000    0.0055
##     10        0.8965             nan     0.5000   -0.0102
##     20        0.8395             nan     0.5000   -0.0027
##     40        0.7920             nan     0.5000   -0.0055
##     60        0.7492             nan     0.5000   -0.0070
##     80        0.7233             nan     0.5000   -0.0124
##    100        0.7035             nan     0.5000   -0.0047
##    120        0.6614             nan     0.5000   -0.0043
##    140        0.6547             nan     0.5000   -0.0066
##    160        0.6336             nan     0.5000   -0.0010
##    180        0.6120             nan     0.5000   -0.0047
##    200        0.5935             nan     0.5000   -0.0047
##    220        0.5912             nan     0.5000   -0.0065
##    240        0.5767             nan     0.5000   -0.0086
##    260        0.5702             nan     0.5000   -0.0080
##    280        0.5580             nan     0.5000   -0.0044
##    300        0.5416             nan     0.5000   -0.0026
##    320        0.5354             nan     0.5000   -0.0063
##    340        0.5264             nan     0.5000   -0.0027
##    360        0.5099             nan     0.5000   -0.0064
##    380        0.5020             nan     0.5000   -0.0018
##    400        0.4872             nan     0.5000   -0.0056
##    420        0.4760             nan     0.5000   -0.0002
##    440        0.4740             nan     0.5000   -0.0081
##    460        0.4706             nan     0.5000   -0.0097
##    480        0.4592             nan     0.5000   -0.0020
##    500        0.4539             nan     0.5000   -0.0035
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1472             nan     0.5000    0.0707
##      2        1.0811             nan     0.5000    0.0250
##      3        1.0412             nan     0.5000    0.0092
##      4        0.9937             nan     0.5000    0.0218
##      5        0.9656             nan     0.5000    0.0131
##      6        0.9530             nan     0.5000    0.0014
##      7        0.9237             nan     0.5000    0.0116
##      8        0.9137             nan     0.5000   -0.0013
##      9        0.8901             nan     0.5000    0.0049
##     10        0.8795             nan     0.5000   -0.0016
##     20        0.8274             nan     0.5000   -0.0071
##     40        0.7884             nan     0.5000   -0.0105
##     60        0.7549             nan     0.5000   -0.0047
##     80        0.7236             nan     0.5000   -0.0023
##    100        0.6889             nan     0.5000    0.0009
##    120        0.6611             nan     0.5000   -0.0007
##    140        0.6373             nan     0.5000   -0.0056
##    160        0.6192             nan     0.5000   -0.0004
##    180        0.6008             nan     0.5000   -0.0048
##    200        0.5891             nan     0.5000   -0.0028
##    220        0.5850             nan     0.5000   -0.0017
##    240        0.5664             nan     0.5000   -0.0032
##    260        0.5565             nan     0.5000   -0.0056
##    280        0.5397             nan     0.5000   -0.0009
##    300        0.5310             nan     0.5000   -0.0064
##    320        0.5248             nan     0.5000   -0.0086
##    340        0.5127             nan     0.5000   -0.0033
##    360        0.5088             nan     0.5000   -0.0038
##    380        0.4918             nan     0.5000   -0.0042
##    400        0.4880             nan     0.5000   -0.0025
##    420        0.4821             nan     0.5000   -0.0041
##    440        0.4690             nan     0.5000   -0.0032
##    460        0.4635             nan     0.5000   -0.0034
##    480        0.4550             nan     0.5000   -0.0026
##    500        0.4488             nan     0.5000   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1498             nan     0.5000    0.0706
##      2        1.0935             nan     0.5000    0.0260
##      3        1.0457             nan     0.5000    0.0140
##      4        0.9898             nan     0.5000    0.0226
##      5        0.9635             nan     0.5000    0.0048
##      6        0.9534             nan     0.5000   -0.0019
##      7        0.9408             nan     0.5000   -0.0008
##      8        0.9310             nan     0.5000   -0.0035
##      9        0.9157             nan     0.5000    0.0011
##     10        0.8956             nan     0.5000    0.0022
##     20        0.8464             nan     0.5000   -0.0058
##     40        0.7839             nan     0.5000   -0.0037
##     60        0.7675             nan     0.5000   -0.0120
##     80        0.7425             nan     0.5000   -0.0148
##    100        0.7098             nan     0.5000   -0.0097
##    120        0.6840             nan     0.5000   -0.0014
##    140        0.6730             nan     0.5000   -0.0081
##    160        0.6447             nan     0.5000   -0.0022
##    180        0.6263             nan     0.5000   -0.0009
##    200        0.6069             nan     0.5000   -0.0055
##    220        0.5902             nan     0.5000   -0.0045
##    240        0.5834             nan     0.5000   -0.0032
##    260        0.5819             nan     0.5000   -0.0067
##    280        0.5649             nan     0.5000   -0.0063
##    300        0.5486             nan     0.5000   -0.0083
##    320        0.5327             nan     0.5000   -0.0049
##    340        0.5203             nan     0.5000   -0.0010
##    360        0.5098             nan     0.5000   -0.0023
##    380        0.5077             nan     0.5000   -0.0023
##    400        0.4974             nan     0.5000   -0.0096
##    420        0.4955             nan     0.5000   -0.0118
##    440        0.4769             nan     0.5000   -0.0103
##    460        0.4785             nan     0.5000   -0.0057
##    480        0.4675             nan     0.5000   -0.0015
##    500        0.4647             nan     0.5000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1131             nan     0.5000    0.0635
##      2        1.0313             nan     0.5000    0.0268
##      3        0.9718             nan     0.5000    0.0127
##      4        0.9176             nan     0.5000    0.0159
##      5        0.9070             nan     0.5000   -0.0072
##      6        0.8897             nan     0.5000   -0.0062
##      7        0.8737             nan     0.5000   -0.0047
##      8        0.8624             nan     0.5000   -0.0045
##      9        0.8445             nan     0.5000    0.0037
##     10        0.8312             nan     0.5000   -0.0013
##     20        0.7654             nan     0.5000   -0.0105
##     40        0.6675             nan     0.5000   -0.0112
##     60        0.5706             nan     0.5000   -0.0017
##     80        0.5204             nan     0.5000   -0.0102
##    100        0.4792             nan     0.5000    0.0010
##    120        0.4480             nan     0.5000   -0.0138
##    140        0.8232             nan     0.5000   -0.0065
##    160        0.8040             nan     0.5000   -0.0016
##    180        0.7836             nan     0.5000    0.0018
##    200        0.7721             nan     0.5000   -0.0019
##    220        0.7683             nan     0.5000   -0.0007
##    240        0.7577             nan     0.5000   -0.0044
##    260        0.7429             nan     0.5000   -0.0076
##    280        0.7315             nan     0.5000   -0.0005
##    300        0.9335             nan     0.5000   -0.0010
##    320        0.9192             nan     0.5000   -0.0073
##    340        0.9069             nan     0.5000   -0.0047
##    360        0.9023             nan     0.5000   -0.0057
##    380        0.9039             nan     0.5000   -0.0031
##    400        0.8961             nan     0.5000   -0.0068
##    420        0.8741             nan     0.5000   -0.0010
##    440        0.8966             nan     0.5000   -0.0003
##    460        0.8662             nan     0.5000   -0.0030
##    480        0.8665             nan     0.5000   -0.0024
##    500        0.8536             nan     0.5000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1051             nan     0.5000    0.0783
##      2        1.0079             nan     0.5000    0.0442
##      3        0.9442             nan     0.5000    0.0127
##      4        0.9114             nan     0.5000    0.0076
##      5        0.8681             nan     0.5000    0.0067
##      6        0.8583             nan     0.5000   -0.0108
##      7        0.8369             nan     0.5000    0.0005
##      8        0.8301             nan     0.5000   -0.0045
##      9        0.8222             nan     0.5000   -0.0072
##     10        0.8257             nan     0.5000   -0.0224
##     20        0.7441             nan     0.5000   -0.0070
##     40        0.6444             nan     0.5000   -0.0123
##     60        0.5753             nan     0.5000   -0.0078
##     80        0.5084             nan     0.5000   -0.0076
##    100        0.4597             nan     0.5000   -0.0061
##    120        0.4150             nan     0.5000    0.0009
##    140        0.3872             nan     0.5000   -0.0085
##    160        0.3570             nan     0.5000   -0.0021
##    180        0.3170             nan     0.5000   -0.0044
##    200        0.3004             nan     0.5000   -0.0017
##    220        0.2764             nan     0.5000   -0.0058
##    240        0.2756             nan     0.5000   -0.0003
##    260        0.2134             nan     0.5000   -0.0035
##    280        0.2047             nan     0.5000   -0.0040
##    300        0.1888             nan     0.5000   -0.0042
##    320        0.1730             nan     0.5000   -0.0039
##    340        0.1553             nan     0.5000   -0.0014
##    360        0.1432             nan     0.5000   -0.0007
##    380        0.1297             nan     0.5000   -0.0022
##    400        0.1167             nan     0.5000   -0.0009
##    420        0.1087             nan     0.5000   -0.0016
##    440        0.0978             nan     0.5000   -0.0004
##    460        0.0891             nan     0.5000   -0.0003
##    480        0.0851             nan     0.5000   -0.0035
##    500        0.0776             nan     0.5000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1104             nan     0.5000    0.0927
##      2        0.9943             nan     0.5000    0.0523
##      3        0.9455             nan     0.5000    0.0126
##      4        0.9120             nan     0.5000    0.0048
##      5        0.8725             nan     0.5000    0.0154
##      6        0.8615             nan     0.5000   -0.0061
##      7        0.8384             nan     0.5000   -0.0029
##      8        0.8270             nan     0.5000   -0.0040
##      9        0.8188             nan     0.5000   -0.0054
##     10        0.8098             nan     0.5000   -0.0074
##     20        0.7256             nan     0.5000   -0.0016
##     40        0.6514             nan     0.5000   -0.0068
##     60        0.5931             nan     0.5000   -0.0106
##     80        0.5179             nan     0.5000   -0.0055
##    100        0.4668             nan     0.5000   -0.0156
##    120        0.4269             nan     0.5000   -0.0053
##    140        0.3882             nan     0.5000   -0.0027
##    160        0.3583             nan     0.5000   -0.0053
##    180        0.3238             nan     0.5000   -0.0012
##    200        0.2957             nan     0.5000   -0.0101
##    220        0.2626             nan     0.5000   -0.0031
##    240        0.2237             nan     0.5000   -0.0014
##    260        0.2059             nan     0.5000   -0.0035
##    280        0.1908             nan     0.5000   -0.0012
##    300        0.1719             nan     0.5000   -0.0016
##    320        0.1606             nan     0.5000   -0.0007
##    340        0.1469             nan     0.5000   -0.0030
##    360        0.1345             nan     0.5000   -0.0023
##    380        0.1226             nan     0.5000   -0.0004
##    400        0.1113             nan     0.5000   -0.0002
##    420        0.1048             nan     0.5000   -0.0020
##    440        0.0978             nan     0.5000   -0.0022
##    460        0.0907             nan     0.5000   -0.0019
##    480        0.0831             nan     0.5000   -0.0007
##    500        0.0768             nan     0.5000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1080             nan     0.5000    0.0810
##      2        0.9793             nan     0.5000    0.0500
##      3        0.9336             nan     0.5000    0.0120
##      4        0.9073             nan     0.5000   -0.0009
##      5        0.8733             nan     0.5000    0.0027
##      6        0.8540             nan     0.5000   -0.0085
##      7        0.8398             nan     0.5000   -0.0124
##      8        0.8216             nan     0.5000   -0.0061
##      9        0.8002             nan     0.5000   -0.0013
##     10        0.7782             nan     0.5000   -0.0015
##     20        0.6881             nan     0.5000   -0.0038
##     40        0.5412             nan     0.5000   -0.0017
##     60        0.4464             nan     0.5000   -0.0058
##     80       54.9539             nan     0.5000   -0.0060
##    100       54.4749             nan     0.5000   -0.0283
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1042             nan     0.5000    0.0758
##      2        1.0047             nan     0.5000    0.0350
##      3        0.9314             nan     0.5000    0.0301
##      4        0.9069             nan     0.5000   -0.0061
##      5        0.8869             nan     0.5000   -0.0023
##      6        0.8692             nan     0.5000   -0.0171
##      7        0.8497             nan     0.5000   -0.0065
##      8        0.8350             nan     0.5000   -0.0168
##      9        0.8112             nan     0.5000    0.0066
##     10        0.8024             nan     0.5000   -0.0152
##     20        0.6933             nan     0.5000   -0.0093
##     40        0.6224             nan     0.5000   -0.0513
##     60        0.4877             nan     0.5000   -0.0031
##     80        0.3552             nan     0.5000   -0.0053
##    100        0.2832             nan     0.5000   -0.0037
##    120        0.2244             nan     0.5000   -0.0031
##    140        0.1852             nan     0.5000   -0.0019
##    160        0.1476             nan     0.5000   -0.0021
##    180        0.1225             nan     0.5000   -0.0009
##    200        0.1062             nan     0.5000   -0.0036
##    220        0.0916             nan     0.5000   -0.0006
##    240        0.0770             nan     0.5000   -0.0013
##    260        0.0655             nan     0.5000   -0.0007
##    280        0.0571             nan     0.5000   -0.0009
##    300        0.0489             nan     0.5000   -0.0002
##    320        0.0415             nan     0.5000   -0.0005
##    340        0.0372             nan     0.5000   -0.0005
##    360        0.0313             nan     0.5000   -0.0007
##    380        0.0285             nan     0.5000   -0.0011
##    400        0.0265             nan     0.5000   -0.0004
##    420        0.0228             nan     0.5000   -0.0003
##    440        0.0199             nan     0.5000   -0.0001
##    460        0.0177             nan     0.5000   -0.0002
##    480        0.0162             nan     0.5000   -0.0003
##    500        0.0147             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0841             nan     0.5000    0.0935
##      2        0.9820             nan     0.5000    0.0322
##      3        0.9343             nan     0.5000    0.0058
##      4        0.8762             nan     0.5000    0.0160
##      5        0.8500             nan     0.5000   -0.0042
##      6        0.8284             nan     0.5000   -0.0023
##      7        0.8143             nan     0.5000   -0.0065
##      8        0.8059             nan     0.5000   -0.0144
##      9        0.8028             nan     0.5000   -0.0240
##     10        0.7943             nan     0.5000   -0.0086
##     20        0.7106             nan     0.5000   -0.0020
##     40        0.5763             nan     0.5000   -0.0196
##     60        0.4514             nan     0.5000   -0.0098
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1208             nan     1.0000    0.0704
##      2        1.0504             nan     1.0000    0.0135
##      3        0.9880             nan     1.0000    0.0062
##      4        0.9716             nan     1.0000   -0.0011
##      5        0.9603             nan     1.0000   -0.0101
##      6        0.9494             nan     1.0000   -0.0157
##      7        0.9292             nan     1.0000    0.0019
##      8        0.9209             nan     1.0000   -0.0079
##      9        0.8966             nan     1.0000    0.0061
##     10        0.8980             nan     1.0000   -0.0133
##     20        0.9293             nan     1.0000   -0.0586
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1280             nan     1.0000    0.0366
##      2        1.0392             nan     1.0000    0.0377
##      3        1.0049             nan     1.0000   -0.0145
##      4        0.9814             nan     1.0000   -0.0044
##      5        0.9690             nan     1.0000   -0.0020
##      6        0.9581             nan     1.0000   -0.0156
##      7        0.9467             nan     1.0000   -0.0124
##      8        0.9352             nan     1.0000   -0.0084
##      9        0.9279             nan     1.0000   -0.0058
##     10        0.9258             nan     1.0000   -0.0135
##     20        1.0397             nan     1.0000   -0.0151
##     40 23256371.2857             nan     1.0000    0.0002
##     60 23256371.2557             nan     1.0000   -0.0146
##     80 23256371.2597             nan     1.0000   -0.0008
##    100 23256371.2482             nan     1.0000   -0.0019
##    120 23256371.2573             nan     1.0000   -0.0359
##    140 23256371.2422             nan     1.0000   -0.0018
##    160 23256371.2278             nan     1.0000    0.0053
##    180 23256371.2150             nan     1.0000   -0.0004
##    200 28769748.4736             nan     1.0000   -0.0165
##    220 28769748.3704             nan     1.0000    0.0001
##    240 28769748.3731             nan     1.0000   -0.0003
##    260 28769748.3678             nan     1.0000   -0.0005
##    280 28769748.3430             nan     1.0000   -0.0094
##    300 28769748.3365             nan     1.0000    0.0011
##    320 28769748.6136             nan     1.0000   -0.0038
##    340 874024405801.1039             nan     1.0000   -0.0022
##    360 874024405801.1002             nan     1.0000   -0.0017
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440 874024413384.2057             nan     1.0000       inf
##    460 874024413383.8336             nan     1.0000   -0.0001
##    480 874024413383.4625             nan     1.0000    0.0231
##    500 4732586512793954.0000             nan     1.0000 -2147670342532169.5000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1151             nan     1.0000    0.0893
##      2        1.0254             nan     1.0000    0.0430
##      3        0.9618             nan     1.0000    0.0166
##      4        0.9160             nan     1.0000    0.0202
##      5        0.9013             nan     1.0000   -0.0117
##      6        0.8961             nan     1.0000   -0.0098
##      7        0.9051             nan     1.0000   -0.0250
##      8        0.8823             nan     1.0000    0.0092
##      9        0.8822             nan     1.0000   -0.0107
##     10        0.8602             nan     1.0000    0.0072
##     20        0.8209             nan     1.0000   -0.0013
##     40   952062.3927             nan     1.0000   -0.0025
##     60   952062.3793             nan     1.0000   -0.0165
##     80   952077.4157             nan     1.0000   -0.0022
##    100   952346.0480             nan     1.0000    0.0066
##    120   952345.9997             nan     1.0000    0.0024
##    140   952347.6261             nan     1.0000   -0.0033
##    160   952347.6136             nan     1.0000   -0.0006
##    180   952347.5544             nan     1.0000    0.0094
##    200   952347.5223             nan     1.0000   -0.0043
##    220   952347.4048             nan     1.0000    0.0014
##    240   952347.3206             nan     1.0000    0.0032
##    260   952347.2438             nan     1.0000    0.0039
##    280   952347.2009             nan     1.0000    0.0018
##    300   952347.1606             nan     1.0000   -0.0034
##    320   952347.0706             nan     1.0000    0.0025
##    340   952347.0192             nan     1.0000    0.0015
##    360   952346.9572             nan     1.0000    0.0012
##    380   952346.8309             nan     1.0000    0.0025
##    400   952346.7729             nan     1.0000    0.0007
##    420 312840496051886.6875             nan     1.0000    0.0030
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0847             nan     1.0000    0.0661
##      2        1.0126             nan     1.0000   -0.0412
##      3        0.9179             nan     1.0000    0.0169
##      4        0.8921             nan     1.0000   -0.0028
##      5        1.0599             nan     1.0000   -0.1921
##      6        1.0395             nan     1.0000   -0.0079
##      7        1.0608             nan     1.0000   -0.0450
##      8        1.0376             nan     1.0000   -0.0116
##      9        1.0237             nan     1.0000   -0.0062
##     10        1.0172             nan     1.0000   -0.0067
##     20        0.9849             nan     1.0000   -0.0152
##     40        0.9716             nan     1.0000   -0.0085
##     60           inf             nan     1.0000      -inf
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0517             nan     1.0000    0.0732
##      2        0.9675             nan     1.0000    0.0363
##      3        0.9510             nan     1.0000   -0.0221
##      4        1.0101             nan     1.0000   -0.0869
##      5        0.9791             nan     1.0000   -0.0187
##      6        6.7741             nan     1.0000   -2.9161
##      7        6.7856             nan     1.0000   -0.0720
##      8        6.7951             nan     1.0000   -0.0589
##      9        6.8549             nan     1.0000   -0.0984
##     10        6.8603             nan     1.0000   -0.0480
##     20        6.8397             nan     1.0000   -0.0470
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0560             nan     1.0000    0.0860
##      2        0.9959             nan     1.0000   -0.0277
##      3        0.9565             nan     1.0000    0.0010
##      4        0.9289             nan     1.0000   -0.0117
##      5        0.9181             nan     1.0000   -0.0338
##      6        0.9019             nan     1.0000   -0.0119
##      7        0.9273             nan     1.0000   -0.0647
##      8        0.9328             nan     1.0000   -0.0372
##      9        0.9514             nan     1.0000   -0.0527
##     10        0.9315             nan     1.0000    0.0023
##     20        1.0462             nan     1.0000   -0.0322
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        0.9950             nan     1.0000    0.1372
##      2        0.9698             nan     1.0000   -0.0352
##      3        0.9589             nan     1.0000   -0.0389
##      4        0.9459             nan     1.0000   -0.0200
##      5        1.0035             nan     1.0000   -0.1079
##      6        1.0742             nan     1.0000   -0.1186
##      7        1.0606             nan     1.0000   -0.0598
##      8        1.0607             nan     1.0000   -0.0486
##      9        1.0112             nan     1.0000   -0.0258
##     10        0.9946             nan     1.0000   -0.0378
##     20 1408633044.0288             nan     1.0000   -0.7810
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0220             nan     1.0000    0.1078
##      2        0.9493             nan     1.0000    0.0062
##      3        0.9002             nan     1.0000    0.0156
##      4        0.8788             nan     1.0000   -0.0182
##      5        0.8696             nan     1.0000   -0.0217
##      6        0.8449             nan     1.0000   -0.0095
##      7        0.8710             nan     1.0000   -0.0624
##      8        0.8910             nan     1.0000   -0.0665
##      9        0.8741             nan     1.0000   -0.0165
##     10        0.8750             nan     1.0000   -0.0418
##     20        0.8579             nan     1.0000   -0.0707
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0349             nan     1.0000    0.0901
##      2        0.9304             nan     1.0000    0.0240
##      3        0.9240             nan     1.0000   -0.0290
##      4        0.9055             nan     1.0000   -0.0164
##      5        0.8842             nan     1.0000   -0.0079
##      6        0.9083             nan     1.0000   -0.0583
##      7        1.3992             nan     1.0000   -0.3016
##      8        1.3855             nan     1.0000   -0.0164
##      9        1.3917             nan     1.0000   -0.0354
##     10        1.0221             nan     1.0000    0.1536
##     20        0.8629             nan     1.0000   -0.0072
##     40 29217001642.9100             nan     1.0000   -0.0001
##     60 151930677290.9368             nan     1.0000    0.0034
##     80 151930677290.9258             nan     1.0000   -0.0002
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0001
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2789             nan     0.0010    0.0001
##     60        1.2720             nan     0.0010    0.0001
##     80        1.2653             nan     0.0010    0.0002
##    100        1.2588             nan     0.0010    0.0001
##    120        1.2524             nan     0.0010    0.0001
##    140        1.2465             nan     0.0010    0.0001
##    160        1.2406             nan     0.0010    0.0001
##    180        1.2351             nan     0.0010    0.0001
##    200        1.2294             nan     0.0010    0.0001
##    220        1.2243             nan     0.0010    0.0001
##    240        1.2191             nan     0.0010    0.0001
##    260        1.2141             nan     0.0010    0.0001
##    280        1.2093             nan     0.0010    0.0001
##    300        1.2044             nan     0.0010    0.0001
##    320        1.1999             nan     0.0010    0.0001
##    340        1.1955             nan     0.0010    0.0001
##    360        1.1912             nan     0.0010    0.0001
##    380        1.1869             nan     0.0010    0.0001
##    400        1.1829             nan     0.0010    0.0001
##    420        1.1788             nan     0.0010    0.0001
##    440        1.1750             nan     0.0010    0.0001
##    460        1.1711             nan     0.0010    0.0001
##    480        1.1675             nan     0.0010    0.0001
##    500        1.1637             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0001
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2862             nan     0.0010    0.0001
##     40        1.2791             nan     0.0010    0.0002
##     60        1.2723             nan     0.0010    0.0001
##     80        1.2660             nan     0.0010    0.0002
##    100        1.2593             nan     0.0010    0.0001
##    120        1.2531             nan     0.0010    0.0001
##    140        1.2469             nan     0.0010    0.0001
##    160        1.2409             nan     0.0010    0.0001
##    180        1.2351             nan     0.0010    0.0001
##    200        1.2296             nan     0.0010    0.0001
##    220        1.2242             nan     0.0010    0.0001
##    240        1.2190             nan     0.0010    0.0001
##    260        1.2141             nan     0.0010    0.0001
##    280        1.2093             nan     0.0010    0.0001
##    300        1.2046             nan     0.0010    0.0001
##    320        1.1998             nan     0.0010    0.0001
##    340        1.1953             nan     0.0010    0.0001
##    360        1.1908             nan     0.0010    0.0001
##    380        1.1867             nan     0.0010    0.0001
##    400        1.1827             nan     0.0010    0.0001
##    420        1.1787             nan     0.0010    0.0001
##    440        1.1748             nan     0.0010    0.0001
##    460        1.1709             nan     0.0010    0.0001
##    480        1.1672             nan     0.0010    0.0001
##    500        1.1635             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0001
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2790             nan     0.0010    0.0001
##     60        1.2720             nan     0.0010    0.0002
##     80        1.2653             nan     0.0010    0.0001
##    100        1.2588             nan     0.0010    0.0001
##    120        1.2526             nan     0.0010    0.0001
##    140        1.2466             nan     0.0010    0.0001
##    160        1.2408             nan     0.0010    0.0001
##    180        1.2352             nan     0.0010    0.0001
##    200        1.2299             nan     0.0010    0.0001
##    220        1.2246             nan     0.0010    0.0001
##    240        1.2196             nan     0.0010    0.0001
##    260        1.2148             nan     0.0010    0.0001
##    280        1.2101             nan     0.0010    0.0001
##    300        1.2055             nan     0.0010    0.0001
##    320        1.2010             nan     0.0010    0.0001
##    340        1.1963             nan     0.0010    0.0001
##    360        1.1918             nan     0.0010    0.0001
##    380        1.1876             nan     0.0010    0.0001
##    400        1.1835             nan     0.0010    0.0001
##    420        1.1794             nan     0.0010    0.0001
##    440        1.1755             nan     0.0010    0.0001
##    460        1.1716             nan     0.0010    0.0001
##    480        1.1678             nan     0.0010    0.0001
##    500        1.1640             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2841             nan     0.0010    0.0002
##     40        1.2747             nan     0.0010    0.0002
##     60        1.2658             nan     0.0010    0.0002
##     80        1.2572             nan     0.0010    0.0002
##    100        1.2488             nan     0.0010    0.0002
##    120        1.2406             nan     0.0010    0.0002
##    140        1.2329             nan     0.0010    0.0002
##    160        1.2253             nan     0.0010    0.0002
##    180        1.2183             nan     0.0010    0.0002
##    200        1.2111             nan     0.0010    0.0002
##    220        1.2041             nan     0.0010    0.0001
##    240        1.1974             nan     0.0010    0.0001
##    260        1.1908             nan     0.0010    0.0002
##    280        1.1845             nan     0.0010    0.0002
##    300        1.1784             nan     0.0010    0.0001
##    320        1.1724             nan     0.0010    0.0001
##    340        1.1666             nan     0.0010    0.0001
##    360        1.1608             nan     0.0010    0.0001
##    380        1.1553             nan     0.0010    0.0001
##    400        1.1496             nan     0.0010    0.0001
##    420        1.1445             nan     0.0010    0.0001
##    440        1.1394             nan     0.0010    0.0001
##    460        1.1344             nan     0.0010    0.0001
##    480        1.1296             nan     0.0010    0.0001
##    500        1.1248             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2838             nan     0.0010    0.0002
##     40        1.2748             nan     0.0010    0.0002
##     60        1.2660             nan     0.0010    0.0002
##     80        1.2573             nan     0.0010    0.0002
##    100        1.2489             nan     0.0010    0.0002
##    120        1.2409             nan     0.0010    0.0002
##    140        1.2332             nan     0.0010    0.0002
##    160        1.2254             nan     0.0010    0.0002
##    180        1.2180             nan     0.0010    0.0002
##    200        1.2110             nan     0.0010    0.0002
##    220        1.2043             nan     0.0010    0.0001
##    240        1.1975             nan     0.0010    0.0001
##    260        1.1908             nan     0.0010    0.0002
##    280        1.1846             nan     0.0010    0.0002
##    300        1.1784             nan     0.0010    0.0001
##    320        1.1723             nan     0.0010    0.0001
##    340        1.1664             nan     0.0010    0.0001
##    360        1.1607             nan     0.0010    0.0001
##    380        1.1552             nan     0.0010    0.0001
##    400        1.1498             nan     0.0010    0.0001
##    420        1.1445             nan     0.0010    0.0001
##    440        1.1392             nan     0.0010    0.0001
##    460        1.1341             nan     0.0010    0.0001
##    480        1.1292             nan     0.0010    0.0001
##    500        1.1244             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0003
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0003
##     20        1.2839             nan     0.0010    0.0002
##     40        1.2747             nan     0.0010    0.0002
##     60        1.2659             nan     0.0010    0.0002
##     80        1.2572             nan     0.0010    0.0002
##    100        1.2490             nan     0.0010    0.0002
##    120        1.2410             nan     0.0010    0.0002
##    140        1.2329             nan     0.0010    0.0002
##    160        1.2254             nan     0.0010    0.0002
##    180        1.2181             nan     0.0010    0.0002
##    200        1.2109             nan     0.0010    0.0002
##    220        1.2040             nan     0.0010    0.0002
##    240        1.1973             nan     0.0010    0.0002
##    260        1.1905             nan     0.0010    0.0001
##    280        1.1845             nan     0.0010    0.0001
##    300        1.1783             nan     0.0010    0.0002
##    320        1.1723             nan     0.0010    0.0001
##    340        1.1664             nan     0.0010    0.0001
##    360        1.1607             nan     0.0010    0.0001
##    380        1.1552             nan     0.0010    0.0001
##    400        1.1496             nan     0.0010    0.0001
##    420        1.1446             nan     0.0010    0.0001
##    440        1.1394             nan     0.0010    0.0001
##    460        1.1344             nan     0.0010    0.0001
##    480        1.1296             nan     0.0010    0.0001
##    500        1.1248             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0003
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2890             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2824             nan     0.0010    0.0002
##     40        1.2719             nan     0.0010    0.0002
##     60        1.2619             nan     0.0010    0.0002
##     80        1.2521             nan     0.0010    0.0002
##    100        1.2426             nan     0.0010    0.0002
##    120        1.2333             nan     0.0010    0.0002
##    140        1.2244             nan     0.0010    0.0002
##    160        1.2161             nan     0.0010    0.0002
##    180        1.2076             nan     0.0010    0.0002
##    200        1.1996             nan     0.0010    0.0002
##    220        1.1916             nan     0.0010    0.0002
##    240        1.1838             nan     0.0010    0.0002
##    260        1.1764             nan     0.0010    0.0001
##    280        1.1691             nan     0.0010    0.0001
##    300        1.1621             nan     0.0010    0.0002
##    320        1.1552             nan     0.0010    0.0002
##    340        1.1484             nan     0.0010    0.0001
##    360        1.1418             nan     0.0010    0.0001
##    380        1.1355             nan     0.0010    0.0001
##    400        1.1294             nan     0.0010    0.0001
##    420        1.1233             nan     0.0010    0.0001
##    440        1.1177             nan     0.0010    0.0001
##    460        1.1121             nan     0.0010    0.0001
##    480        1.1064             nan     0.0010    0.0001
##    500        1.1010             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0003
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2895             nan     0.0010    0.0003
##      8        1.2889             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0002
##     20        1.2824             nan     0.0010    0.0003
##     40        1.2720             nan     0.0010    0.0002
##     60        1.2618             nan     0.0010    0.0003
##     80        1.2519             nan     0.0010    0.0002
##    100        1.2424             nan     0.0010    0.0002
##    120        1.2334             nan     0.0010    0.0002
##    140        1.2245             nan     0.0010    0.0002
##    160        1.2159             nan     0.0010    0.0002
##    180        1.2075             nan     0.0010    0.0002
##    200        1.1993             nan     0.0010    0.0001
##    220        1.1912             nan     0.0010    0.0002
##    240        1.1834             nan     0.0010    0.0002
##    260        1.1760             nan     0.0010    0.0002
##    280        1.1685             nan     0.0010    0.0002
##    300        1.1617             nan     0.0010    0.0001
##    320        1.1547             nan     0.0010    0.0001
##    340        1.1480             nan     0.0010    0.0001
##    360        1.1419             nan     0.0010    0.0001
##    380        1.1358             nan     0.0010    0.0001
##    400        1.1297             nan     0.0010    0.0001
##    420        1.1237             nan     0.0010    0.0001
##    440        1.1180             nan     0.0010    0.0001
##    460        1.1124             nan     0.0010    0.0001
##    480        1.1069             nan     0.0010    0.0001
##    500        1.1015             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0003
##     10        1.2880             nan     0.0010    0.0002
##     20        1.2826             nan     0.0010    0.0002
##     40        1.2721             nan     0.0010    0.0002
##     60        1.2618             nan     0.0010    0.0002
##     80        1.2518             nan     0.0010    0.0002
##    100        1.2422             nan     0.0010    0.0002
##    120        1.2331             nan     0.0010    0.0002
##    140        1.2245             nan     0.0010    0.0002
##    160        1.2157             nan     0.0010    0.0002
##    180        1.2072             nan     0.0010    0.0002
##    200        1.1990             nan     0.0010    0.0002
##    220        1.1910             nan     0.0010    0.0002
##    240        1.1833             nan     0.0010    0.0002
##    260        1.1760             nan     0.0010    0.0002
##    280        1.1688             nan     0.0010    0.0001
##    300        1.1618             nan     0.0010    0.0001
##    320        1.1550             nan     0.0010    0.0002
##    340        1.1483             nan     0.0010    0.0001
##    360        1.1417             nan     0.0010    0.0001
##    380        1.1353             nan     0.0010    0.0001
##    400        1.1292             nan     0.0010    0.0001
##    420        1.1235             nan     0.0010    0.0001
##    440        1.1177             nan     0.0010    0.0001
##    460        1.1122             nan     0.0010    0.0001
##    480        1.1066             nan     0.0010    0.0001
##    500        1.1013             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2583             nan     0.1000    0.0170
##      2        1.2295             nan     0.1000    0.0142
##      3        1.2036             nan     0.1000    0.0112
##      4        1.1826             nan     0.1000    0.0093
##      5        1.1629             nan     0.1000    0.0090
##      6        1.1484             nan     0.1000    0.0066
##      7        1.1320             nan     0.1000    0.0080
##      8        1.1178             nan     0.1000    0.0068
##      9        1.1040             nan     0.1000    0.0050
##     10        1.0903             nan     0.1000    0.0040
##     20        1.0024             nan     0.1000    0.0016
##     40        0.9193             nan     0.1000    0.0006
##     60        0.8785             nan     0.1000    0.0004
##     80        0.8508             nan     0.1000   -0.0005
##    100        0.8319             nan     0.1000   -0.0010
##    120        0.8167             nan     0.1000   -0.0010
##    140        0.8025             nan     0.1000   -0.0005
##    160        0.7931             nan     0.1000   -0.0005
##    180        0.7826             nan     0.1000   -0.0014
##    200        0.7768             nan     0.1000   -0.0007
##    220        0.7685             nan     0.1000   -0.0008
##    240        0.7640             nan     0.1000   -0.0016
##    260        0.7557             nan     0.1000   -0.0006
##    280        0.7510             nan     0.1000   -0.0002
##    300        0.7458             nan     0.1000   -0.0004
##    320        0.7392             nan     0.1000   -0.0010
##    340        0.7328             nan     0.1000   -0.0006
##    360        0.7246             nan     0.1000   -0.0006
##    380        0.7191             nan     0.1000   -0.0004
##    400        0.7131             nan     0.1000   -0.0013
##    420        0.7094             nan     0.1000   -0.0009
##    440        0.7033             nan     0.1000   -0.0007
##    460        0.6996             nan     0.1000   -0.0011
##    480        0.6956             nan     0.1000   -0.0007
##    500        0.6911             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2563             nan     0.1000    0.0176
##      2        1.2310             nan     0.1000    0.0122
##      3        1.2060             nan     0.1000    0.0128
##      4        1.1814             nan     0.1000    0.0098
##      5        1.1613             nan     0.1000    0.0096
##      6        1.1445             nan     0.1000    0.0080
##      7        1.1291             nan     0.1000    0.0060
##      8        1.1132             nan     0.1000    0.0063
##      9        1.0987             nan     0.1000    0.0060
##     10        1.0864             nan     0.1000    0.0044
##     20        0.9996             nan     0.1000    0.0027
##     40        0.9199             nan     0.1000   -0.0007
##     60        0.8767             nan     0.1000   -0.0028
##     80        0.8501             nan     0.1000   -0.0008
##    100        0.8334             nan     0.1000   -0.0006
##    120        0.8175             nan     0.1000   -0.0001
##    140        0.8056             nan     0.1000   -0.0006
##    160        0.7940             nan     0.1000   -0.0007
##    180        0.7826             nan     0.1000   -0.0007
##    200        0.7740             nan     0.1000    0.0000
##    220        0.7673             nan     0.1000   -0.0013
##    240        0.7589             nan     0.1000   -0.0009
##    260        0.7520             nan     0.1000   -0.0003
##    280        0.7443             nan     0.1000   -0.0005
##    300        0.7386             nan     0.1000   -0.0027
##    320        0.7335             nan     0.1000   -0.0007
##    340        0.7273             nan     0.1000   -0.0007
##    360        0.7212             nan     0.1000   -0.0009
##    380        0.7162             nan     0.1000   -0.0006
##    400        0.7121             nan     0.1000   -0.0008
##    420        0.7059             nan     0.1000   -0.0001
##    440        0.7017             nan     0.1000   -0.0017
##    460        0.6972             nan     0.1000   -0.0013
##    480        0.6913             nan     0.1000   -0.0005
##    500        0.6875             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2601             nan     0.1000    0.0158
##      2        1.2287             nan     0.1000    0.0130
##      3        1.2034             nan     0.1000    0.0120
##      4        1.1839             nan     0.1000    0.0084
##      5        1.1626             nan     0.1000    0.0084
##      6        1.1468             nan     0.1000    0.0068
##      7        1.1305             nan     0.1000    0.0071
##      8        1.1149             nan     0.1000    0.0056
##      9        1.1024             nan     0.1000    0.0031
##     10        1.0876             nan     0.1000    0.0060
##     20        1.0029             nan     0.1000    0.0024
##     40        0.9236             nan     0.1000   -0.0007
##     60        0.8829             nan     0.1000    0.0001
##     80        0.8573             nan     0.1000   -0.0001
##    100        0.8375             nan     0.1000   -0.0013
##    120        0.8242             nan     0.1000   -0.0012
##    140        0.8143             nan     0.1000   -0.0010
##    160        0.8030             nan     0.1000   -0.0009
##    180        0.7926             nan     0.1000   -0.0004
##    200        0.7799             nan     0.1000   -0.0014
##    220        0.7718             nan     0.1000   -0.0010
##    240        0.7622             nan     0.1000   -0.0003
##    260        0.7533             nan     0.1000   -0.0002
##    280        0.7477             nan     0.1000   -0.0003
##    300        0.7425             nan     0.1000   -0.0017
##    320        0.7350             nan     0.1000   -0.0010
##    340        0.7295             nan     0.1000   -0.0021
##    360        0.7245             nan     0.1000   -0.0010
##    380        0.7196             nan     0.1000   -0.0007
##    400        0.7153             nan     0.1000   -0.0014
##    420        0.7099             nan     0.1000   -0.0004
##    440        0.7044             nan     0.1000   -0.0012
##    460        0.7018             nan     0.1000   -0.0008
##    480        0.6971             nan     0.1000   -0.0009
##    500        0.6925             nan     0.1000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2455             nan     0.1000    0.0212
##      2        1.2090             nan     0.1000    0.0182
##      3        1.1747             nan     0.1000    0.0157
##      4        1.1439             nan     0.1000    0.0104
##      5        1.1192             nan     0.1000    0.0124
##      6        1.0955             nan     0.1000    0.0103
##      7        1.0755             nan     0.1000    0.0086
##      8        1.0606             nan     0.1000    0.0054
##      9        1.0475             nan     0.1000    0.0054
##     10        1.0335             nan     0.1000    0.0032
##     20        0.9371             nan     0.1000    0.0003
##     40        0.8548             nan     0.1000   -0.0002
##     60        0.8155             nan     0.1000   -0.0065
##     80        0.7785             nan     0.1000   -0.0021
##    100        0.7411             nan     0.1000   -0.0007
##    120        0.7104             nan     0.1000   -0.0011
##    140        0.6846             nan     0.1000   -0.0001
##    160        0.6618             nan     0.1000   -0.0012
##    180        0.6434             nan     0.1000   -0.0010
##    200        0.6256             nan     0.1000   -0.0006
##    220        0.6089             nan     0.1000   -0.0003
##    240        0.5957             nan     0.1000   -0.0004
##    260        0.5808             nan     0.1000   -0.0014
##    280        0.5662             nan     0.1000   -0.0013
##    300        0.5543             nan     0.1000   -0.0019
##    320        0.5404             nan     0.1000   -0.0003
##    340        0.5272             nan     0.1000   -0.0014
##    360        0.5098             nan     0.1000   -0.0020
##    380        0.4976             nan     0.1000   -0.0013
##    400        0.4871             nan     0.1000   -0.0006
##    420        0.4738             nan     0.1000   -0.0011
##    440        0.4612             nan     0.1000   -0.0009
##    460        0.4506             nan     0.1000   -0.0001
##    480        0.4386             nan     0.1000   -0.0007
##    500        0.4276             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2443             nan     0.1000    0.0217
##      2        1.2105             nan     0.1000    0.0170
##      3        1.1775             nan     0.1000    0.0172
##      4        1.1500             nan     0.1000    0.0102
##      5        1.1261             nan     0.1000    0.0106
##      6        1.1027             nan     0.1000    0.0099
##      7        1.0838             nan     0.1000    0.0063
##      8        1.0642             nan     0.1000    0.0074
##      9        1.0470             nan     0.1000    0.0073
##     10        1.0338             nan     0.1000    0.0052
##     20        0.9346             nan     0.1000   -0.0001
##     40        0.8503             nan     0.1000   -0.0021
##     60        0.8004             nan     0.1000   -0.0008
##     80        0.7649             nan     0.1000   -0.0012
##    100        0.7361             nan     0.1000   -0.0019
##    120        0.7135             nan     0.1000   -0.0022
##    140        0.6909             nan     0.1000   -0.0004
##    160        0.6721             nan     0.1000   -0.0014
##    180        0.6485             nan     0.1000   -0.0011
##    200        0.6299             nan     0.1000   -0.0025
##    220        0.6153             nan     0.1000   -0.0012
##    240        0.5999             nan     0.1000   -0.0016
##    260        0.5863             nan     0.1000   -0.0007
##    280        0.5677             nan     0.1000   -0.0012
##    300        0.5507             nan     0.1000   -0.0009
##    320        0.5385             nan     0.1000   -0.0016
##    340        0.5288             nan     0.1000   -0.0019
##    360        0.5172             nan     0.1000   -0.0003
##    380        0.5031             nan     0.1000   -0.0005
##    400        0.4922             nan     0.1000   -0.0015
##    420        0.4790             nan     0.1000   -0.0012
##    440        0.4674             nan     0.1000   -0.0007
##    460        0.4579             nan     0.1000   -0.0009
##    480        0.4466             nan     0.1000   -0.0010
##    500        0.4346             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2511             nan     0.1000    0.0154
##      2        1.2112             nan     0.1000    0.0191
##      3        1.1792             nan     0.1000    0.0135
##      4        1.1502             nan     0.1000    0.0127
##      5        1.1212             nan     0.1000    0.0107
##      6        1.1025             nan     0.1000    0.0066
##      7        1.0860             nan     0.1000    0.0052
##      8        1.0670             nan     0.1000    0.0090
##      9        1.0478             nan     0.1000    0.0069
##     10        1.0351             nan     0.1000    0.0032
##     20        0.9319             nan     0.1000    0.0005
##     40        0.8482             nan     0.1000    0.0004
##     60        0.7987             nan     0.1000   -0.0008
##     80        0.7617             nan     0.1000    0.0001
##    100        0.7394             nan     0.1000   -0.0011
##    120        0.7146             nan     0.1000   -0.0004
##    140        0.6899             nan     0.1000   -0.0002
##    160        0.6693             nan     0.1000   -0.0003
##    180        0.6489             nan     0.1000    0.0000
##    200        0.6341             nan     0.1000   -0.0012
##    220        0.6174             nan     0.1000   -0.0015
##    240        0.6049             nan     0.1000   -0.0009
##    260        0.5880             nan     0.1000   -0.0009
##    280        0.5722             nan     0.1000   -0.0012
##    300        0.5556             nan     0.1000   -0.0012
##    320        0.5420             nan     0.1000   -0.0013
##    340        0.5268             nan     0.1000   -0.0011
##    360        0.5150             nan     0.1000   -0.0008
##    380        0.5022             nan     0.1000   -0.0005
##    400        0.4917             nan     0.1000   -0.0014
##    420        0.4809             nan     0.1000   -0.0007
##    440        0.4692             nan     0.1000   -0.0011
##    460        0.4591             nan     0.1000   -0.0008
##    480        0.4478             nan     0.1000   -0.0006
##    500        0.4382             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2426             nan     0.1000    0.0222
##      2        1.1932             nan     0.1000    0.0219
##      3        1.1542             nan     0.1000    0.0154
##      4        1.1220             nan     0.1000    0.0142
##      5        1.0909             nan     0.1000    0.0091
##      6        1.0700             nan     0.1000    0.0072
##      7        1.0464             nan     0.1000    0.0065
##      8        1.0239             nan     0.1000    0.0091
##      9        1.0065             nan     0.1000    0.0019
##     10        0.9904             nan     0.1000    0.0043
##     20        0.8937             nan     0.1000    0.0014
##     40        0.7940             nan     0.1000    0.0001
##     60        0.7330             nan     0.1000   -0.0017
##     80        0.6927             nan     0.1000   -0.0012
##    100        0.6530             nan     0.1000   -0.0007
##    120        0.6161             nan     0.1000   -0.0003
##    140        0.5835             nan     0.1000   -0.0005
##    160        0.5543             nan     0.1000   -0.0016
##    180        0.5292             nan     0.1000   -0.0014
##    200        0.5037             nan     0.1000   -0.0015
##    220        0.4805             nan     0.1000   -0.0022
##    240        0.4614             nan     0.1000   -0.0016
##    260        0.4445             nan     0.1000   -0.0014
##    280        0.4269             nan     0.1000   -0.0012
##    300        0.4083             nan     0.1000   -0.0014
##    320        0.3910             nan     0.1000   -0.0007
##    340        0.3775             nan     0.1000   -0.0004
##    360        0.3645             nan     0.1000   -0.0013
##    380        0.3489             nan     0.1000   -0.0014
##    400        0.3361             nan     0.1000   -0.0005
##    420        0.3225             nan     0.1000   -0.0003
##    440        0.3109             nan     0.1000   -0.0009
##    460        0.3018             nan     0.1000   -0.0007
##    480        0.2895             nan     0.1000   -0.0006
##    500        0.2805             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2421             nan     0.1000    0.0254
##      2        1.2042             nan     0.1000    0.0194
##      3        1.1650             nan     0.1000    0.0161
##      4        1.1312             nan     0.1000    0.0141
##      5        1.0989             nan     0.1000    0.0099
##      6        1.0756             nan     0.1000    0.0090
##      7        1.0533             nan     0.1000    0.0093
##      8        1.0366             nan     0.1000    0.0038
##      9        1.0203             nan     0.1000    0.0044
##     10        1.0025             nan     0.1000    0.0058
##     20        0.8992             nan     0.1000    0.0003
##     40        0.7937             nan     0.1000   -0.0010
##     60        0.7336             nan     0.1000   -0.0022
##     80        0.6898             nan     0.1000   -0.0015
##    100        0.6453             nan     0.1000   -0.0003
##    120        0.6105             nan     0.1000   -0.0006
##    140        0.5763             nan     0.1000   -0.0011
##    160        0.5515             nan     0.1000   -0.0009
##    180        0.5287             nan     0.1000   -0.0004
##    200        0.5030             nan     0.1000   -0.0011
##    220        0.4809             nan     0.1000   -0.0013
##    240        0.4614             nan     0.1000   -0.0003
##    260        0.4414             nan     0.1000   -0.0005
##    280        0.4202             nan     0.1000   -0.0015
##    300        0.4027             nan     0.1000   -0.0007
##    320        0.3869             nan     0.1000   -0.0006
##    340        0.3720             nan     0.1000   -0.0006
##    360        0.3554             nan     0.1000   -0.0009
##    380        0.3397             nan     0.1000   -0.0001
##    400        0.3254             nan     0.1000   -0.0006
##    420        0.3114             nan     0.1000   -0.0008
##    440        0.2992             nan     0.1000   -0.0004
##    460        0.2884             nan     0.1000   -0.0009
##    480        0.2785             nan     0.1000   -0.0005
##    500        0.2688             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2402             nan     0.1000    0.0258
##      2        1.2001             nan     0.1000    0.0145
##      3        1.1552             nan     0.1000    0.0182
##      4        1.1232             nan     0.1000    0.0118
##      5        1.0933             nan     0.1000    0.0095
##      6        1.0685             nan     0.1000    0.0090
##      7        1.0406             nan     0.1000    0.0090
##      8        1.0236             nan     0.1000    0.0048
##      9        1.0054             nan     0.1000    0.0061
##     10        0.9903             nan     0.1000    0.0055
##     20        0.8938             nan     0.1000    0.0009
##     40        0.8024             nan     0.1000   -0.0006
##     60        0.7474             nan     0.1000   -0.0027
##     80        0.7083             nan     0.1000   -0.0009
##    100        0.6637             nan     0.1000   -0.0019
##    120        0.6331             nan     0.1000   -0.0018
##    140        0.5999             nan     0.1000   -0.0006
##    160        0.5731             nan     0.1000   -0.0017
##    180        0.5407             nan     0.1000   -0.0001
##    200        0.5177             nan     0.1000   -0.0003
##    220        0.4929             nan     0.1000   -0.0020
##    240        0.4762             nan     0.1000   -0.0010
##    260        0.4542             nan     0.1000   -0.0017
##    280        0.4330             nan     0.1000   -0.0020
##    300        0.4170             nan     0.1000   -0.0005
##    320        0.4049             nan     0.1000   -0.0006
##    340        0.3874             nan     0.1000   -0.0003
##    360        0.3694             nan     0.1000   -0.0009
##    380        0.3550             nan     0.1000   -0.0011
##    400        0.3434             nan     0.1000   -0.0015
##    420        0.3287             nan     0.1000   -0.0008
##    440        0.3143             nan     0.1000   -0.0006
##    460        0.3012             nan     0.1000   -0.0010
##    480        0.2903             nan     0.1000   -0.0004
##    500        0.2790             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2237             nan     0.2000    0.0306
##      2        1.1711             nan     0.2000    0.0219
##      3        1.1309             nan     0.2000    0.0132
##      4        1.1043             nan     0.2000    0.0122
##      5        1.0799             nan     0.2000    0.0095
##      6        1.0608             nan     0.2000    0.0076
##      7        1.0446             nan     0.2000    0.0054
##      8        1.0265             nan     0.2000    0.0050
##      9        1.0141             nan     0.2000    0.0046
##     10        0.9994             nan     0.2000    0.0040
##     20        0.9174             nan     0.2000    0.0022
##     40        0.8538             nan     0.2000   -0.0021
##     60        0.8160             nan     0.2000   -0.0007
##     80        0.7954             nan     0.2000   -0.0010
##    100        0.7795             nan     0.2000   -0.0028
##    120        0.7600             nan     0.2000   -0.0016
##    140        0.7490             nan     0.2000   -0.0011
##    160        0.7374             nan     0.2000   -0.0031
##    180        0.7264             nan     0.2000   -0.0011
##    200        0.7172             nan     0.2000   -0.0007
##    220        0.7092             nan     0.2000   -0.0013
##    240        0.6987             nan     0.2000   -0.0017
##    260        0.6929             nan     0.2000   -0.0003
##    280        0.6802             nan     0.2000   -0.0022
##    300        0.6724             nan     0.2000   -0.0002
##    320        0.6632             nan     0.2000   -0.0011
##    340        0.6573             nan     0.2000   -0.0012
##    360        0.6510             nan     0.2000   -0.0027
##    380        0.6431             nan     0.2000   -0.0013
##    400        0.6372             nan     0.2000   -0.0012
##    420        0.6330             nan     0.2000   -0.0031
##    440        0.6262             nan     0.2000    0.0002
##    460        0.6227             nan     0.2000   -0.0010
##    480        0.6200             nan     0.2000   -0.0037
##    500        0.6191             nan     0.2000   -0.0032
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2239             nan     0.2000    0.0300
##      2        1.1738             nan     0.2000    0.0220
##      3        1.1432             nan     0.2000    0.0136
##      4        1.1140             nan     0.2000    0.0120
##      5        1.0822             nan     0.2000    0.0127
##      6        1.0654             nan     0.2000    0.0046
##      7        1.0428             nan     0.2000    0.0104
##      8        1.0267             nan     0.2000    0.0057
##      9        1.0142             nan     0.2000    0.0018
##     10        0.9984             nan     0.2000    0.0052
##     20        0.9179             nan     0.2000    0.0003
##     40        0.8553             nan     0.2000   -0.0008
##     60        0.8225             nan     0.2000   -0.0009
##     80        0.7972             nan     0.2000   -0.0004
##    100        0.7783             nan     0.2000   -0.0025
##    120        0.7642             nan     0.2000   -0.0027
##    140        0.7426             nan     0.2000   -0.0002
##    160        0.7306             nan     0.2000   -0.0019
##    180        0.7197             nan     0.2000   -0.0014
##    200        0.7114             nan     0.2000   -0.0028
##    220        0.7070             nan     0.2000   -0.0036
##    240        0.6959             nan     0.2000   -0.0013
##    260        0.6839             nan     0.2000   -0.0012
##    280        0.6778             nan     0.2000   -0.0016
##    300        0.6708             nan     0.2000   -0.0003
##    320        0.6643             nan     0.2000   -0.0013
##    340        0.6572             nan     0.2000   -0.0007
##    360        0.6516             nan     0.2000   -0.0019
##    380        0.6471             nan     0.2000   -0.0019
##    400        0.6416             nan     0.2000   -0.0017
##    420        0.6348             nan     0.2000   -0.0002
##    440        0.6274             nan     0.2000   -0.0011
##    460        0.6218             nan     0.2000   -0.0005
##    480        0.6166             nan     0.2000   -0.0005
##    500        0.6156             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2287             nan     0.2000    0.0327
##      2        1.1733             nan     0.2000    0.0206
##      3        1.1400             nan     0.2000    0.0127
##      4        1.1108             nan     0.2000    0.0129
##      5        1.0882             nan     0.2000    0.0081
##      6        1.0703             nan     0.2000    0.0067
##      7        1.0475             nan     0.2000    0.0110
##      8        1.0278             nan     0.2000    0.0057
##      9        1.0144             nan     0.2000    0.0037
##     10        0.9979             nan     0.2000    0.0047
##     20        0.9232             nan     0.2000   -0.0022
##     40        0.8665             nan     0.2000   -0.0017
##     60        0.8324             nan     0.2000   -0.0031
##     80        0.8041             nan     0.2000   -0.0012
##    100        0.7841             nan     0.2000   -0.0019
##    120        0.7739             nan     0.2000   -0.0020
##    140        0.7575             nan     0.2000   -0.0029
##    160        0.7443             nan     0.2000   -0.0035
##    180        0.7343             nan     0.2000   -0.0031
##    200        0.7255             nan     0.2000   -0.0012
##    220        0.7148             nan     0.2000   -0.0036
##    240        0.7062             nan     0.2000   -0.0011
##    260        0.7003             nan     0.2000   -0.0016
##    280        0.6932             nan     0.2000   -0.0007
##    300        0.6879             nan     0.2000   -0.0016
##    320        0.6784             nan     0.2000   -0.0005
##    340        0.6724             nan     0.2000   -0.0011
##    360        0.6647             nan     0.2000   -0.0014
##    380        0.6577             nan     0.2000   -0.0018
##    400        0.6516             nan     0.2000   -0.0019
##    420        0.6430             nan     0.2000   -0.0020
##    440        0.6398             nan     0.2000   -0.0041
##    460        0.6333             nan     0.2000   -0.0029
##    480        0.6278             nan     0.2000   -0.0017
##    500        0.6227             nan     0.2000   -0.0040
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2067             nan     0.2000    0.0411
##      2        1.1440             nan     0.2000    0.0278
##      3        1.0909             nan     0.2000    0.0199
##      4        1.0562             nan     0.2000    0.0142
##      5        1.0252             nan     0.2000    0.0076
##      6        1.0061             nan     0.2000    0.0074
##      7        0.9851             nan     0.2000    0.0074
##      8        0.9738             nan     0.2000    0.0006
##      9        0.9514             nan     0.2000    0.0050
##     10        0.9378             nan     0.2000    0.0049
##     20        0.8557             nan     0.2000   -0.0039
##     40        0.7752             nan     0.2000   -0.0045
##     60        0.7229             nan     0.2000   -0.0020
##     80        0.6839             nan     0.2000   -0.0025
##    100        0.6484             nan     0.2000   -0.0032
##    120        0.6172             nan     0.2000   -0.0048
##    140        0.5842             nan     0.2000   -0.0049
##    160        0.5561             nan     0.2000   -0.0016
##    180        0.5251             nan     0.2000   -0.0015
##    200        0.5018             nan     0.2000   -0.0028
##    220        0.4827             nan     0.2000   -0.0009
##    240        0.4555             nan     0.2000   -0.0026
##    260        0.4334             nan     0.2000   -0.0022
##    280        0.4192             nan     0.2000   -0.0026
##    300        0.3983             nan     0.2000   -0.0002
##    320        0.3786             nan     0.2000   -0.0023
##    340        0.3650             nan     0.2000   -0.0019
##    360        0.3474             nan     0.2000   -0.0031
##    380        0.3371             nan     0.2000   -0.0025
##    400        0.3228             nan     0.2000   -0.0018
##    420        0.3124             nan     0.2000   -0.0013
##    440        0.3036             nan     0.2000   -0.0010
##    460        0.2927             nan     0.2000   -0.0013
##    480        0.2847             nan     0.2000   -0.0035
##    500        0.2770             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2042             nan     0.2000    0.0399
##      2        1.1508             nan     0.2000    0.0227
##      3        1.0981             nan     0.2000    0.0191
##      4        1.0621             nan     0.2000    0.0148
##      5        1.0337             nan     0.2000    0.0084
##      6        1.0098             nan     0.2000    0.0086
##      7        0.9894             nan     0.2000    0.0085
##      8        0.9756             nan     0.2000    0.0013
##      9        0.9574             nan     0.2000    0.0050
##     10        0.9435             nan     0.2000   -0.0015
##     20        0.8558             nan     0.2000   -0.0010
##     40        0.7751             nan     0.2000   -0.0013
##     60        0.7224             nan     0.2000   -0.0013
##     80        0.6828             nan     0.2000   -0.0021
##    100        0.6507             nan     0.2000   -0.0018
##    120        0.6167             nan     0.2000   -0.0036
##    140        0.5884             nan     0.2000   -0.0018
##    160        0.5543             nan     0.2000   -0.0014
##    180        0.5323             nan     0.2000   -0.0037
##    200        0.5029             nan     0.2000   -0.0007
##    220        0.4778             nan     0.2000   -0.0014
##    240        0.4603             nan     0.2000   -0.0019
##    260        0.4400             nan     0.2000   -0.0021
##    280        0.4214             nan     0.2000   -0.0007
##    300        0.4025             nan     0.2000   -0.0017
##    320        0.3860             nan     0.2000   -0.0008
##    340        0.3668             nan     0.2000   -0.0015
##    360        0.3538             nan     0.2000   -0.0013
##    380        0.3370             nan     0.2000   -0.0006
##    400        0.3218             nan     0.2000   -0.0021
##    420        0.3109             nan     0.2000   -0.0007
##    440        0.2997             nan     0.2000   -0.0015
##    460        0.2860             nan     0.2000   -0.0017
##    480        0.2788             nan     0.2000   -0.0008
##    500        0.2711             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2015             nan     0.2000    0.0407
##      2        1.1346             nan     0.2000    0.0270
##      3        1.0936             nan     0.2000    0.0197
##      4        1.0578             nan     0.2000    0.0124
##      5        1.0337             nan     0.2000    0.0071
##      6        1.0084             nan     0.2000    0.0104
##      7        0.9867             nan     0.2000    0.0077
##      8        0.9665             nan     0.2000    0.0082
##      9        0.9525             nan     0.2000    0.0024
##     10        0.9436             nan     0.2000   -0.0018
##     20        0.8554             nan     0.2000   -0.0030
##     40        0.7788             nan     0.2000   -0.0032
##     60        0.7309             nan     0.2000   -0.0010
##     80        0.6787             nan     0.2000    0.0003
##    100        0.6429             nan     0.2000   -0.0022
##    120        0.6133             nan     0.2000   -0.0014
##    140        0.5858             nan     0.2000   -0.0022
##    160        0.5515             nan     0.2000   -0.0023
##    180        0.5240             nan     0.2000   -0.0010
##    200        0.5005             nan     0.2000   -0.0014
##    220        0.4815             nan     0.2000   -0.0017
##    240        0.4571             nan     0.2000   -0.0031
##    260        0.4278             nan     0.2000   -0.0001
##    280        0.4098             nan     0.2000   -0.0014
##    300        0.3938             nan     0.2000   -0.0023
##    320        0.3780             nan     0.2000   -0.0020
##    340        0.3598             nan     0.2000   -0.0013
##    360        0.3472             nan     0.2000   -0.0023
##    380        0.3336             nan     0.2000   -0.0008
##    400        0.3241             nan     0.2000   -0.0018
##    420        0.3139             nan     0.2000   -0.0017
##    440        0.3028             nan     0.2000   -0.0011
##    460        0.2906             nan     0.2000   -0.0015
##    480        0.2829             nan     0.2000   -0.0004
##    500        0.2746             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1993             nan     0.2000    0.0392
##      2        1.1217             nan     0.2000    0.0317
##      3        1.0730             nan     0.2000    0.0181
##      4        1.0356             nan     0.2000    0.0149
##      5        1.0024             nan     0.2000    0.0136
##      6        0.9706             nan     0.2000    0.0075
##      7        0.9506             nan     0.2000    0.0048
##      8        0.9266             nan     0.2000    0.0038
##      9        0.9112             nan     0.2000    0.0004
##     10        0.9021             nan     0.2000   -0.0024
##     20        0.8124             nan     0.2000    0.0002
##     40        0.7102             nan     0.2000   -0.0014
##     60        0.6427             nan     0.2000   -0.0061
##     80        0.5840             nan     0.2000   -0.0011
##    100        0.5239             nan     0.2000   -0.0005
##    120        0.4739             nan     0.2000   -0.0025
##    140        0.4345             nan     0.2000   -0.0031
##    160        0.3974             nan     0.2000   -0.0039
##    180        0.3593             nan     0.2000   -0.0018
##    200        0.3364             nan     0.2000   -0.0026
##    220        0.3128             nan     0.2000   -0.0028
##    240        0.2910             nan     0.2000   -0.0019
##    260        0.2722             nan     0.2000   -0.0015
##    280        0.2521             nan     0.2000   -0.0008
##    300        0.2348             nan     0.2000   -0.0009
##    320        0.2181             nan     0.2000   -0.0023
##    340        0.2071             nan     0.2000   -0.0008
##    360        0.1936             nan     0.2000   -0.0010
##    380        0.1819             nan     0.2000   -0.0004
##    400        0.1737             nan     0.2000   -0.0011
##    420        0.1633             nan     0.2000   -0.0005
##    440        0.1522             nan     0.2000   -0.0008
##    460        0.1413             nan     0.2000   -0.0006
##    480        0.1337             nan     0.2000   -0.0005
##    500        0.1272             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1895             nan     0.2000    0.0413
##      2        1.1124             nan     0.2000    0.0317
##      3        1.0714             nan     0.2000    0.0131
##      4        1.0308             nan     0.2000    0.0179
##      5        0.9977             nan     0.2000    0.0082
##      6        0.9767             nan     0.2000    0.0079
##      7        0.9476             nan     0.2000    0.0107
##      8        0.9293             nan     0.2000    0.0025
##      9        0.9148             nan     0.2000    0.0005
##     10        0.8980             nan     0.2000    0.0023
##     20        0.7983             nan     0.2000   -0.0016
##     40        0.6975             nan     0.2000   -0.0051
##     60        0.6408             nan     0.2000   -0.0033
##     80        0.5734             nan     0.2000   -0.0017
##    100        0.5259             nan     0.2000   -0.0020
##    120        0.4779             nan     0.2000   -0.0006
##    140        0.4414             nan     0.2000   -0.0016
##    160        0.4056             nan     0.2000   -0.0007
##    180        0.3738             nan     0.2000   -0.0020
##    200        0.3454             nan     0.2000   -0.0130
##    220        0.3177             nan     0.2000   -0.0020
##    240        0.2959             nan     0.2000   -0.0010
##    260        0.2722             nan     0.2000   -0.0011
##    280        0.2526             nan     0.2000   -0.0014
##    300        0.2372             nan     0.2000   -0.0021
##    320        0.2231             nan     0.2000   -0.0018
##    340        0.2081             nan     0.2000   -0.0001
##    360        0.1920             nan     0.2000   -0.0005
##    380        0.1801             nan     0.2000   -0.0002
##    400        0.1677             nan     0.2000   -0.0003
##    420        0.1572             nan     0.2000   -0.0010
##    440        0.1475             nan     0.2000   -0.0010
##    460        0.1403             nan     0.2000   -0.0008
##    480        0.1316             nan     0.2000   -0.0001
##    500        0.1258             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1877             nan     0.2000    0.0478
##      2        1.1334             nan     0.2000    0.0222
##      3        1.0850             nan     0.2000    0.0194
##      4        1.0445             nan     0.2000    0.0159
##      5        1.0085             nan     0.2000    0.0123
##      6        0.9816             nan     0.2000    0.0082
##      7        0.9629             nan     0.2000    0.0021
##      8        0.9372             nan     0.2000    0.0116
##      9        0.9178             nan     0.2000    0.0048
##     10        0.9036             nan     0.2000    0.0037
##     20        0.7985             nan     0.2000    0.0011
##     40        0.7063             nan     0.2000   -0.0030
##     60        0.6374             nan     0.2000   -0.0029
##     80        0.5805             nan     0.2000   -0.0030
##    100        0.5236             nan     0.2000   -0.0036
##    120        0.4853             nan     0.2000   -0.0006
##    140        0.4437             nan     0.2000   -0.0010
##    160        0.4055             nan     0.2000   -0.0015
##    180        0.3739             nan     0.2000   -0.0012
##    200        0.3401             nan     0.2000   -0.0021
##    220        0.3095             nan     0.2000   -0.0024
##    240        0.2822             nan     0.2000   -0.0013
##    260        0.2594             nan     0.2000   -0.0010
##    280        0.2416             nan     0.2000   -0.0015
##    300        0.2265             nan     0.2000   -0.0020
##    320        0.2114             nan     0.2000   -0.0007
##    340        0.1957             nan     0.2000   -0.0010
##    360        0.1834             nan     0.2000   -0.0012
##    380        0.1722             nan     0.2000   -0.0014
##    400        0.1624             nan     0.2000   -0.0008
##    420        0.1508             nan     0.2000   -0.0005
##    440        0.1420             nan     0.2000   -0.0005
##    460        0.1326             nan     0.2000   -0.0010
##    480        0.1247             nan     0.2000   -0.0006
##    500        0.1178             nan     0.2000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2038             nan     0.3000    0.0465
##      2        1.1382             nan     0.3000    0.0289
##      3        1.1053             nan     0.3000    0.0099
##      4        1.0730             nan     0.3000    0.0125
##      5        1.0339             nan     0.3000    0.0121
##      6        1.0158             nan     0.3000    0.0044
##      7        0.9980             nan     0.3000    0.0043
##      8        0.9808             nan     0.3000    0.0038
##      9        0.9642             nan     0.3000    0.0058
##     10        0.9550             nan     0.3000   -0.0002
##     20        0.8896             nan     0.3000   -0.0023
##     40        0.8368             nan     0.3000   -0.0030
##     60        0.8056             nan     0.3000   -0.0007
##     80        0.7854             nan     0.3000   -0.0020
##    100        0.7573             nan     0.3000   -0.0006
##    120        0.7332             nan     0.3000   -0.0011
##    140        0.7168             nan     0.3000   -0.0011
##    160        0.7053             nan     0.3000   -0.0011
##    180        0.6931             nan     0.3000   -0.0029
##    200        0.6833             nan     0.3000   -0.0020
##    220        0.6726             nan     0.3000   -0.0047
##    240        0.6589             nan     0.3000   -0.0022
##    260        0.6459             nan     0.3000   -0.0018
##    280        0.6416             nan     0.3000   -0.0039
##    300        0.6272             nan     0.3000   -0.0030
##    320        0.6189             nan     0.3000   -0.0022
##    340        0.6161             nan     0.3000   -0.0057
##    360        0.6076             nan     0.3000   -0.0038
##    380        0.5993             nan     0.3000    0.0002
##    400        0.5901             nan     0.3000   -0.0009
##    420        0.5812             nan     0.3000   -0.0014
##    440        0.5800             nan     0.3000   -0.0056
##    460        0.5712             nan     0.3000   -0.0035
##    480        0.5732             nan     0.3000   -0.0029
##    500        0.5636             nan     0.3000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1883             nan     0.3000    0.0456
##      2        1.1331             nan     0.3000    0.0282
##      3        1.0941             nan     0.3000    0.0138
##      4        1.0574             nan     0.3000    0.0144
##      5        1.0376             nan     0.3000    0.0056
##      6        1.0239             nan     0.3000    0.0029
##      7        0.9953             nan     0.3000    0.0121
##      8        0.9827             nan     0.3000    0.0001
##      9        0.9664             nan     0.3000    0.0019
##     10        0.9491             nan     0.3000    0.0046
##     20        0.8809             nan     0.3000   -0.0001
##     40        0.8182             nan     0.3000   -0.0047
##     60        0.7917             nan     0.3000   -0.0032
##     80        0.7788             nan     0.3000   -0.0051
##    100        0.7621             nan     0.3000   -0.0033
##    120        0.7367             nan     0.3000   -0.0064
##    140        0.7151             nan     0.3000   -0.0032
##    160        0.7057             nan     0.3000   -0.0032
##    180        0.6913             nan     0.3000   -0.0020
##    200        0.6768             nan     0.3000   -0.0011
##    220        0.6712             nan     0.3000   -0.0036
##    240        0.6661             nan     0.3000   -0.0024
##    260        0.6530             nan     0.3000   -0.0058
##    280        0.6466             nan     0.3000   -0.0002
##    300        0.6385             nan     0.3000   -0.0038
##    320        0.6263             nan     0.3000   -0.0026
##    340        0.6209             nan     0.3000   -0.0014
##    360        0.6107             nan     0.3000   -0.0011
##    380        0.6030             nan     0.3000   -0.0026
##    400        0.5928             nan     0.3000   -0.0036
##    420        0.5840             nan     0.3000   -0.0018
##    440        0.5763             nan     0.3000   -0.0016
##    460        0.5710             nan     0.3000   -0.0013
##    480        0.5684             nan     0.3000   -0.0038
##    500        0.5592             nan     0.3000   -0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1981             nan     0.3000    0.0418
##      2        1.1525             nan     0.3000    0.0139
##      3        1.0997             nan     0.3000    0.0197
##      4        1.0642             nan     0.3000    0.0178
##      5        1.0404             nan     0.3000    0.0066
##      6        1.0122             nan     0.3000    0.0136
##      7        0.9952             nan     0.3000    0.0039
##      8        0.9808             nan     0.3000    0.0013
##      9        0.9680             nan     0.3000    0.0031
##     10        0.9512             nan     0.3000    0.0073
##     20        0.8924             nan     0.3000   -0.0020
##     40        0.8223             nan     0.3000   -0.0007
##     60        0.7871             nan     0.3000   -0.0040
##     80        0.7613             nan     0.3000   -0.0020
##    100        0.7426             nan     0.3000    0.0003
##    120        0.7281             nan     0.3000   -0.0084
##    140        0.7099             nan     0.3000   -0.0042
##    160        0.6954             nan     0.3000   -0.0023
##    180        0.6886             nan     0.3000   -0.0043
##    200        0.6758             nan     0.3000   -0.0011
##    220        0.6660             nan     0.3000   -0.0053
##    240        0.6568             nan     0.3000   -0.0034
##    260        0.6457             nan     0.3000   -0.0035
##    280        0.6328             nan     0.3000   -0.0034
##    300        0.6246             nan     0.3000   -0.0050
##    320        0.6137             nan     0.3000   -0.0007
##    340        0.6110             nan     0.3000   -0.0014
##    360        0.6079             nan     0.3000   -0.0073
##    380        0.6001             nan     0.3000   -0.0062
##    400        0.5933             nan     0.3000   -0.0025
##    420        0.5873             nan     0.3000   -0.0025
##    440        0.5828             nan     0.3000   -0.0014
##    460        0.5761             nan     0.3000   -0.0049
##    480        0.5708             nan     0.3000   -0.0016
##    500        0.5668             nan     0.3000   -0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1594             nan     0.3000    0.0546
##      2        1.0841             nan     0.3000    0.0298
##      3        1.0332             nan     0.3000    0.0197
##      4        0.9903             nan     0.3000    0.0081
##      5        0.9613             nan     0.3000    0.0079
##      6        0.9491             nan     0.3000   -0.0005
##      7        0.9341             nan     0.3000    0.0017
##      8        0.9146             nan     0.3000    0.0031
##      9        0.9050             nan     0.3000   -0.0019
##     10        0.8916             nan     0.3000   -0.0010
##     20        0.8207             nan     0.3000    0.0015
##     40        0.7303             nan     0.3000   -0.0020
##     60        0.6693             nan     0.3000   -0.0052
##     80        0.6198             nan     0.3000   -0.0056
##    100        1.0629             nan     0.3000   -0.0789
##    120       54.9271             nan     0.3000   -0.0029
##    140       54.8984             nan     0.3000   -0.0024
##    160       54.8690             nan     0.3000   -0.0002
##    180       54.8546             nan     0.3000   -0.0057
##    200       54.8416             nan     0.3000   -0.0008
##    220           inf             nan     0.3000      -inf
##    240           inf             nan     0.3000       nan
##    260           inf             nan     0.3000       nan
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1652             nan     0.3000    0.0550
##      2        1.1026             nan     0.3000    0.0199
##      3        1.0418             nan     0.3000    0.0171
##      4        1.0004             nan     0.3000    0.0136
##      5        0.9704             nan     0.3000    0.0093
##      6        0.9458             nan     0.3000    0.0098
##      7        0.9207             nan     0.3000    0.0028
##      8        0.9095             nan     0.3000   -0.0021
##      9        0.9009             nan     0.3000   -0.0016
##     10        0.8877             nan     0.3000    0.0002
##     20        0.8096             nan     0.3000   -0.0057
##     40        0.7211             nan     0.3000    0.0000
##     60        0.6575             nan     0.3000   -0.0020
##     80        0.6139             nan     0.3000   -0.0044
##    100        0.5736             nan     0.3000   -0.0042
##    120        0.5382             nan     0.3000   -0.0066
##    140        0.5007             nan     0.3000   -0.0015
##    160        0.4609             nan     0.3000   -0.0053
##    180        0.4316             nan     0.3000   -0.0008
##    200        0.4058             nan     0.3000   -0.0006
##    220        0.3893             nan     0.3000   -0.0037
##    240        0.3575             nan     0.3000   -0.0007
##    260        0.3323             nan     0.3000    0.0005
##    280        0.3076             nan     0.3000   -0.0008
##    300        0.2910             nan     0.3000   -0.0029
##    320        0.2794             nan     0.3000   -0.0016
##    340        0.2602             nan     0.3000   -0.0019
##    360        0.2468             nan     0.3000   -0.0012
##    380        0.2346             nan     0.3000   -0.0041
##    400        0.2218             nan     0.3000   -0.0020
##    420        0.2119             nan     0.3000   -0.0011
##    440        0.1996             nan     0.3000   -0.0006
##    460        0.1868             nan     0.3000   -0.0012
##    480        0.1778             nan     0.3000   -0.0004
##    500        0.1677             nan     0.3000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1707             nan     0.3000    0.0620
##      2        1.0881             nan     0.3000    0.0384
##      3        1.0388             nan     0.3000    0.0179
##      4        1.0004             nan     0.3000    0.0143
##      5        0.9646             nan     0.3000    0.0129
##      6        0.9398             nan     0.3000    0.0050
##      7        0.9249             nan     0.3000   -0.0007
##      8        0.9107             nan     0.3000    0.0016
##      9        0.8944             nan     0.3000    0.0007
##     10        0.8849             nan     0.3000   -0.0011
##     20        0.8033             nan     0.3000   -0.0024
##     40        0.7204             nan     0.3000   -0.0031
##     60        0.6607             nan     0.3000   -0.0041
##     80        0.6115             nan     0.3000   -0.0003
##    100        0.5582             nan     0.3000   -0.0027
##    120        0.5188             nan     0.3000   -0.0003
##    140        0.4792             nan     0.3000   -0.0044
##    160        0.4517             nan     0.3000   -0.0035
##    180        0.4300             nan     0.3000   -0.0008
##    200        0.4034             nan     0.3000   -0.0036
##    220        0.3847             nan     0.3000   -0.0039
##    240        0.3659             nan     0.3000   -0.0032
##    260        0.3433             nan     0.3000   -0.0011
##    280        0.3259             nan     0.3000   -0.0044
##    300        0.3119             nan     0.3000   -0.0039
##    320        0.2942             nan     0.3000   -0.0018
##    340        0.2753             nan     0.3000   -0.0019
##    360        0.2626             nan     0.3000   -0.0020
##    380        0.2500             nan     0.3000   -0.0022
##    400        0.2338             nan     0.3000   -0.0024
##    420        0.2200             nan     0.3000   -0.0015
##    440        0.2045             nan     0.3000   -0.0026
##    460        0.1948             nan     0.3000   -0.0011
##    480        0.1841             nan     0.3000   -0.0014
##    500        0.1733             nan     0.3000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1493             nan     0.3000    0.0525
##      2        1.0621             nan     0.3000    0.0383
##      3        1.0180             nan     0.3000    0.0057
##      4        0.9760             nan     0.3000    0.0098
##      5        0.9499             nan     0.3000    0.0006
##      6        0.9190             nan     0.3000    0.0073
##      7        0.9087             nan     0.3000   -0.0115
##      8        0.8908             nan     0.3000   -0.0019
##      9        0.8737             nan     0.3000   -0.0003
##     10        0.8622             nan     0.3000   -0.0048
##     20        0.7722             nan     0.3000   -0.0028
##     40        0.6478             nan     0.3000   -0.0069
##     60        0.5452             nan     0.3000    0.0008
##     80        0.4865             nan     0.3000   -0.0120
##    100        0.4415             nan     0.3000   -0.0058
##    120        0.3792             nan     0.3000   -0.0023
##    140        0.3322             nan     0.3000   -0.0027
##    160        0.2947             nan     0.3000   -0.0034
##    180        0.2678             nan     0.3000   -0.0032
##    200        0.2344             nan     0.3000   -0.0007
##    220        0.2190             nan     0.3000   -0.0022
##    240        0.1951             nan     0.3000   -0.0021
##    260        0.1753             nan     0.3000   -0.0008
##    280        0.1619             nan     0.3000   -0.0013
##    300        0.1461             nan     0.3000    0.0003
##    320        0.1351             nan     0.3000   -0.0007
##    340        0.1246             nan     0.3000   -0.0004
##    360        0.1140             nan     0.3000   -0.0008
##    380        0.1042             nan     0.3000   -0.0009
##    400        0.0973             nan     0.3000   -0.0006
##    420        0.0900             nan     0.3000   -0.0011
##    440        0.0830             nan     0.3000   -0.0006
##    460        0.0777             nan     0.3000   -0.0006
##    480        0.0737             nan     0.3000   -0.0004
##    500        0.0687             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1709             nan     0.3000    0.0612
##      2        1.0923             nan     0.3000    0.0243
##      3        1.0163             nan     0.3000    0.0320
##      4        0.9700             nan     0.3000    0.0125
##      5        0.9453             nan     0.3000    0.0020
##      6        0.9262             nan     0.3000   -0.0027
##      7        0.9030             nan     0.3000    0.0021
##      8        0.8749             nan     0.3000    0.0088
##      9        0.8611             nan     0.3000   -0.0032
##     10        0.8493             nan     0.3000   -0.0043
##     20        0.7499             nan     0.3000   -0.0049
##     40        0.6548             nan     0.3000   -0.0039
##     60        0.5532             nan     0.3000   -0.0050
##     80        0.4931             nan     0.3000   -0.0070
##    100        0.4386             nan     0.3000   -0.0037
##    120        0.3860             nan     0.3000   -0.0034
##    140        0.3418             nan     0.3000   -0.0007
##    160        0.3036             nan     0.3000   -0.0029
##    180        0.2721             nan     0.3000   -0.0021
##    200        0.2391             nan     0.3000   -0.0034
##    220        0.2136             nan     0.3000   -0.0018
##    240        0.1913             nan     0.3000   -0.0024
##    260        0.1755             nan     0.3000   -0.0011
##    280        0.1575             nan     0.3000   -0.0005
##    300        0.1426             nan     0.3000   -0.0006
##    320        0.1303             nan     0.3000   -0.0016
##    340        0.1214             nan     0.3000   -0.0007
##    360        0.1120             nan     0.3000   -0.0007
##    380        0.1032             nan     0.3000   -0.0006
##    400        0.0922             nan     0.3000   -0.0003
##    420        0.0846             nan     0.3000   -0.0001
##    440        0.0773             nan     0.3000   -0.0007
##    460        0.0718             nan     0.3000   -0.0008
##    480        0.0672             nan     0.3000   -0.0004
##    500        0.0606             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1573             nan     0.3000    0.0573
##      2        1.0873             nan     0.3000    0.0261
##      3        1.0338             nan     0.3000    0.0153
##      4        0.9816             nan     0.3000    0.0106
##      5        0.9516             nan     0.3000    0.0044
##      6        0.9265             nan     0.3000    0.0044
##      7        0.9093             nan     0.3000    0.0002
##      8        0.8893             nan     0.3000    0.0027
##      9        0.8755             nan     0.3000   -0.0053
##     10        0.8611             nan     0.3000    0.0005
##     20        0.7550             nan     0.3000   -0.0025
##     40        0.6493             nan     0.3000   -0.0017
##     60        0.5704             nan     0.3000   -0.0009
##     80        0.4946             nan     0.3000   -0.0033
##    100        0.4341             nan     0.3000   -0.0018
##    120        0.4007             nan     0.3000   -0.0038
##    140        0.3595             nan     0.3000   -0.0026
##    160        0.3268             nan     0.3000   -0.0014
##    180        0.2905             nan     0.3000   -0.0037
##    200        0.2505             nan     0.3000   -0.0014
##    220        0.2262             nan     0.3000   -0.0018
##    240        0.2045             nan     0.3000   -0.0021
##    260        0.1850             nan     0.3000   -0.0011
##    280        0.1693             nan     0.3000   -0.0009
##    300        0.1558             nan     0.3000   -0.0009
##    320        0.1446             nan     0.3000   -0.0008
##    340        0.1306             nan     0.3000   -0.0011
##    360        0.1186             nan     0.3000   -0.0017
##    380        0.1092             nan     0.3000   -0.0008
##    400        0.0991             nan     0.3000   -0.0008
##    420        0.0890             nan     0.3000   -0.0011
##    440        0.0804             nan     0.3000   -0.0007
##    460        0.0723             nan     0.3000   -0.0008
##    480        0.0672             nan     0.3000   -0.0001
##    500        0.0594             nan     0.3000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1562             nan     0.5000    0.0658
##      2        1.0906             nan     0.5000    0.0270
##      3        1.0405             nan     0.5000    0.0247
##      4        1.0016             nan     0.5000    0.0149
##      5        0.9838             nan     0.5000   -0.0022
##      6        0.9647             nan     0.5000    0.0020
##      7        0.9440             nan     0.5000    0.0012
##      8        0.9361             nan     0.5000    0.0009
##      9        0.9213             nan     0.5000   -0.0002
##     10        0.9084             nan     0.5000   -0.0014
##     20        0.8412             nan     0.5000   -0.0044
##     40        0.7815             nan     0.5000   -0.0042
##     60        0.7410             nan     0.5000   -0.0040
##     80        0.7161             nan     0.5000   -0.0034
##    100        0.7027             nan     0.5000   -0.0012
##    120        0.6908             nan     0.5000   -0.0039
##    140        0.6715             nan     0.5000   -0.0030
##    160        0.6587             nan     0.5000   -0.0068
##    180        0.6480             nan     0.5000   -0.0075
##    200        0.6210             nan     0.5000   -0.0057
##    220        0.6091             nan     0.5000   -0.0004
##    240        0.5924             nan     0.5000    0.0013
##    260        0.5835             nan     0.5000   -0.0113
##    280        0.5717             nan     0.5000   -0.0042
##    300        0.5671             nan     0.5000   -0.0073
##    320        0.5489             nan     0.5000   -0.0079
##    340        0.5418             nan     0.5000   -0.0020
##    360        0.5300             nan     0.5000   -0.0053
##    380        0.5190             nan     0.5000   -0.0031
##    400        0.5189             nan     0.5000   -0.0049
##    420        0.5128             nan     0.5000   -0.0062
##    440        0.5075             nan     0.5000   -0.0094
##    460        0.4975             nan     0.5000   -0.0034
##    480        0.4927             nan     0.5000   -0.0056
##    500        0.4852             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1586             nan     0.5000    0.0501
##      2        1.1011             nan     0.5000    0.0199
##      3        1.0487             nan     0.5000    0.0219
##      4        1.0138             nan     0.5000    0.0147
##      5        0.9768             nan     0.5000    0.0129
##      6        0.9673             nan     0.5000   -0.0043
##      7        0.9569             nan     0.5000   -0.0026
##      8        0.9525             nan     0.5000   -0.0115
##      9        0.9337             nan     0.5000   -0.0003
##     10        0.9305             nan     0.5000   -0.0065
##     20        0.8526             nan     0.5000   -0.0009
##     40        0.7934             nan     0.5000   -0.0043
##     60        0.7567             nan     0.5000   -0.0030
##     80        0.7393             nan     0.5000   -0.0041
##    100        0.7067             nan     0.5000    0.0020
##    120        0.6904             nan     0.5000   -0.0033
##    140        0.6814             nan     0.5000   -0.0039
##    160        0.6736             nan     0.5000   -0.0028
##    180        0.6543             nan     0.5000   -0.0083
##    200        0.6415             nan     0.5000   -0.0047
##    220        0.6237             nan     0.5000   -0.0048
##    240        0.6202             nan     0.5000   -0.0142
##    260        0.6010             nan     0.5000    0.0001
##    280        0.5918             nan     0.5000   -0.0072
##    300        0.5757             nan     0.5000   -0.0036
##    320        0.5750             nan     0.5000   -0.0038
##    340        0.5633             nan     0.5000   -0.0058
##    360        0.5525             nan     0.5000   -0.0007
##    380        0.5462             nan     0.5000   -0.0051
##    400        0.5425             nan     0.5000   -0.0033
##    420        0.5417             nan     0.5000   -0.0007
##    440        0.5365             nan     0.5000   -0.0070
##    460        0.5275             nan     0.5000   -0.0046
##    480        0.5200             nan     0.5000   -0.0070
##    500        0.5001             nan     0.5000   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1694             nan     0.5000    0.0471
##      2        1.0959             nan     0.5000    0.0144
##      3        1.0441             nan     0.5000    0.0187
##      4        0.9983             nan     0.5000    0.0149
##      5        0.9681             nan     0.5000    0.0117
##      6        0.9484             nan     0.5000    0.0082
##      7        0.9469             nan     0.5000   -0.0114
##      8        0.9272             nan     0.5000    0.0067
##      9        0.9220             nan     0.5000   -0.0089
##     10        0.9160             nan     0.5000   -0.0018
##     20        0.8587             nan     0.5000   -0.0063
##     40        0.8094             nan     0.5000   -0.0072
##     60        0.7832             nan     0.5000   -0.0082
##     80        0.7422             nan     0.5000   -0.0025
##    100        0.7201             nan     0.5000   -0.0035
##    120        0.7019             nan     0.5000   -0.0057
##    140        0.6928             nan     0.5000   -0.0008
##    160        0.6752             nan     0.5000   -0.0028
##    180        0.6555             nan     0.5000   -0.0030
##    200        0.6397             nan     0.5000   -0.0023
##    220        0.6297             nan     0.5000   -0.0034
##    240        0.6112             nan     0.5000   -0.0030
##    260        0.6116             nan     0.5000   -0.0026
##    280        0.5908             nan     0.5000   -0.0034
##    300        0.5820             nan     0.5000   -0.0025
##    320        0.5674             nan     0.5000   -0.0019
##    340        0.5618             nan     0.5000   -0.0050
##    360        0.5579             nan     0.5000   -0.0004
##    380        0.5491             nan     0.5000   -0.0036
##    400        0.5388             nan     0.5000   -0.0040
##    420        0.5349             nan     0.5000   -0.0047
##    440        0.5289             nan     0.5000   -0.0135
##    460        0.5108             nan     0.5000    0.0004
##    480        0.5051             nan     0.5000   -0.0013
##    500        0.4998             nan     0.5000   -0.0053
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1272             nan     0.5000    0.0862
##      2        1.0330             nan     0.5000    0.0373
##      3        0.9774             nan     0.5000    0.0145
##      4        0.9537             nan     0.5000    0.0018
##      5        0.9293             nan     0.5000    0.0046
##      6        0.9058             nan     0.5000   -0.0003
##      7        0.8943             nan     0.5000   -0.0076
##      8        0.8838             nan     0.5000   -0.0053
##      9        0.8746             nan     0.5000   -0.0060
##     10        0.8578             nan     0.5000    0.0033
##     20        0.7910             nan     0.5000   -0.0091
##     40        0.6926             nan     0.5000   -0.0117
##     60           inf             nan     0.5000   -0.0075
##     80           inf             nan     0.5000   -0.0223
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000   -0.0035
##    160           inf             nan     0.5000   -0.0080
##    180           inf             nan     0.5000   -0.0023
##    200           inf             nan     0.5000   -0.0011
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1207             nan     0.5000    0.0690
##      2        1.0506             nan     0.5000    0.0153
##      3        0.9845             nan     0.5000    0.0136
##      4        0.9430             nan     0.5000   -0.0027
##      5        0.9219             nan     0.5000   -0.0028
##      6        0.9162             nan     0.5000   -0.0116
##      7        0.9031             nan     0.5000   -0.0001
##      8        0.8834             nan     0.5000    0.0059
##      9        0.8693             nan     0.5000   -0.0034
##     10        0.8653             nan     0.5000   -0.0093
##     20        0.7892             nan     0.5000   -0.0114
##     40        0.7015             nan     0.5000   -0.0012
##     60        0.6319             nan     0.5000   -0.0034
##     80        0.5899             nan     0.5000   -0.0110
##    100        0.5064             nan     0.5000   -0.0051
##    120        0.4585             nan     0.5000   -0.0095
##    140        0.4048             nan     0.5000   -0.0038
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1243             nan     0.5000    0.0619
##      2        1.0308             nan     0.5000    0.0322
##      3        0.9870             nan     0.5000    0.0107
##      4        0.9663             nan     0.5000   -0.0123
##      5        0.9294             nan     0.5000   -0.0016
##      6        0.9052             nan     0.5000    0.0031
##      7        0.8886             nan     0.5000    0.0010
##      8        0.8702             nan     0.5000   -0.0043
##      9        0.8605             nan     0.5000   -0.0062
##     10        0.8530             nan     0.5000   -0.0049
##     20        0.7821             nan     0.5000   -0.0132
##     40        0.6752             nan     0.5000   -0.0023
##     60        0.5936             nan     0.5000   -0.0006
##     80        0.5339             nan     0.5000   -0.0001
##    100        0.4934             nan     0.5000   -0.0077
##    120        0.4728             nan     0.5000   -0.0102
##    140        0.4122             nan     0.5000   -0.0065
##    160        0.3677             nan     0.5000   -0.0040
##    180        0.3245             nan     0.5000   -0.0034
##    200        0.2864             nan     0.5000   -0.0020
##    220        0.2597             nan     0.5000   -0.0048
##    240        0.2352             nan     0.5000   -0.0024
##    260        0.2147             nan     0.5000   -0.0010
##    280        0.1908             nan     0.5000   -0.0042
##    300        0.1763             nan     0.5000   -0.0015
##    320        0.1632             nan     0.5000   -0.0028
##    340        0.1507             nan     0.5000   -0.0013
##    360        0.1394             nan     0.5000   -0.0013
##    380        0.1314             nan     0.5000   -0.0032
##    400        0.1204             nan     0.5000   -0.0011
##    420        0.1113             nan     0.5000   -0.0011
##    440        0.1031             nan     0.5000   -0.0019
##    460        0.0947             nan     0.5000   -0.0006
##    480        0.0846             nan     0.5000   -0.0008
##    500        0.0784             nan     0.5000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0995             nan     0.5000    0.0840
##      2        1.0316             nan     0.5000    0.0165
##      3        0.9689             nan     0.5000    0.0141
##      4        0.9382             nan     0.5000   -0.0013
##      5        0.8976             nan     0.5000    0.0122
##      6        0.8663             nan     0.5000   -0.0132
##      7        0.8431             nan     0.5000    0.0003
##      8        0.8293             nan     0.5000   -0.0113
##      9        0.8182             nan     0.5000   -0.0069
##     10        0.8241             nan     0.5000   -0.0233
##     20        0.7113             nan     0.5000   -0.0111
##     40        0.5467             nan     0.5000   -0.0031
##     60           inf             nan     0.5000   -0.0111
##     80           inf             nan     0.5000   -0.0055
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1195             nan     0.5000    0.0877
##      2        1.0171             nan     0.5000    0.0392
##      3        0.9644             nan     0.5000    0.0154
##      4        0.9371             nan     0.5000   -0.0019
##      5        0.8996             nan     0.5000   -0.0048
##      6        0.8706             nan     0.5000   -0.0013
##      7        0.8626             nan     0.5000   -0.0088
##      8        0.8602             nan     0.5000   -0.0236
##      9        0.8391             nan     0.5000   -0.0068
##     10        0.8244             nan     0.5000   -0.0108
##     20        0.6968             nan     0.5000   -0.0167
##     40        0.5663             nan     0.5000   -0.0069
##     60        0.4628             nan     0.5000   -0.0045
##     80        0.3742             nan     0.5000   -0.0000
##    100        0.3037             nan     0.5000   -0.0080
##    120        0.2368             nan     0.5000   -0.0036
##    140        0.2017             nan     0.5000   -0.0019
##    160        0.1630             nan     0.5000   -0.0003
##    180        0.1378             nan     0.5000   -0.0017
##    200        0.1171             nan     0.5000   -0.0002
##    220        0.1056             nan     0.5000   -0.0038
##    240        0.0915             nan     0.5000   -0.0016
##    260        0.0780             nan     0.5000   -0.0013
##    280        0.0682             nan     0.5000   -0.0004
##    300        0.0600             nan     0.5000   -0.0012
##    320        0.0532             nan     0.5000   -0.0007
##    340        0.0470             nan     0.5000   -0.0011
##    360        0.0408             nan     0.5000   -0.0000
##    380        0.0377             nan     0.5000   -0.0006
##    400        0.0324             nan     0.5000   -0.0005
##    420        0.0285             nan     0.5000   -0.0003
##    440        0.0258             nan     0.5000   -0.0002
##    460        0.0231             nan     0.5000   -0.0001
##    480        0.0197             nan     0.5000   -0.0004
##    500        0.0170             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0806             nan     0.5000    0.0817
##      2        0.9938             nan     0.5000    0.0213
##      3        0.9474             nan     0.5000    0.0097
##      4        0.9071             nan     0.5000    0.0013
##      5        0.8885             nan     0.5000   -0.0119
##      6        0.8669             nan     0.5000   -0.0068
##      7        0.8440             nan     0.5000   -0.0053
##      8        0.8272             nan     0.5000   -0.0035
##      9        0.8151             nan     0.5000   -0.0072
##     10        0.8072             nan     0.5000   -0.0033
##     20        0.7283             nan     0.5000   -0.0084
##     40        0.5781             nan     0.5000   -0.0026
##     60        0.4629             nan     0.5000   -0.0072
##     80        0.3848             nan     0.5000   -0.0037
##    100        0.3064             nan     0.5000    0.0001
##    120        0.2724             nan     0.5000   -0.0008
##    140      223.3358             nan     0.5000    0.5598
##    160      223.3082             nan     0.5000   -0.0008
##    180      223.2906             nan     0.5000   -0.0013
##    200      223.2688             nan     0.5000   -0.0017
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1140             nan     1.0000    0.0923
##      2        1.0563             nan     1.0000    0.0201
##      3        0.9951             nan     1.0000    0.0143
##      4        0.9585             nan     1.0000    0.0127
##      5        0.9565             nan     1.0000   -0.0275
##      6        0.9805             nan     1.0000   -0.0468
##      7        1.0111             nan     1.0000   -0.0465
##      8        1.1297             nan     1.0000   -0.0182
##      9        5.6244             nan     1.0000   -3.0503
##     10        5.5970             nan     1.0000    0.0128
##     20        5.5434             nan     1.0000    0.0016
##     40        5.3270             nan     1.0000   -0.0156
##     60        5.3159             nan     1.0000   -0.0224
##     80        5.2456             nan     1.0000   -0.0158
##    100 6517510314578.6992             nan     1.0000   -0.0020
##    120 6517510314579.3672             nan     1.0000    0.0007
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1239             nan     1.0000    0.0741
##      2        1.0584             nan     1.0000    0.0211
##      3        1.0036             nan     1.0000    0.0179
##      4        0.9606             nan     1.0000    0.0177
##      5        0.9613             nan     1.0000   -0.0149
##      6        0.9647             nan     1.0000   -0.0168
##      7        0.9725             nan     1.0000   -0.0246
##      8        0.9461             nan     1.0000    0.0095
##      9        0.9373             nan     1.0000   -0.0099
##     10        0.9403             nan     1.0000   -0.0218
##     20   163438.9410             nan     1.0000   -0.0156
##     40   163438.9014             nan     1.0000   -0.0219
##     60   163438.9025             nan     1.0000   -0.0091
##     80   163438.9653             nan     1.0000   -0.1053
##    100   163454.1726             nan     1.0000    0.0018
##    120   163453.5858             nan     1.0000   -0.0033
##    140   163453.5845             nan     1.0000   -0.0030
##    160   163453.5415             nan     1.0000   -0.0337
##    180   163453.5737             nan     1.0000   -0.0041
##    200   163453.5668             nan     1.0000   -0.0119
##    220   163453.5275             nan     1.0000    0.0062
##    240   163703.9984             nan     1.0000    0.0075
##    260   163703.9950             nan     1.0000   -0.0059
##    280   163703.9865             nan     1.0000   -0.0579
##    300   163703.9545             nan     1.0000    0.0001
##    320   163703.9532             nan     1.0000    0.0012
##    340   226897.9306             nan     1.0000   -0.0001
##    360   226897.9369             nan     1.0000    0.0000
##    380   226897.9479             nan     1.0000    0.0007
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1213             nan     1.0000    0.0892
##      2        1.0611             nan     1.0000    0.0191
##      3        1.0125             nan     1.0000    0.0174
##      4        0.9968             nan     1.0000   -0.0085
##      5        1.0001             nan     1.0000   -0.0270
##      6        0.9953             nan     1.0000   -0.0224
##      7        0.9554             nan     1.0000    0.0060
##      8        0.9570             nan     1.0000   -0.0214
##      9        0.9385             nan     1.0000    0.0017
##     10        0.9372             nan     1.0000   -0.0150
##     20        1.0574             nan     1.0000   -0.0254
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0571             nan     1.0000    0.0973
##      2        0.9821             nan     1.0000   -0.0000
##      3        0.9690             nan     1.0000   -0.0211
##      4        0.9702             nan     1.0000   -0.0285
##      5        0.9678             nan     1.0000   -0.0273
##      6        0.9832             nan     1.0000   -0.0365
##      7        0.9643             nan     1.0000   -0.0132
##      8        0.9482             nan     1.0000   -0.0219
##      9        0.9621             nan     1.0000   -0.0448
##     10        0.9203             nan     1.0000    0.0112
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0484             nan     1.0000    0.1103
##      2        0.9828             nan     1.0000   -0.0031
##      3        0.9944             nan     1.0000   -0.0598
##      4        0.9420             nan     1.0000    0.0215
##      5        1.0143             nan     1.0000   -0.1116
##      6        1.1866             nan     1.0000   -0.2061
##      7           inf             nan     1.0000      -inf
##      8           inf             nan     1.0000       nan
##      9           inf             nan     1.0000       nan
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0658             nan     1.0000    0.1032
##      2        0.9735             nan     1.0000    0.0357
##      3        0.9581             nan     1.0000   -0.0183
##      4        1.0771             nan     1.0000   -0.1233
##      5        1.0456             nan     1.0000   -0.0017
##      6        1.0100             nan     1.0000    0.0038
##      7           inf             nan     1.0000      -inf
##      8           inf             nan     1.0000       nan
##      9           inf             nan     1.0000       nan
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0203             nan     1.0000    0.1248
##      2        0.9504             nan     1.0000   -0.0101
##      3        0.9459             nan     1.0000   -0.0352
##      4        0.9435             nan     1.0000   -0.0457
##      5        0.9158             nan     1.0000   -0.0238
##      6        0.8850             nan     1.0000   -0.0222
##      7        0.9508             nan     1.0000   -0.0916
##      8        0.9396             nan     1.0000   -0.0430
##      9        0.8694             nan     1.0000    0.0025
##     10        0.8474             nan     1.0000   -0.0204
##     20        1.4553             nan     1.0000   -0.0191
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0432             nan     1.0000    0.0964
##      2        0.9659             nan     1.0000    0.0111
##      3        0.9517             nan     1.0000   -0.0285
##      4        0.9410             nan     1.0000   -0.0286
##      5        0.8997             nan     1.0000   -0.0067
##      6        0.8988             nan     1.0000   -0.0322
##      7        0.8818             nan     1.0000   -0.0239
##      8        0.8423             nan     1.0000   -0.0025
##      9        0.8542             nan     1.0000   -0.0498
##     10        0.8266             nan     1.0000   -0.0035
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0209             nan     1.0000    0.1231
##      2        0.9496             nan     1.0000   -0.0088
##      3        0.9186             nan     1.0000   -0.0247
##      4        0.9252             nan     1.0000   -0.0345
##      5        0.9424             nan     1.0000   -0.0556
##      6        1.1138             nan     1.0000   -0.2296
##      7        1.0930             nan     1.0000   -0.0307
##      8        3.3595             nan     1.0000   -1.9786
##      9        4.0013             nan     1.0000   -0.5752
##     10   191267.1195             nan     1.0000 -95493.4622
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0001
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0001
##      5        1.2915             nan     0.0010    0.0001
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2860             nan     0.0010    0.0001
##     40        1.2792             nan     0.0010    0.0001
##     60        1.2724             nan     0.0010    0.0002
##     80        1.2659             nan     0.0010    0.0001
##    100        1.2595             nan     0.0010    0.0001
##    120        1.2535             nan     0.0010    0.0001
##    140        1.2479             nan     0.0010    0.0001
##    160        1.2424             nan     0.0010    0.0001
##    180        1.2369             nan     0.0010    0.0001
##    200        1.2318             nan     0.0010    0.0001
##    220        1.2267             nan     0.0010    0.0001
##    240        1.2218             nan     0.0010    0.0001
##    260        1.2171             nan     0.0010    0.0001
##    280        1.2125             nan     0.0010    0.0001
##    300        1.2081             nan     0.0010    0.0001
##    320        1.2038             nan     0.0010    0.0001
##    340        1.1996             nan     0.0010    0.0001
##    360        1.1955             nan     0.0010    0.0001
##    380        1.1916             nan     0.0010    0.0001
##    400        1.1879             nan     0.0010    0.0001
##    420        1.1842             nan     0.0010    0.0001
##    440        1.1804             nan     0.0010    0.0001
##    460        1.1767             nan     0.0010    0.0001
##    480        1.1733             nan     0.0010    0.0001
##    500        1.1697             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0001
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0001
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2794             nan     0.0010    0.0002
##     60        1.2729             nan     0.0010    0.0002
##     80        1.2667             nan     0.0010    0.0001
##    100        1.2605             nan     0.0010    0.0001
##    120        1.2545             nan     0.0010    0.0001
##    140        1.2488             nan     0.0010    0.0001
##    160        1.2433             nan     0.0010    0.0001
##    180        1.2379             nan     0.0010    0.0001
##    200        1.2326             nan     0.0010    0.0001
##    220        1.2275             nan     0.0010    0.0001
##    240        1.2227             nan     0.0010    0.0001
##    260        1.2179             nan     0.0010    0.0001
##    280        1.2132             nan     0.0010    0.0001
##    300        1.2086             nan     0.0010    0.0001
##    320        1.2042             nan     0.0010    0.0001
##    340        1.2001             nan     0.0010    0.0001
##    360        1.1959             nan     0.0010    0.0001
##    380        1.1920             nan     0.0010    0.0001
##    400        1.1881             nan     0.0010    0.0001
##    420        1.1845             nan     0.0010    0.0001
##    440        1.1807             nan     0.0010    0.0001
##    460        1.1771             nan     0.0010    0.0001
##    480        1.1734             nan     0.0010    0.0001
##    500        1.1700             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2906             nan     0.0010    0.0001
##      8        1.2903             nan     0.0010    0.0001
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2895             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2792             nan     0.0010    0.0001
##     60        1.2728             nan     0.0010    0.0001
##     80        1.2664             nan     0.0010    0.0001
##    100        1.2600             nan     0.0010    0.0001
##    120        1.2542             nan     0.0010    0.0001
##    140        1.2485             nan     0.0010    0.0001
##    160        1.2431             nan     0.0010    0.0001
##    180        1.2377             nan     0.0010    0.0001
##    200        1.2324             nan     0.0010    0.0001
##    220        1.2273             nan     0.0010    0.0001
##    240        1.2225             nan     0.0010    0.0001
##    260        1.2179             nan     0.0010    0.0001
##    280        1.2132             nan     0.0010    0.0001
##    300        1.2086             nan     0.0010    0.0001
##    320        1.2042             nan     0.0010    0.0001
##    340        1.2000             nan     0.0010    0.0001
##    360        1.1958             nan     0.0010    0.0001
##    380        1.1919             nan     0.0010    0.0001
##    400        1.1880             nan     0.0010    0.0001
##    420        1.1842             nan     0.0010    0.0001
##    440        1.1805             nan     0.0010    0.0000
##    460        1.1768             nan     0.0010    0.0001
##    480        1.1732             nan     0.0010    0.0001
##    500        1.1697             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2839             nan     0.0010    0.0002
##     40        1.2752             nan     0.0010    0.0002
##     60        1.2666             nan     0.0010    0.0002
##     80        1.2587             nan     0.0010    0.0002
##    100        1.2510             nan     0.0010    0.0002
##    120        1.2432             nan     0.0010    0.0002
##    140        1.2359             nan     0.0010    0.0002
##    160        1.2288             nan     0.0010    0.0002
##    180        1.2219             nan     0.0010    0.0002
##    200        1.2148             nan     0.0010    0.0002
##    220        1.2080             nan     0.0010    0.0001
##    240        1.2016             nan     0.0010    0.0001
##    260        1.1953             nan     0.0010    0.0001
##    280        1.1891             nan     0.0010    0.0001
##    300        1.1833             nan     0.0010    0.0001
##    320        1.1774             nan     0.0010    0.0001
##    340        1.1716             nan     0.0010    0.0001
##    360        1.1661             nan     0.0010    0.0001
##    380        1.1607             nan     0.0010    0.0001
##    400        1.1553             nan     0.0010    0.0001
##    420        1.1502             nan     0.0010    0.0001
##    440        1.1451             nan     0.0010    0.0001
##    460        1.1404             nan     0.0010    0.0001
##    480        1.1356             nan     0.0010    0.0001
##    500        1.1311             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2842             nan     0.0010    0.0002
##     40        1.2749             nan     0.0010    0.0002
##     60        1.2663             nan     0.0010    0.0002
##     80        1.2580             nan     0.0010    0.0002
##    100        1.2501             nan     0.0010    0.0002
##    120        1.2424             nan     0.0010    0.0001
##    140        1.2349             nan     0.0010    0.0002
##    160        1.2277             nan     0.0010    0.0001
##    180        1.2208             nan     0.0010    0.0001
##    200        1.2140             nan     0.0010    0.0001
##    220        1.2074             nan     0.0010    0.0002
##    240        1.2010             nan     0.0010    0.0001
##    260        1.1945             nan     0.0010    0.0001
##    280        1.1883             nan     0.0010    0.0001
##    300        1.1823             nan     0.0010    0.0001
##    320        1.1764             nan     0.0010    0.0001
##    340        1.1709             nan     0.0010    0.0001
##    360        1.1654             nan     0.0010    0.0001
##    380        1.1600             nan     0.0010    0.0001
##    400        1.1548             nan     0.0010    0.0001
##    420        1.1497             nan     0.0010    0.0001
##    440        1.1446             nan     0.0010    0.0001
##    460        1.1397             nan     0.0010    0.0001
##    480        1.1350             nan     0.0010    0.0001
##    500        1.1302             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2841             nan     0.0010    0.0002
##     40        1.2752             nan     0.0010    0.0002
##     60        1.2666             nan     0.0010    0.0002
##     80        1.2581             nan     0.0010    0.0002
##    100        1.2501             nan     0.0010    0.0002
##    120        1.2423             nan     0.0010    0.0001
##    140        1.2348             nan     0.0010    0.0001
##    160        1.2274             nan     0.0010    0.0001
##    180        1.2204             nan     0.0010    0.0001
##    200        1.2136             nan     0.0010    0.0002
##    220        1.2068             nan     0.0010    0.0002
##    240        1.2002             nan     0.0010    0.0001
##    260        1.1939             nan     0.0010    0.0001
##    280        1.1878             nan     0.0010    0.0001
##    300        1.1818             nan     0.0010    0.0001
##    320        1.1762             nan     0.0010    0.0001
##    340        1.1706             nan     0.0010    0.0001
##    360        1.1652             nan     0.0010    0.0001
##    380        1.1598             nan     0.0010    0.0001
##    400        1.1545             nan     0.0010    0.0001
##    420        1.1493             nan     0.0010    0.0001
##    440        1.1442             nan     0.0010    0.0001
##    460        1.1396             nan     0.0010    0.0001
##    480        1.1348             nan     0.0010    0.0001
##    500        1.1301             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0003
##      4        1.2913             nan     0.0010    0.0002
##      5        1.2908             nan     0.0010    0.0002
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2897             nan     0.0010    0.0003
##      8        1.2892             nan     0.0010    0.0002
##      9        1.2887             nan     0.0010    0.0002
##     10        1.2881             nan     0.0010    0.0003
##     20        1.2829             nan     0.0010    0.0002
##     40        1.2726             nan     0.0010    0.0002
##     60        1.2627             nan     0.0010    0.0002
##     80        1.2532             nan     0.0010    0.0002
##    100        1.2439             nan     0.0010    0.0002
##    120        1.2350             nan     0.0010    0.0002
##    140        1.2264             nan     0.0010    0.0002
##    160        1.2177             nan     0.0010    0.0002
##    180        1.2096             nan     0.0010    0.0002
##    200        1.2013             nan     0.0010    0.0002
##    220        1.1936             nan     0.0010    0.0002
##    240        1.1862             nan     0.0010    0.0001
##    260        1.1788             nan     0.0010    0.0002
##    280        1.1717             nan     0.0010    0.0001
##    300        1.1649             nan     0.0010    0.0001
##    320        1.1580             nan     0.0010    0.0002
##    340        1.1513             nan     0.0010    0.0001
##    360        1.1450             nan     0.0010    0.0001
##    380        1.1388             nan     0.0010    0.0001
##    400        1.1327             nan     0.0010    0.0001
##    420        1.1269             nan     0.0010    0.0001
##    440        1.1212             nan     0.0010    0.0001
##    460        1.1159             nan     0.0010    0.0001
##    480        1.1103             nan     0.0010    0.0001
##    500        1.1051             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0003
##      7        1.2897             nan     0.0010    0.0002
##      8        1.2892             nan     0.0010    0.0002
##      9        1.2887             nan     0.0010    0.0002
##     10        1.2881             nan     0.0010    0.0003
##     20        1.2828             nan     0.0010    0.0002
##     40        1.2728             nan     0.0010    0.0002
##     60        1.2626             nan     0.0010    0.0002
##     80        1.2532             nan     0.0010    0.0002
##    100        1.2437             nan     0.0010    0.0002
##    120        1.2347             nan     0.0010    0.0002
##    140        1.2262             nan     0.0010    0.0002
##    160        1.2174             nan     0.0010    0.0002
##    180        1.2091             nan     0.0010    0.0002
##    200        1.2012             nan     0.0010    0.0001
##    220        1.1935             nan     0.0010    0.0001
##    240        1.1859             nan     0.0010    0.0001
##    260        1.1787             nan     0.0010    0.0002
##    280        1.1716             nan     0.0010    0.0002
##    300        1.1647             nan     0.0010    0.0001
##    320        1.1580             nan     0.0010    0.0001
##    340        1.1514             nan     0.0010    0.0001
##    360        1.1451             nan     0.0010    0.0001
##    380        1.1391             nan     0.0010    0.0001
##    400        1.1330             nan     0.0010    0.0001
##    420        1.1271             nan     0.0010    0.0001
##    440        1.1214             nan     0.0010    0.0001
##    460        1.1158             nan     0.0010    0.0001
##    480        1.1103             nan     0.0010    0.0001
##    500        1.1050             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0002
##     10        1.2880             nan     0.0010    0.0003
##     20        1.2831             nan     0.0010    0.0002
##     40        1.2730             nan     0.0010    0.0002
##     60        1.2630             nan     0.0010    0.0002
##     80        1.2532             nan     0.0010    0.0002
##    100        1.2440             nan     0.0010    0.0002
##    120        1.2348             nan     0.0010    0.0002
##    140        1.2262             nan     0.0010    0.0002
##    160        1.2176             nan     0.0010    0.0002
##    180        1.2096             nan     0.0010    0.0002
##    200        1.2016             nan     0.0010    0.0002
##    220        1.1941             nan     0.0010    0.0002
##    240        1.1865             nan     0.0010    0.0002
##    260        1.1791             nan     0.0010    0.0002
##    280        1.1722             nan     0.0010    0.0001
##    300        1.1652             nan     0.0010    0.0001
##    320        1.1585             nan     0.0010    0.0001
##    340        1.1519             nan     0.0010    0.0001
##    360        1.1455             nan     0.0010    0.0001
##    380        1.1394             nan     0.0010    0.0001
##    400        1.1336             nan     0.0010    0.0001
##    420        1.1278             nan     0.0010    0.0001
##    440        1.1223             nan     0.0010    0.0001
##    460        1.1166             nan     0.0010    0.0001
##    480        1.1112             nan     0.0010    0.0001
##    500        1.1057             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2623             nan     0.1000    0.0159
##      2        1.2328             nan     0.1000    0.0127
##      3        1.2086             nan     0.1000    0.0119
##      4        1.1878             nan     0.1000    0.0088
##      5        1.1702             nan     0.1000    0.0072
##      6        1.1550             nan     0.1000    0.0048
##      7        1.1396             nan     0.1000    0.0053
##      8        1.1260             nan     0.1000    0.0054
##      9        1.1142             nan     0.1000    0.0042
##     10        1.1007             nan     0.1000    0.0052
##     20        1.0120             nan     0.1000    0.0018
##     40        0.9325             nan     0.1000    0.0020
##     60        0.8919             nan     0.1000   -0.0010
##     80        0.8690             nan     0.1000   -0.0004
##    100        0.8491             nan     0.1000   -0.0007
##    120        0.8343             nan     0.1000   -0.0009
##    140        0.8215             nan     0.1000   -0.0009
##    160        0.8128             nan     0.1000   -0.0022
##    180        0.8051             nan     0.1000   -0.0014
##    200        0.7970             nan     0.1000   -0.0015
##    220        0.7893             nan     0.1000   -0.0010
##    240        0.7825             nan     0.1000   -0.0007
##    260        0.7751             nan     0.1000   -0.0011
##    280        0.7692             nan     0.1000   -0.0005
##    300        0.7641             nan     0.1000   -0.0010
##    320        0.7604             nan     0.1000   -0.0017
##    340        0.7550             nan     0.1000   -0.0005
##    360        0.7499             nan     0.1000   -0.0006
##    380        0.7451             nan     0.1000   -0.0005
##    400        0.7391             nan     0.1000    0.0001
##    420        0.7340             nan     0.1000   -0.0009
##    440        0.7298             nan     0.1000   -0.0008
##    460        0.7250             nan     0.1000   -0.0005
##    480        0.7206             nan     0.1000   -0.0007
##    500        0.7171             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2545             nan     0.1000    0.0168
##      2        1.2272             nan     0.1000    0.0125
##      3        1.2042             nan     0.1000    0.0107
##      4        1.1843             nan     0.1000    0.0094
##      5        1.1650             nan     0.1000    0.0065
##      6        1.1478             nan     0.1000    0.0071
##      7        1.1341             nan     0.1000    0.0047
##      8        1.1203             nan     0.1000    0.0053
##      9        1.1080             nan     0.1000    0.0055
##     10        1.0946             nan     0.1000    0.0042
##     20        1.0107             nan     0.1000    0.0023
##     40        0.9303             nan     0.1000    0.0005
##     60        0.8898             nan     0.1000   -0.0004
##     80        0.8635             nan     0.1000   -0.0015
##    100        0.8453             nan     0.1000   -0.0006
##    120        0.8283             nan     0.1000   -0.0014
##    140        0.8167             nan     0.1000   -0.0009
##    160        0.8040             nan     0.1000   -0.0007
##    180        0.7969             nan     0.1000   -0.0006
##    200        0.7896             nan     0.1000   -0.0013
##    220        0.7804             nan     0.1000   -0.0006
##    240        0.7748             nan     0.1000   -0.0013
##    260        0.7661             nan     0.1000   -0.0005
##    280        0.7590             nan     0.1000   -0.0006
##    300        0.7548             nan     0.1000   -0.0013
##    320        0.7479             nan     0.1000   -0.0009
##    340        0.7450             nan     0.1000   -0.0008
##    360        0.7367             nan     0.1000   -0.0007
##    380        0.7307             nan     0.1000   -0.0007
##    400        0.7262             nan     0.1000   -0.0012
##    420        0.7215             nan     0.1000   -0.0007
##    440        0.7170             nan     0.1000   -0.0016
##    460        0.7133             nan     0.1000   -0.0008
##    480        0.7103             nan     0.1000   -0.0002
##    500        0.7052             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2536             nan     0.1000    0.0156
##      2        1.2255             nan     0.1000    0.0125
##      3        1.2049             nan     0.1000    0.0110
##      4        1.1851             nan     0.1000    0.0075
##      5        1.1677             nan     0.1000    0.0063
##      6        1.1510             nan     0.1000    0.0054
##      7        1.1371             nan     0.1000    0.0039
##      8        1.1263             nan     0.1000    0.0033
##      9        1.1103             nan     0.1000    0.0067
##     10        1.0978             nan     0.1000    0.0052
##     20        1.0128             nan     0.1000    0.0011
##     40        0.9261             nan     0.1000    0.0001
##     60        0.8876             nan     0.1000    0.0000
##     80        0.8625             nan     0.1000   -0.0002
##    100        0.8453             nan     0.1000   -0.0014
##    120        0.8277             nan     0.1000   -0.0004
##    140        0.8175             nan     0.1000   -0.0017
##    160        0.8079             nan     0.1000   -0.0010
##    180        0.7998             nan     0.1000   -0.0009
##    200        0.7925             nan     0.1000   -0.0016
##    220        0.7865             nan     0.1000   -0.0005
##    240        0.7808             nan     0.1000   -0.0006
##    260        0.7741             nan     0.1000   -0.0005
##    280        0.7672             nan     0.1000   -0.0008
##    300        0.7595             nan     0.1000   -0.0007
##    320        0.7553             nan     0.1000   -0.0033
##    340        0.7509             nan     0.1000   -0.0011
##    360        0.7458             nan     0.1000   -0.0008
##    380        0.7411             nan     0.1000   -0.0007
##    400        0.7375             nan     0.1000   -0.0019
##    420        0.7323             nan     0.1000   -0.0004
##    440        0.7289             nan     0.1000   -0.0010
##    460        0.7245             nan     0.1000   -0.0002
##    480        0.7196             nan     0.1000   -0.0010
##    500        0.7169             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2511             nan     0.1000    0.0185
##      2        1.2125             nan     0.1000    0.0157
##      3        1.1773             nan     0.1000    0.0158
##      4        1.1463             nan     0.1000    0.0151
##      5        1.1266             nan     0.1000    0.0079
##      6        1.1072             nan     0.1000    0.0074
##      7        1.0844             nan     0.1000    0.0085
##      8        1.0650             nan     0.1000    0.0066
##      9        1.0496             nan     0.1000    0.0055
##     10        1.0363             nan     0.1000    0.0043
##     20        0.9489             nan     0.1000   -0.0011
##     40        0.8606             nan     0.1000   -0.0006
##     60        0.8148             nan     0.1000   -0.0001
##     80        0.7807             nan     0.1000   -0.0013
##    100        0.7449             nan     0.1000   -0.0001
##    120        0.7222             nan     0.1000    0.0002
##    140        0.7012             nan     0.1000   -0.0023
##    160        0.6815             nan     0.1000   -0.0004
##    180        0.6654             nan     0.1000   -0.0007
##    200        0.6467             nan     0.1000   -0.0010
##    220        0.6270             nan     0.1000   -0.0011
##    240        0.6133             nan     0.1000   -0.0008
##    260        0.6008             nan     0.1000   -0.0014
##    280        0.5837             nan     0.1000   -0.0006
##    300        0.5692             nan     0.1000   -0.0015
##    320        0.5532             nan     0.1000   -0.0010
##    340        0.5399             nan     0.1000   -0.0006
##    360        0.5268             nan     0.1000   -0.0006
##    380        0.5145             nan     0.1000   -0.0008
##    400        0.5035             nan     0.1000   -0.0006
##    420        0.4920             nan     0.1000   -0.0010
##    440        0.4847             nan     0.1000   -0.0012
##    460        0.4750             nan     0.1000   -0.0007
##    480        0.4633             nan     0.1000   -0.0007
##    500        0.4530             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2491             nan     0.1000    0.0212
##      2        1.2102             nan     0.1000    0.0176
##      3        1.1794             nan     0.1000    0.0146
##      4        1.1545             nan     0.1000    0.0097
##      5        1.1270             nan     0.1000    0.0134
##      6        1.1066             nan     0.1000    0.0078
##      7        1.0850             nan     0.1000    0.0084
##      8        1.0696             nan     0.1000    0.0056
##      9        1.0509             nan     0.1000    0.0046
##     10        1.0315             nan     0.1000    0.0063
##     20        0.9470             nan     0.1000    0.0002
##     40        0.8571             nan     0.1000    0.0002
##     60        0.8118             nan     0.1000   -0.0001
##     80        0.7803             nan     0.1000   -0.0010
##    100        0.7477             nan     0.1000   -0.0014
##    120        0.7268             nan     0.1000   -0.0014
##    140        0.7033             nan     0.1000   -0.0022
##    160        0.6853             nan     0.1000   -0.0008
##    180        0.6688             nan     0.1000   -0.0020
##    200        0.6500             nan     0.1000   -0.0009
##    220        0.6338             nan     0.1000   -0.0016
##    240        0.6119             nan     0.1000   -0.0004
##    260        0.5975             nan     0.1000   -0.0009
##    280        0.5828             nan     0.1000   -0.0010
##    300        0.5699             nan     0.1000   -0.0010
##    320        0.5533             nan     0.1000   -0.0006
##    340        0.5346             nan     0.1000   -0.0003
##    360        0.5237             nan     0.1000   -0.0004
##    380        0.5115             nan     0.1000   -0.0011
##    400        0.5000             nan     0.1000   -0.0009
##    420        0.4898             nan     0.1000   -0.0010
##    440        0.4772             nan     0.1000   -0.0005
##    460        0.4684             nan     0.1000   -0.0015
##    480        0.4556             nan     0.1000   -0.0006
##    500        0.4464             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2466             nan     0.1000    0.0190
##      2        1.2124             nan     0.1000    0.0138
##      3        1.1825             nan     0.1000    0.0118
##      4        1.1532             nan     0.1000    0.0126
##      5        1.1304             nan     0.1000    0.0094
##      6        1.1121             nan     0.1000    0.0078
##      7        1.0929             nan     0.1000    0.0078
##      8        1.0765             nan     0.1000    0.0068
##      9        1.0610             nan     0.1000    0.0077
##     10        1.0456             nan     0.1000    0.0047
##     20        0.9515             nan     0.1000    0.0019
##     40        0.8592             nan     0.1000   -0.0002
##     60        0.8077             nan     0.1000   -0.0014
##     80        0.7789             nan     0.1000   -0.0013
##    100        0.7527             nan     0.1000   -0.0006
##    120        0.7253             nan     0.1000   -0.0015
##    140        0.7033             nan     0.1000   -0.0010
##    160        0.6865             nan     0.1000   -0.0015
##    180        0.6647             nan     0.1000   -0.0011
##    200        0.6474             nan     0.1000   -0.0012
##    220        0.6278             nan     0.1000   -0.0012
##    240        0.6123             nan     0.1000   -0.0011
##    260        0.5992             nan     0.1000   -0.0009
##    280        0.5800             nan     0.1000   -0.0019
##    300        0.5653             nan     0.1000   -0.0013
##    320        0.5533             nan     0.1000   -0.0006
##    340        0.5406             nan     0.1000   -0.0020
##    360        0.5282             nan     0.1000   -0.0004
##    380        0.5149             nan     0.1000   -0.0009
##    400        0.5011             nan     0.1000   -0.0012
##    420        0.4904             nan     0.1000   -0.0022
##    440        0.4774             nan     0.1000   -0.0007
##    460        0.4692             nan     0.1000   -0.0000
##    480        0.4604             nan     0.1000   -0.0014
##    500        0.4499             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2463             nan     0.1000    0.0211
##      2        1.2064             nan     0.1000    0.0191
##      3        1.1720             nan     0.1000    0.0168
##      4        1.1378             nan     0.1000    0.0132
##      5        1.1111             nan     0.1000    0.0098
##      6        1.0828             nan     0.1000    0.0102
##      7        1.0595             nan     0.1000    0.0066
##      8        1.0401             nan     0.1000    0.0056
##      9        1.0212             nan     0.1000    0.0046
##     10        1.0042             nan     0.1000    0.0054
##     20        0.9028             nan     0.1000    0.0002
##     40        0.8074             nan     0.1000   -0.0007
##     60        0.7520             nan     0.1000   -0.0013
##     80        0.7119             nan     0.1000   -0.0018
##    100        0.6729             nan     0.1000   -0.0012
##    120        0.6389             nan     0.1000   -0.0011
##    140        0.6029             nan     0.1000   -0.0015
##    160        0.5708             nan     0.1000   -0.0017
##    180        0.5354             nan     0.1000   -0.0017
##    200        0.5128             nan     0.1000   -0.0019
##    220        0.4931             nan     0.1000   -0.0014
##    240        0.4729             nan     0.1000   -0.0010
##    260        0.4524             nan     0.1000   -0.0013
##    280        0.4350             nan     0.1000   -0.0007
##    300        0.4172             nan     0.1000   -0.0013
##    320        0.4027             nan     0.1000   -0.0016
##    340        0.3857             nan     0.1000   -0.0000
##    360        0.3710             nan     0.1000   -0.0005
##    380        0.3549             nan     0.1000   -0.0010
##    400        0.3412             nan     0.1000   -0.0016
##    420        0.3304             nan     0.1000   -0.0010
##    440        0.3193             nan     0.1000   -0.0004
##    460        0.3071             nan     0.1000   -0.0001
##    480        0.2975             nan     0.1000   -0.0008
##    500        0.2847             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2443             nan     0.1000    0.0232
##      2        1.2026             nan     0.1000    0.0194
##      3        1.1625             nan     0.1000    0.0156
##      4        1.1303             nan     0.1000    0.0145
##      5        1.1020             nan     0.1000    0.0124
##      6        1.0746             nan     0.1000    0.0085
##      7        1.0546             nan     0.1000    0.0080
##      8        1.0349             nan     0.1000    0.0081
##      9        1.0171             nan     0.1000    0.0075
##     10        1.0027             nan     0.1000    0.0043
##     20        0.9040             nan     0.1000   -0.0005
##     40        0.8156             nan     0.1000   -0.0004
##     60        0.7624             nan     0.1000   -0.0018
##     80        0.7121             nan     0.1000   -0.0008
##    100        0.6722             nan     0.1000   -0.0019
##    120        0.6370             nan     0.1000   -0.0012
##    140        0.6026             nan     0.1000   -0.0027
##    160        0.5709             nan     0.1000   -0.0012
##    180        0.5420             nan     0.1000   -0.0012
##    200        0.5224             nan     0.1000   -0.0008
##    220        0.4959             nan     0.1000   -0.0009
##    240        0.4768             nan     0.1000   -0.0009
##    260        0.4565             nan     0.1000   -0.0009
##    280        0.4400             nan     0.1000   -0.0011
##    300        0.4201             nan     0.1000   -0.0006
##    320        0.4017             nan     0.1000   -0.0011
##    340        0.3864             nan     0.1000   -0.0010
##    360        0.3704             nan     0.1000   -0.0008
##    380        0.3584             nan     0.1000   -0.0013
##    400        0.3434             nan     0.1000   -0.0007
##    420        0.3315             nan     0.1000   -0.0010
##    440        0.3192             nan     0.1000   -0.0006
##    460        0.3063             nan     0.1000   -0.0003
##    480        0.2951             nan     0.1000   -0.0006
##    500        0.2864             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2413             nan     0.1000    0.0252
##      2        1.2005             nan     0.1000    0.0164
##      3        1.1685             nan     0.1000    0.0146
##      4        1.1353             nan     0.1000    0.0111
##      5        1.1030             nan     0.1000    0.0136
##      6        1.0819             nan     0.1000    0.0086
##      7        1.0609             nan     0.1000    0.0078
##      8        1.0417             nan     0.1000    0.0067
##      9        1.0239             nan     0.1000    0.0063
##     10        1.0120             nan     0.1000    0.0015
##     20        0.9052             nan     0.1000    0.0003
##     40        0.8123             nan     0.1000   -0.0009
##     60        0.7609             nan     0.1000   -0.0003
##     80        0.7193             nan     0.1000   -0.0013
##    100        0.6831             nan     0.1000   -0.0013
##    120        0.6497             nan     0.1000   -0.0016
##    140        0.6165             nan     0.1000   -0.0007
##    160        0.5876             nan     0.1000   -0.0002
##    180        0.5580             nan     0.1000   -0.0004
##    200        0.5364             nan     0.1000   -0.0017
##    220        0.5087             nan     0.1000   -0.0001
##    240        0.4844             nan     0.1000   -0.0009
##    260        0.4650             nan     0.1000   -0.0013
##    280        0.4459             nan     0.1000   -0.0009
##    300        0.4287             nan     0.1000    0.0000
##    320        0.4139             nan     0.1000   -0.0006
##    340        0.3988             nan     0.1000   -0.0006
##    360        0.3861             nan     0.1000   -0.0004
##    380        0.3707             nan     0.1000   -0.0006
##    400        0.3563             nan     0.1000   -0.0009
##    420        0.3428             nan     0.1000   -0.0007
##    440        0.3313             nan     0.1000   -0.0007
##    460        0.3211             nan     0.1000   -0.0005
##    480        0.3084             nan     0.1000   -0.0003
##    500        0.2976             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2274             nan     0.2000    0.0327
##      2        1.1815             nan     0.2000    0.0206
##      3        1.1499             nan     0.2000    0.0122
##      4        1.1273             nan     0.2000    0.0047
##      5        1.0916             nan     0.2000    0.0075
##      6        1.0691             nan     0.2000    0.0045
##      7        1.0512             nan     0.2000    0.0055
##      8        1.0300             nan     0.2000    0.0077
##      9        1.0156             nan     0.2000    0.0046
##     10        1.0021             nan     0.2000    0.0050
##     20        0.9301             nan     0.2000   -0.0012
##     40        0.8744             nan     0.2000   -0.0021
##     60        0.8510             nan     0.2000   -0.0008
##     80        0.8246             nan     0.2000   -0.0052
##    100        0.8023             nan     0.2000   -0.0019
##    120        0.7879             nan     0.2000   -0.0002
##    140        0.7718             nan     0.2000   -0.0005
##    160        0.7649             nan     0.2000   -0.0014
##    180        0.7590             nan     0.2000   -0.0023
##    200        0.7452             nan     0.2000   -0.0031
##    220        0.7316             nan     0.2000   -0.0013
##    240        0.7243             nan     0.2000   -0.0008
##    260        0.7183             nan     0.2000   -0.0026
##    280        0.7043             nan     0.2000   -0.0009
##    300        0.6982             nan     0.2000   -0.0025
##    320        0.6910             nan     0.2000   -0.0022
##    340        0.6853             nan     0.2000   -0.0022
##    360        0.6808             nan     0.2000   -0.0018
##    380        0.6760             nan     0.2000   -0.0016
##    400        0.6661             nan     0.2000   -0.0002
##    420        0.6612             nan     0.2000   -0.0012
##    440        0.6598             nan     0.2000   -0.0017
##    460        0.6513             nan     0.2000   -0.0008
##    480        0.6461             nan     0.2000   -0.0013
##    500        0.6425             nan     0.2000   -0.0025
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2251             nan     0.2000    0.0306
##      2        1.1804             nan     0.2000    0.0145
##      3        1.1485             nan     0.2000    0.0088
##      4        1.1206             nan     0.2000    0.0135
##      5        1.0953             nan     0.2000    0.0100
##      6        1.0759             nan     0.2000    0.0086
##      7        1.0522             nan     0.2000    0.0109
##      8        1.0368             nan     0.2000    0.0056
##      9        1.0223             nan     0.2000    0.0024
##     10        1.0106             nan     0.2000    0.0028
##     20        0.9287             nan     0.2000   -0.0008
##     40        0.8647             nan     0.2000   -0.0001
##     60        0.8341             nan     0.2000   -0.0030
##     80        0.8090             nan     0.2000   -0.0022
##    100        0.7934             nan     0.2000   -0.0025
##    120        0.7751             nan     0.2000   -0.0019
##    140        0.7663             nan     0.2000   -0.0026
##    160        0.7558             nan     0.2000   -0.0010
##    180        0.7454             nan     0.2000   -0.0017
##    200        0.7379             nan     0.2000   -0.0016
##    220        0.7281             nan     0.2000   -0.0025
##    240        0.7202             nan     0.2000   -0.0006
##    260        0.7096             nan     0.2000   -0.0015
##    280        0.7024             nan     0.2000   -0.0007
##    300        0.6992             nan     0.2000   -0.0044
##    320        0.6887             nan     0.2000   -0.0021
##    340        0.6829             nan     0.2000   -0.0002
##    360        0.6749             nan     0.2000   -0.0024
##    380        0.6702             nan     0.2000   -0.0010
##    400        0.6671             nan     0.2000   -0.0012
##    420        0.6607             nan     0.2000   -0.0009
##    440        0.6534             nan     0.2000   -0.0022
##    460        0.6505             nan     0.2000   -0.0012
##    480        0.6474             nan     0.2000   -0.0008
##    500        0.6424             nan     0.2000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2193             nan     0.2000    0.0303
##      2        1.1788             nan     0.2000    0.0212
##      3        1.1489             nan     0.2000    0.0118
##      4        1.1170             nan     0.2000    0.0126
##      5        1.0924             nan     0.2000    0.0103
##      6        1.0723             nan     0.2000    0.0064
##      7        1.0497             nan     0.2000    0.0088
##      8        1.0347             nan     0.2000    0.0026
##      9        1.0200             nan     0.2000    0.0053
##     10        1.0033             nan     0.2000    0.0060
##     20        0.9264             nan     0.2000    0.0007
##     40        0.8637             nan     0.2000   -0.0037
##     60        0.8377             nan     0.2000   -0.0028
##     80        0.8162             nan     0.2000   -0.0015
##    100        0.7947             nan     0.2000   -0.0012
##    120        0.7779             nan     0.2000   -0.0025
##    140        0.7638             nan     0.2000   -0.0043
##    160        0.7546             nan     0.2000   -0.0009
##    180        0.7426             nan     0.2000   -0.0005
##    200        0.7310             nan     0.2000   -0.0022
##    220        0.7243             nan     0.2000   -0.0008
##    240        0.7138             nan     0.2000   -0.0026
##    260        0.7072             nan     0.2000   -0.0020
##    280        0.6993             nan     0.2000   -0.0014
##    300        0.6888             nan     0.2000   -0.0015
##    320        0.6853             nan     0.2000   -0.0011
##    340        0.6819             nan     0.2000   -0.0006
##    360        0.6759             nan     0.2000   -0.0028
##    380        0.6701             nan     0.2000   -0.0011
##    400        0.6668             nan     0.2000   -0.0026
##    420        0.6593             nan     0.2000   -0.0009
##    440        0.6562             nan     0.2000   -0.0036
##    460        0.6521             nan     0.2000    0.0001
##    480        0.6442             nan     0.2000   -0.0015
##    500        0.6424             nan     0.2000   -0.0040
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2112             nan     0.2000    0.0339
##      2        1.1480             nan     0.2000    0.0272
##      3        1.1039             nan     0.2000    0.0192
##      4        1.0670             nan     0.2000    0.0142
##      5        1.0390             nan     0.2000    0.0121
##      6        1.0131             nan     0.2000    0.0057
##      7        0.9943             nan     0.2000    0.0052
##      8        0.9829             nan     0.2000   -0.0010
##      9        0.9680             nan     0.2000    0.0033
##     10        0.9516             nan     0.2000    0.0061
##     20        0.8728             nan     0.2000   -0.0011
##     40        0.7910             nan     0.2000   -0.0028
##     60        0.7334             nan     0.2000   -0.0025
##     80        0.7011             nan     0.2000   -0.0016
##    100        0.6641             nan     0.2000   -0.0011
##    120        0.6303             nan     0.2000   -0.0017
##    140        0.5950             nan     0.2000   -0.0007
##    160        0.5580             nan     0.2000   -0.0016
##    180        0.5301             nan     0.2000   -0.0007
##    200        0.5104             nan     0.2000   -0.0019
##    220        0.4941             nan     0.2000   -0.0019
##    240        0.4677             nan     0.2000   -0.0011
##    260        0.4514             nan     0.2000   -0.0017
##    280        0.4233             nan     0.2000   -0.0025
##    300        0.4067             nan     0.2000   -0.0008
##    320        0.3892             nan     0.2000   -0.0014
##    340        0.3738             nan     0.2000   -0.0023
##    360        0.3601             nan     0.2000   -0.0009
##    380        0.3461             nan     0.2000   -0.0021
##    400        0.3325             nan     0.2000   -0.0013
##    420        0.3203             nan     0.2000   -0.0010
##    440        0.3073             nan     0.2000   -0.0005
##    460        0.2983             nan     0.2000   -0.0025
##    480        0.2895             nan     0.2000   -0.0017
##    500        0.2784             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2131             nan     0.2000    0.0381
##      2        1.1492             nan     0.2000    0.0275
##      3        1.1120             nan     0.2000    0.0141
##      4        1.0755             nan     0.2000    0.0174
##      5        1.0489             nan     0.2000    0.0097
##      6        1.0175             nan     0.2000    0.0136
##      7        0.9975             nan     0.2000    0.0069
##      8        0.9798             nan     0.2000    0.0062
##      9        0.9596             nan     0.2000    0.0050
##     10        0.9479             nan     0.2000    0.0004
##     20        0.8579             nan     0.2000   -0.0016
##     40        0.7798             nan     0.2000   -0.0016
##     60        0.7305             nan     0.2000   -0.0017
##     80        0.6774             nan     0.2000   -0.0024
##    100        0.6382             nan     0.2000   -0.0004
##    120        0.6057             nan     0.2000   -0.0054
##    140        0.5823             nan     0.2000   -0.0020
##    160        0.5531             nan     0.2000   -0.0015
##    180        0.5297             nan     0.2000   -0.0031
##    200        0.5051             nan     0.2000   -0.0017
##    220        0.4807             nan     0.2000   -0.0011
##    240        0.4590             nan     0.2000   -0.0006
##    260        0.4450             nan     0.2000   -0.0007
##    280        0.4284             nan     0.2000   -0.0006
##    300        0.4089             nan     0.2000   -0.0012
##    320        0.3908             nan     0.2000   -0.0007
##    340        0.3772             nan     0.2000   -0.0019
##    360        0.3660             nan     0.2000   -0.0020
##    380        0.3546             nan     0.2000   -0.0017
##    400        0.3429             nan     0.2000   -0.0010
##    420        0.3308             nan     0.2000   -0.0008
##    440        0.3167             nan     0.2000   -0.0012
##    460        0.3056             nan     0.2000   -0.0020
##    480        0.2952             nan     0.2000   -0.0020
##    500        0.2817             nan     0.2000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2114             nan     0.2000    0.0385
##      2        1.1560             nan     0.2000    0.0231
##      3        1.1064             nan     0.2000    0.0210
##      4        1.0684             nan     0.2000    0.0105
##      5        1.0390             nan     0.2000    0.0103
##      6        1.0133             nan     0.2000    0.0082
##      7        0.9899             nan     0.2000    0.0093
##      8        0.9684             nan     0.2000    0.0055
##      9        0.9567             nan     0.2000    0.0001
##     10        0.9439             nan     0.2000    0.0030
##     20        0.8698             nan     0.2000   -0.0006
##     40        0.7944             nan     0.2000   -0.0018
##     60        0.7471             nan     0.2000   -0.0039
##     80        0.7038             nan     0.2000   -0.0003
##    100        0.6651             nan     0.2000   -0.0010
##    120        0.6354             nan     0.2000   -0.0007
##    140        0.6058             nan     0.2000   -0.0031
##    160        0.5808             nan     0.2000   -0.0028
##    180        0.5569             nan     0.2000   -0.0027
##    200        0.5253             nan     0.2000   -0.0012
##    220        0.5022             nan     0.2000   -0.0011
##    240        0.4885             nan     0.2000   -0.0015
##    260        0.4646             nan     0.2000   -0.0013
##    280        0.4450             nan     0.2000   -0.0031
##    300        0.4275             nan     0.2000   -0.0018
##    320        0.4110             nan     0.2000   -0.0023
##    340        0.3915             nan     0.2000   -0.0024
##    360        0.3753             nan     0.2000   -0.0020
##    380        0.3619             nan     0.2000   -0.0007
##    400        0.3490             nan     0.2000   -0.0033
##    420        0.3372             nan     0.2000   -0.0012
##    440        0.3211             nan     0.2000   -0.0007
##    460        0.3113             nan     0.2000   -0.0020
##    480        0.3021             nan     0.2000   -0.0011
##    500        0.2916             nan     0.2000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1931             nan     0.2000    0.0456
##      2        1.1224             nan     0.2000    0.0301
##      3        1.0722             nan     0.2000    0.0202
##      4        1.0345             nan     0.2000    0.0120
##      5        0.9990             nan     0.2000    0.0138
##      6        0.9673             nan     0.2000    0.0128
##      7        0.9436             nan     0.2000    0.0052
##      8        0.9196             nan     0.2000    0.0083
##      9        0.9064             nan     0.2000    0.0017
##     10        0.8956             nan     0.2000   -0.0023
##     20        0.8008             nan     0.2000   -0.0008
##     40        0.7011             nan     0.2000   -0.0010
##     60        0.6239             nan     0.2000   -0.0019
##     80        0.5710             nan     0.2000   -0.0033
##    100        0.5164             nan     0.2000   -0.0007
##    120        0.4729             nan     0.2000   -0.0007
##    140        0.4434             nan     0.2000    0.0002
##    160        0.4136             nan     0.2000   -0.0008
##    180        0.3785             nan     0.2000   -0.0027
##    200        0.3522             nan     0.2000   -0.0015
##    220        0.3345             nan     0.2000   -0.0032
##    240        0.3184             nan     0.2000   -0.0025
##    260        0.2909             nan     0.2000   -0.0007
##    280        0.2733             nan     0.2000   -0.0015
##    300        0.2569             nan     0.2000   -0.0020
##    320        0.2397             nan     0.2000   -0.0018
##    340        0.2246             nan     0.2000   -0.0010
##    360        0.2117             nan     0.2000   -0.0021
##    380        0.2003             nan     0.2000   -0.0014
##    400        0.1856             nan     0.2000   -0.0013
##    420        0.1720             nan     0.2000   -0.0010
##    440        0.1598             nan     0.2000   -0.0018
##    460        0.1507             nan     0.2000   -0.0009
##    480        0.1421             nan     0.2000   -0.0003
##    500        0.1370             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1991             nan     0.2000    0.0354
##      2        1.1284             nan     0.2000    0.0369
##      3        1.0786             nan     0.2000    0.0205
##      4        1.0396             nan     0.2000    0.0143
##      5        1.0045             nan     0.2000    0.0150
##      6        0.9815             nan     0.2000    0.0034
##      7        0.9585             nan     0.2000    0.0034
##      8        0.9410             nan     0.2000    0.0004
##      9        0.9209             nan     0.2000    0.0055
##     10        0.9089             nan     0.2000   -0.0002
##     20        0.8164             nan     0.2000   -0.0015
##     40        0.7237             nan     0.2000   -0.0030
##     60        0.6536             nan     0.2000   -0.0021
##     80        0.5810             nan     0.2000   -0.0016
##    100        0.5376             nan     0.2000   -0.0023
##    120        0.4975             nan     0.2000   -0.0026
##    140        0.4608             nan     0.2000   -0.0029
##    160        0.4228             nan     0.2000   -0.0033
##    180        0.3893             nan     0.2000   -0.0009
##    200        0.3732             nan     0.2000   -0.0180
##    220        0.3223             nan     0.2000   -0.0021
##    240        0.3007             nan     0.2000   -0.0019
##    260        0.2782             nan     0.2000   -0.0025
##    280        0.2538             nan     0.2000   -0.0011
##    300        0.2367             nan     0.2000   -0.0008
##    320        0.2186             nan     0.2000   -0.0002
##    340        0.2022             nan     0.2000   -0.0011
##    360        0.1920             nan     0.2000   -0.0014
##    380        0.1785             nan     0.2000   -0.0010
##    400        0.1671             nan     0.2000   -0.0009
##    420        0.1570             nan     0.2000   -0.0008
##    440        0.1481             nan     0.2000   -0.0011
##    460        0.1391             nan     0.2000   -0.0009
##    480        0.1333             nan     0.2000   -0.0011
##    500        0.1251             nan     0.2000   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1989             nan     0.2000    0.0370
##      2        1.1311             nan     0.2000    0.0226
##      3        1.0767             nan     0.2000    0.0245
##      4        1.0309             nan     0.2000    0.0132
##      5        1.0043             nan     0.2000    0.0088
##      6        0.9802             nan     0.2000    0.0051
##      7        0.9553             nan     0.2000    0.0060
##      8        0.9361             nan     0.2000    0.0056
##      9        0.9189             nan     0.2000    0.0052
##     10        0.9044             nan     0.2000    0.0022
##     20        0.8081             nan     0.2000    0.0012
##     40        0.7131             nan     0.2000   -0.0015
##     60        0.6474             nan     0.2000   -0.0036
##     80        0.5889             nan     0.2000   -0.0004
##    100        0.5362             nan     0.2000   -0.0036
##    120        0.4912             nan     0.2000   -0.0012
##    140        0.4408             nan     0.2000   -0.0023
##    160        0.4102             nan     0.2000   -0.0032
##    180        0.3816             nan     0.2000   -0.0006
##    200        0.3552             nan     0.2000   -0.0024
##    220        0.3303             nan     0.2000   -0.0032
##    240        0.3070             nan     0.2000   -0.0018
##    260        0.2860             nan     0.2000   -0.0007
##    280        0.2677             nan     0.2000   -0.0012
##    300        0.2524             nan     0.2000   -0.0012
##    320        0.2319             nan     0.2000   -0.0019
##    340        0.2137             nan     0.2000   -0.0005
##    360        0.2005             nan     0.2000   -0.0021
##    380        0.1902             nan     0.2000   -0.0011
##    400        0.1772             nan     0.2000   -0.0007
##    420        0.1669             nan     0.2000   -0.0011
##    440        0.1569             nan     0.2000   -0.0007
##    460        0.1484             nan     0.2000   -0.0014
##    480        0.1410             nan     0.2000   -0.0005
##    500        0.1341             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2031             nan     0.3000    0.0418
##      2        1.1495             nan     0.3000    0.0213
##      3        1.1026             nan     0.3000    0.0141
##      4        1.0689             nan     0.3000    0.0134
##      5        1.0422             nan     0.3000    0.0142
##      6        1.0238             nan     0.3000    0.0062
##      7        1.0023             nan     0.3000    0.0079
##      8        0.9881             nan     0.3000    0.0040
##      9        0.9713             nan     0.3000    0.0051
##     10        0.9590             nan     0.3000    0.0026
##     20        0.9097             nan     0.3000   -0.0002
##     40        0.8394             nan     0.3000   -0.0002
##     60        0.8028             nan     0.3000   -0.0031
##     80        0.7873             nan     0.3000   -0.0007
##    100        0.7746             nan     0.3000    0.0003
##    120        0.7633             nan     0.3000   -0.0034
##    140        0.7463             nan     0.3000   -0.0020
##    160        0.7294             nan     0.3000   -0.0023
##    180        0.7175             nan     0.3000   -0.0067
##    200        0.7038             nan     0.3000   -0.0020
##    220        0.6996             nan     0.3000   -0.0036
##    240        0.6813             nan     0.3000   -0.0016
##    260        0.6708             nan     0.3000   -0.0034
##    280        0.6591             nan     0.3000   -0.0037
##    300        0.6531             nan     0.3000   -0.0036
##    320        0.6468             nan     0.3000   -0.0047
##    340        0.6367             nan     0.3000   -0.0044
##    360        0.6321             nan     0.3000   -0.0035
##    380        0.6271             nan     0.3000   -0.0027
##    400        0.6207             nan     0.3000   -0.0009
##    420        0.6129             nan     0.3000   -0.0016
##    440        0.6084             nan     0.3000   -0.0024
##    460        0.6031             nan     0.3000   -0.0017
##    480        0.5988             nan     0.3000   -0.0025
##    500        0.5950             nan     0.3000   -0.0042
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1908             nan     0.3000    0.0397
##      2        1.1498             nan     0.3000    0.0150
##      3        1.1177             nan     0.3000    0.0063
##      4        1.0863             nan     0.3000    0.0133
##      5        1.0521             nan     0.3000    0.0144
##      6        1.0310             nan     0.3000    0.0041
##      7        1.0105             nan     0.3000    0.0100
##      8        0.9998             nan     0.3000    0.0006
##      9        0.9892             nan     0.3000    0.0006
##     10        0.9725             nan     0.3000    0.0062
##     20        0.8924             nan     0.3000    0.0006
##     40        0.8431             nan     0.3000    0.0008
##     60        0.8163             nan     0.3000   -0.0027
##     80        0.7960             nan     0.3000   -0.0022
##    100        0.7784             nan     0.3000   -0.0015
##    120        0.7618             nan     0.3000   -0.0027
##    140        0.7436             nan     0.3000   -0.0029
##    160        0.7328             nan     0.3000   -0.0011
##    180        0.7176             nan     0.3000   -0.0029
##    200        0.7094             nan     0.3000   -0.0027
##    220        0.6945             nan     0.3000   -0.0017
##    240        0.6849             nan     0.3000   -0.0034
##    260        0.6692             nan     0.3000   -0.0031
##    280        0.6631             nan     0.3000   -0.0036
##    300        0.6569             nan     0.3000   -0.0019
##    320        0.6510             nan     0.3000   -0.0020
##    340        0.6454             nan     0.3000   -0.0039
##    360        0.6377             nan     0.3000   -0.0037
##    380        0.6309             nan     0.3000   -0.0025
##    400        0.6215             nan     0.3000   -0.0028
##    420        0.6132             nan     0.3000   -0.0027
##    440        0.6100             nan     0.3000   -0.0024
##    460        0.6033             nan     0.3000   -0.0046
##    480        0.5987             nan     0.3000   -0.0025
##    500        0.5952             nan     0.3000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2010             nan     0.3000    0.0448
##      2        1.1465             nan     0.3000    0.0223
##      3        1.1093             nan     0.3000    0.0121
##      4        1.0732             nan     0.3000    0.0145
##      5        1.0467             nan     0.3000    0.0088
##      6        1.0311             nan     0.3000    0.0041
##      7        1.0068             nan     0.3000    0.0092
##      8        0.9933             nan     0.3000    0.0006
##      9        0.9735             nan     0.3000    0.0068
##     10        0.9567             nan     0.3000    0.0059
##     20        0.8833             nan     0.3000   -0.0006
##     40        0.8294             nan     0.3000   -0.0055
##     60        0.8012             nan     0.3000   -0.0022
##     80        0.7826             nan     0.3000   -0.0038
##    100        0.7642             nan     0.3000   -0.0002
##    120        0.7464             nan     0.3000   -0.0040
##    140        0.7302             nan     0.3000   -0.0052
##    160        0.7141             nan     0.3000    0.0001
##    180        0.7010             nan     0.3000   -0.0026
##    200        0.6942             nan     0.3000   -0.0042
##    220        0.6852             nan     0.3000   -0.0065
##    240        0.6715             nan     0.3000   -0.0032
##    260        0.6667             nan     0.3000   -0.0040
##    280        0.6575             nan     0.3000   -0.0026
##    300        0.6502             nan     0.3000   -0.0027
##    320        0.6412             nan     0.3000   -0.0021
##    340        0.6383             nan     0.3000   -0.0046
##    360        0.6315             nan     0.3000   -0.0001
##    380        0.6227             nan     0.3000   -0.0031
##    400        0.6164             nan     0.3000   -0.0011
##    420        0.6146             nan     0.3000   -0.0051
##    440        0.6081             nan     0.3000   -0.0045
##    460        0.6047             nan     0.3000   -0.0042
##    480        0.6004             nan     0.3000   -0.0048
##    500        0.5945             nan     0.3000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1759             nan     0.3000    0.0530
##      2        1.0943             nan     0.3000    0.0339
##      3        1.0534             nan     0.3000    0.0098
##      4        1.0194             nan     0.3000    0.0078
##      5        0.9777             nan     0.3000    0.0163
##      6        0.9523             nan     0.3000    0.0066
##      7        0.9385             nan     0.3000    0.0034
##      8        0.9267             nan     0.3000    0.0008
##      9        0.9078             nan     0.3000    0.0029
##     10        0.8958             nan     0.3000    0.0027
##     20        0.8199             nan     0.3000   -0.0042
##     40        0.7343             nan     0.3000   -0.0010
##     60        0.6795             nan     0.3000   -0.0001
##     80        0.6244             nan     0.3000   -0.0041
##    100        0.5887             nan     0.3000   -0.0058
##    120        0.5448             nan     0.3000   -0.0057
##    140        0.5023             nan     0.3000   -0.0034
##    160        0.4718             nan     0.3000   -0.0034
##    180        0.4428             nan     0.3000   -0.0017
##    200        0.4194             nan     0.3000   -0.0022
##    220        0.3879             nan     0.3000   -0.0005
##    240        0.3628             nan     0.3000   -0.0033
##    260        0.3452             nan     0.3000   -0.0039
##    280        0.3185             nan     0.3000   -0.0011
##    300        0.3010             nan     0.3000   -0.0001
##    320        0.2849             nan     0.3000   -0.0039
##    340        0.2674             nan     0.3000   -0.0028
##    360        0.2551             nan     0.3000   -0.0022
##    380        0.2434             nan     0.3000   -0.0004
##    400        0.2286             nan     0.3000   -0.0012
##    420        0.2178             nan     0.3000   -0.0016
##    440        0.2074             nan     0.3000   -0.0025
##    460        0.1989             nan     0.3000   -0.0003
##    480        0.1876             nan     0.3000   -0.0017
##    500        0.1774             nan     0.3000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1775             nan     0.3000    0.0536
##      2        1.1009             nan     0.3000    0.0372
##      3        1.0549             nan     0.3000    0.0159
##      4        1.0150             nan     0.3000    0.0123
##      5        0.9894             nan     0.3000    0.0080
##      6        0.9598             nan     0.3000    0.0090
##      7        0.9367             nan     0.3000    0.0040
##      8        0.9206             nan     0.3000    0.0035
##      9        0.9075             nan     0.3000   -0.0012
##     10        0.8974             nan     0.3000   -0.0034
##     20        0.8253             nan     0.3000    0.0012
##     40        0.7497             nan     0.3000   -0.0062
##     60        0.6848             nan     0.3000   -0.0028
##     80        0.6363             nan     0.3000   -0.0055
##    100        0.5923             nan     0.3000   -0.0062
##    120        0.5514             nan     0.3000   -0.0018
##    140        0.5141             nan     0.3000   -0.0061
##    160        0.4807             nan     0.3000   -0.0019
##    180        0.4437             nan     0.3000   -0.0040
##    200        0.4250             nan     0.3000   -0.0056
##    220        0.4016             nan     0.3000   -0.0017
##    240        0.3763             nan     0.3000   -0.0023
##    260        0.3496             nan     0.3000   -0.0027
##    280        0.3335             nan     0.3000   -0.0024
##    300        0.3147             nan     0.3000   -0.0020
##    320        0.2942             nan     0.3000   -0.0023
##    340        0.2792             nan     0.3000   -0.0016
##    360        0.2657             nan     0.3000   -0.0023
##    380        0.2513             nan     0.3000   -0.0012
##    400        0.2482             nan     0.3000   -0.0028
##    420        0.2405             nan     0.3000   -0.0031
##    440        0.2161             nan     0.3000   -0.0016
##    460        0.2005             nan     0.3000   -0.0020
##    480        0.1897             nan     0.3000   -0.0014
##    500        0.1814             nan     0.3000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1670             nan     0.3000    0.0544
##      2        1.0990             nan     0.3000    0.0307
##      3        1.0573             nan     0.3000    0.0118
##      4        1.0136             nan     0.3000    0.0182
##      5        0.9827             nan     0.3000    0.0079
##      6        0.9595             nan     0.3000    0.0074
##      7        0.9381             nan     0.3000    0.0058
##      8        0.9244             nan     0.3000    0.0037
##      9        0.9061             nan     0.3000    0.0006
##     10        0.8895             nan     0.3000    0.0040
##     20        0.8315             nan     0.3000   -0.0043
##     40        0.7595             nan     0.3000   -0.0019
##     60        0.7029             nan     0.3000    0.0004
##     80        0.6565             nan     0.3000   -0.0034
##    100        0.6094             nan     0.3000   -0.0015
##    120        0.5688             nan     0.3000   -0.0003
##    140        0.5297             nan     0.3000   -0.0037
##    160        0.4998             nan     0.3000   -0.0021
##    180        0.4655             nan     0.3000   -0.0026
##    200        0.4365             nan     0.3000   -0.0020
##    220        0.4086             nan     0.3000   -0.0015
##    240        0.3856             nan     0.3000   -0.0040
##    260        0.3617             nan     0.3000   -0.0028
##    280        0.3395             nan     0.3000   -0.0031
##    300        0.3215             nan     0.3000   -0.0025
##    320        0.3067             nan     0.3000   -0.0007
##    340        0.2897             nan     0.3000   -0.0033
##    360        0.2772             nan     0.3000   -0.0010
##    380        0.2663             nan     0.3000   -0.0016
##    400        0.2510             nan     0.3000   -0.0022
##    420        0.2384             nan     0.3000   -0.0000
##    440        0.2285             nan     0.3000   -0.0018
##    460        0.2145             nan     0.3000   -0.0017
##    480        0.2033             nan     0.3000   -0.0013
##    500        0.1940             nan     0.3000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1474             nan     0.3000    0.0713
##      2        1.0615             nan     0.3000    0.0287
##      3        1.0159             nan     0.3000    0.0151
##      4        0.9696             nan     0.3000    0.0177
##      5        0.9441             nan     0.3000    0.0057
##      6        0.9175             nan     0.3000    0.0034
##      7        0.8965             nan     0.3000    0.0003
##      8        0.8816             nan     0.3000   -0.0012
##      9        0.8581             nan     0.3000    0.0061
##     10        0.8483             nan     0.3000   -0.0070
##     20        0.7645             nan     0.3000   -0.0016
##     40        0.6678             nan     0.3000   -0.0013
##     60        0.5858             nan     0.3000   -0.0008
##     80        0.5140             nan     0.3000   -0.0093
##    100        0.4557             nan     0.3000   -0.0035
##    120        0.4023             nan     0.3000   -0.0017
##    140        0.3638             nan     0.3000   -0.0026
##    160        0.3209             nan     0.3000   -0.0026
##    180        0.2902             nan     0.3000   -0.0005
##    200        0.2574             nan     0.3000   -0.0014
##    220        0.2342             nan     0.3000   -0.0027
##    240        0.2136             nan     0.3000   -0.0015
##    260        0.1919             nan     0.3000   -0.0013
##    280        0.1719             nan     0.3000   -0.0012
##    300        0.1591             nan     0.3000   -0.0006
##    320        0.1460             nan     0.3000   -0.0004
##    340        0.1333             nan     0.3000   -0.0010
##    360        0.1212             nan     0.3000   -0.0013
##    380        0.1135             nan     0.3000   -0.0005
##    400        0.1060             nan     0.3000   -0.0012
##    420        0.0982             nan     0.3000   -0.0008
##    440        0.0893             nan     0.3000   -0.0007
##    460        0.0818             nan     0.3000   -0.0009
##    480        0.0743             nan     0.3000   -0.0009
##    500        0.0685             nan     0.3000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1470             nan     0.3000    0.0553
##      2        1.0744             nan     0.3000    0.0248
##      3        1.0221             nan     0.3000    0.0181
##      4        0.9783             nan     0.3000    0.0155
##      5        0.9497             nan     0.3000    0.0036
##      6        0.9197             nan     0.3000    0.0066
##      7        0.9001             nan     0.3000    0.0043
##      8        0.8859             nan     0.3000    0.0005
##      9        0.8712             nan     0.3000   -0.0018
##     10        0.8561             nan     0.3000   -0.0017
##     20        0.7803             nan     0.3000   -0.0100
##     40        0.6606             nan     0.3000   -0.0008
##     60        0.5826             nan     0.3000   -0.0039
##     80        0.4961             nan     0.3000   -0.0021
##    100        0.4432             nan     0.3000   -0.0037
##    120        0.3918             nan     0.3000   -0.0006
##    140        0.3535             nan     0.3000   -0.0041
##    160        0.3076             nan     0.3000   -0.0021
##    180        0.2805             nan     0.3000   -0.0019
##    200        0.2543             nan     0.3000   -0.0009
##    220        0.2333             nan     0.3000   -0.0017
##    240        0.2092             nan     0.3000   -0.0022
##    260        0.1900             nan     0.3000   -0.0018
##    280        0.1772             nan     0.3000   -0.0018
##    300        0.1596             nan     0.3000   -0.0006
##    320        0.1463             nan     0.3000   -0.0011
##    340        0.1367             nan     0.3000   -0.0018
##    360        0.1244             nan     0.3000   -0.0009
##    380        0.1143             nan     0.3000   -0.0014
##    400        0.1049             nan     0.3000   -0.0010
##    420        0.0957             nan     0.3000   -0.0010
##    440        0.0872             nan     0.3000   -0.0010
##    460        0.0825             nan     0.3000   -0.0014
##    480        0.0755             nan     0.3000   -0.0002
##    500        0.0700             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1644             nan     0.3000    0.0558
##      2        1.0740             nan     0.3000    0.0398
##      3        1.0216             nan     0.3000    0.0157
##      4        0.9810             nan     0.3000    0.0044
##      5        0.9519             nan     0.3000    0.0063
##      6        0.9374             nan     0.3000   -0.0057
##      7        0.9085             nan     0.3000    0.0002
##      8        0.8842             nan     0.3000    0.0011
##      9        0.8770             nan     0.3000   -0.0045
##     10        0.8655             nan     0.3000   -0.0039
##     20        0.7731             nan     0.3000   -0.0032
##     40        0.6689             nan     0.3000   -0.0017
##     60        0.5899             nan     0.3000   -0.0024
##     80        0.5211             nan     0.3000   -0.0015
##    100        0.4504             nan     0.3000   -0.0015
##    120        0.3948             nan     0.3000   -0.0025
##    140        0.3494             nan     0.3000   -0.0061
##    160        0.3175             nan     0.3000   -0.0041
##    180        0.2785             nan     0.3000   -0.0017
##    200        0.2488             nan     0.3000   -0.0055
##    220        0.2219             nan     0.3000   -0.0018
##    240        0.1986             nan     0.3000   -0.0016
##    260        0.1764             nan     0.3000   -0.0026
##    280        0.1625             nan     0.3000   -0.0021
##    300        0.1481             nan     0.3000   -0.0014
##    320        0.1336             nan     0.3000   -0.0011
##    340        0.1228             nan     0.3000   -0.0014
##    360        0.1115             nan     0.3000   -0.0019
##    380        0.1031             nan     0.3000   -0.0020
##    400        0.0949             nan     0.3000   -0.0008
##    420        0.0887             nan     0.3000   -0.0010
##    440        0.0810             nan     0.3000   -0.0005
##    460        0.0727             nan     0.3000   -0.0001
##    480        0.0663             nan     0.3000   -0.0003
##    500        0.0618             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1595             nan     0.5000    0.0654
##      2        1.1059             nan     0.5000    0.0221
##      3        1.0640             nan     0.5000    0.0129
##      4        1.0158             nan     0.5000    0.0096
##      5        0.9849             nan     0.5000    0.0129
##      6        0.9552             nan     0.5000    0.0107
##      7        0.9409             nan     0.5000    0.0005
##      8        0.9206             nan     0.5000   -0.0006
##      9        0.9132             nan     0.5000   -0.0039
##     10        0.8988             nan     0.5000    0.0045
##     20        0.8409             nan     0.5000   -0.0017
##     40        0.7962             nan     0.5000   -0.0044
##     60        0.7632             nan     0.5000   -0.0075
##     80        0.7514             nan     0.5000   -0.0104
##    100        0.7399             nan     0.5000   -0.0062
##    120        0.7062             nan     0.5000   -0.0061
##    140        0.6943             nan     0.5000   -0.0104
##    160        0.6781             nan     0.5000   -0.0015
##    180        0.6594             nan     0.5000   -0.0040
##    200        0.6497             nan     0.5000   -0.0042
##    220        0.6425             nan     0.5000   -0.0032
##    240        0.6303             nan     0.5000   -0.0056
##    260        0.6196             nan     0.5000   -0.0075
##    280        0.6178             nan     0.5000   -0.0103
##    300        0.5935             nan     0.5000   -0.0003
##    320        0.5876             nan     0.5000   -0.0083
##    340        0.5820             nan     0.5000   -0.0013
##    360        0.5660             nan     0.5000   -0.0071
##    380        0.5577             nan     0.5000   -0.0041
##    400        0.5469             nan     0.5000   -0.0057
##    420        0.5520             nan     0.5000   -0.0085
##    440        0.5420             nan     0.5000   -0.0038
##    460        0.5329             nan     0.5000   -0.0056
##    480        0.5270             nan     0.5000   -0.0065
##    500        0.5105             nan     0.5000   -0.0031
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1784             nan     0.5000    0.0512
##      2        1.1041             nan     0.5000    0.0341
##      3        1.0487             nan     0.5000    0.0247
##      4        0.9977             nan     0.5000    0.0129
##      5        0.9754             nan     0.5000    0.0032
##      6        0.9540             nan     0.5000    0.0022
##      7        0.9410             nan     0.5000   -0.0001
##      8        0.9246             nan     0.5000    0.0080
##      9        0.9202             nan     0.5000   -0.0060
##     10        0.9136             nan     0.5000    0.0005
##     20        0.8588             nan     0.5000   -0.0079
##     40        0.8055             nan     0.5000   -0.0020
##     60        0.7784             nan     0.5000   -0.0003
##     80        0.7503             nan     0.5000   -0.0063
##    100        0.7391             nan     0.5000   -0.0058
##    120        0.7200             nan     0.5000   -0.0055
##    140        0.7050             nan     0.5000   -0.0045
##    160        0.6916             nan     0.5000    0.0023
##    180        0.6782             nan     0.5000   -0.0035
##    200        0.6722             nan     0.5000   -0.0028
##    220        0.6444             nan     0.5000   -0.0045
##    240        0.6286             nan     0.5000   -0.0084
##    260        0.6229             nan     0.5000   -0.0004
##    280        0.6131             nan     0.5000   -0.0056
##    300        0.6034             nan     0.5000   -0.0063
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000   -0.0061
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1504             nan     0.5000    0.0627
##      2        1.1000             nan     0.5000    0.0239
##      3        1.0452             nan     0.5000    0.0237
##      4        1.0056             nan     0.5000    0.0178
##      5        0.9854             nan     0.5000    0.0058
##      6        0.9685             nan     0.5000    0.0026
##      7        0.9437             nan     0.5000    0.0052
##      8        0.9184             nan     0.5000    0.0007
##      9        0.9119             nan     0.5000   -0.0023
##     10        0.8992             nan     0.5000   -0.0002
##     20        0.8496             nan     0.5000   -0.0035
##     40        0.8067             nan     0.5000   -0.0037
##     60        0.7811             nan     0.5000   -0.0042
##     80        0.7629             nan     0.5000   -0.0045
##    100        0.7383             nan     0.5000   -0.0054
##    120        0.7171             nan     0.5000   -0.0030
##    140        0.7091             nan     0.5000   -0.0037
##    160        0.6880             nan     0.5000   -0.0010
##    180        0.6740             nan     0.5000   -0.0106
##    200        0.6649             nan     0.5000   -0.0064
##    220        0.6508             nan     0.5000   -0.0053
##    240        0.6475             nan     0.5000   -0.0092
##    260        0.6498             nan     0.5000   -0.0028
##    280        0.6370             nan     0.5000   -0.0095
##    300        0.6236             nan     0.5000   -0.0061
##    320        0.6208             nan     0.5000   -0.0114
##    340        0.5992             nan     0.5000   -0.0012
##    360        0.5882             nan     0.5000   -0.0067
##    380        0.5802             nan     0.5000   -0.0093
##    400        0.5697             nan     0.5000   -0.0033
##    420        0.5655             nan     0.5000   -0.0012
##    440        0.5555             nan     0.5000   -0.0037
##    460        0.5554             nan     0.5000   -0.0091
##    480        0.5451             nan     0.5000   -0.0034
##    500        0.5403             nan     0.5000   -0.0074
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1349             nan     0.5000    0.0563
##      2        1.0497             nan     0.5000    0.0275
##      3        1.0033             nan     0.5000   -0.0021
##      4        0.9741             nan     0.5000    0.0062
##      5        0.9455             nan     0.5000    0.0039
##      6        0.9250             nan     0.5000   -0.0067
##      7        0.8982             nan     0.5000    0.0045
##      8        0.8928             nan     0.5000   -0.0120
##      9        0.8873             nan     0.5000   -0.0128
##     10        0.8812             nan     0.5000   -0.0118
##     20        0.7868             nan     0.5000   -0.0117
##     40        0.6830             nan     0.5000   -0.0061
##     60        0.8574             nan     0.5000   -0.0080
##     80        0.8233             nan     0.5000   -0.0034
##    100        0.7827             nan     0.5000   -0.0061
##    120        0.7177             nan     0.5000   -0.0042
##    140        0.6825             nan     0.5000   -0.0086
##    160        0.6643             nan     0.5000   -0.0033
##    180        0.6401             nan     0.5000   -0.0000
##    200        0.6071             nan     0.5000   -0.0059
##    220        0.5797             nan     0.5000   -0.0038
##    240        0.5707             nan     0.5000   -0.0018
##    260        0.5478             nan     0.5000   -0.0021
##    280        0.5638             nan     0.5000    0.0007
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000   -0.0014
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000    0.0000
##    420           inf             nan     0.5000   -0.0044
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000   -0.0031
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1282             nan     0.5000    0.0723
##      2        1.0738             nan     0.5000    0.0071
##      3        0.9964             nan     0.5000    0.0201
##      4        0.9672             nan     0.5000    0.0007
##      5        0.9441             nan     0.5000    0.0002
##      6        0.9235             nan     0.5000   -0.0028
##      7        0.8942             nan     0.5000    0.0044
##      8        0.8857             nan     0.5000   -0.0117
##      9        0.8730             nan     0.5000   -0.0013
##     10        0.8620             nan     0.5000   -0.0028
##     20        0.7830             nan     0.5000   -0.0021
##     40        0.6981             nan     0.5000   -0.0032
##     60        0.6519             nan     0.5000   -0.0111
##     80        0.5733             nan     0.5000   -0.0093
##    100        0.5139             nan     0.5000   -0.0218
##    120        0.4602             nan     0.5000   -0.0045
##    140        0.4257             nan     0.5000   -0.0099
##    160        0.4081             nan     0.5000   -0.0067
##    180        0.3650             nan     0.5000   -0.0033
##    200        0.3461             nan     0.5000   -0.0049
##    220        0.3153             nan     0.5000   -0.0025
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1156             nan     0.5000    0.0744
##      2        1.0355             nan     0.5000    0.0271
##      3        0.9806             nan     0.5000    0.0132
##      4        0.9548             nan     0.5000    0.0017
##      5        0.9246             nan     0.5000    0.0005
##      6        0.8993             nan     0.5000   -0.0068
##      7        0.8850             nan     0.5000   -0.0140
##      8        0.8726             nan     0.5000   -0.0061
##      9        0.8631             nan     0.5000   -0.0084
##     10        0.8480             nan     0.5000    0.0015
##     20        0.7711             nan     0.5000   -0.0056
##     40        0.7061             nan     0.5000   -0.0031
##     60        0.6504             nan     0.5000   -0.0121
##     80        0.5640             nan     0.5000   -0.0060
##    100        0.5115             nan     0.5000   -0.0016
##    120        0.4813             nan     0.5000   -0.0012
##    140        0.4381             nan     0.5000   -0.0038
##    160        0.4033             nan     0.5000   -0.0081
##    180        0.3569             nan     0.5000   -0.0064
##    200        0.3136             nan     0.5000   -0.0059
##    220        0.2774             nan     0.5000   -0.0013
##    240        0.2514             nan     0.5000   -0.0026
##    260        0.2279             nan     0.5000   -0.0026
##    280        0.2107             nan     0.5000   -0.0011
##    300        0.1979             nan     0.5000   -0.0052
##    320        0.1812             nan     0.5000   -0.0016
##    340        0.1619             nan     0.5000   -0.0002
##    360        0.1538             nan     0.5000   -0.0018
##    380        0.1414             nan     0.5000   -0.0011
##    400        0.1311             nan     0.5000   -0.0023
##    420        0.1203             nan     0.5000   -0.0002
##    440        0.1119             nan     0.5000   -0.0002
##    460        0.1055             nan     0.5000   -0.0021
##    480        0.0976             nan     0.5000   -0.0014
##    500        0.0906             nan     0.5000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1050             nan     0.5000    0.0810
##      2        1.0201             nan     0.5000    0.0235
##      3        0.9660             nan     0.5000    0.0098
##      4        0.9488             nan     0.5000   -0.0099
##      5        0.9241             nan     0.5000   -0.0102
##      6        0.8928             nan     0.5000    0.0005
##      7        0.8831             nan     0.5000   -0.0091
##      8        0.8781             nan     0.5000   -0.0179
##      9        0.8619             nan     0.5000   -0.0071
##     10        0.8503             nan     0.5000   -0.0063
##     20        0.7652             nan     0.5000   -0.0347
##     40        0.6160             nan     0.5000   -0.0068
##     60        0.5264             nan     0.5000   -0.0204
##     80        0.5828             nan     0.5000   -0.0002
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0931             nan     0.5000    0.0840
##      2        1.0154             nan     0.5000    0.0267
##      3        0.9564             nan     0.5000    0.0121
##      4        0.9149             nan     0.5000    0.0031
##      5        0.8810             nan     0.5000   -0.0038
##      6        0.8630             nan     0.5000   -0.0062
##      7        0.8431             nan     0.5000   -0.0036
##      8        0.8314             nan     0.5000   -0.0132
##      9        0.8116             nan     0.5000   -0.0013
##     10        0.7988             nan     0.5000   -0.0083
##     20        0.7208             nan     0.5000   -0.0139
##     40        0.5989             nan     0.5000   -0.0145
##     60        0.4847             nan     0.5000   -0.0026
##     80        0.4327             nan     0.5000   -0.0159
##    100        0.3763             nan     0.5000   -0.0033
##    120        0.3079             nan     0.5000   -0.0037
##    140        0.2573             nan     0.5000   -0.0008
##    160        0.2077             nan     0.5000   -0.0018
##    180        0.1710             nan     0.5000   -0.0032
##    200        0.1438             nan     0.5000   -0.0008
##    220        0.1254             nan     0.5000   -0.0009
##    240        0.1070             nan     0.5000   -0.0004
##    260        0.0939             nan     0.5000   -0.0013
##    280        0.0815             nan     0.5000   -0.0011
##    300        0.0689             nan     0.5000   -0.0009
##    320        0.0627             nan     0.5000   -0.0004
##    340        0.0546             nan     0.5000   -0.0009
##    360        0.0500             nan     0.5000   -0.0003
##    380        0.0445             nan     0.5000   -0.0002
##    400        0.0393             nan     0.5000   -0.0003
##    420        0.0347             nan     0.5000   -0.0002
##    440        0.0310             nan     0.5000   -0.0003
##    460        0.0276             nan     0.5000   -0.0001
##    480        0.0245             nan     0.5000   -0.0001
##    500        0.0223             nan     0.5000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0932             nan     0.5000    0.0780
##      2        1.0120             nan     0.5000    0.0249
##      3        0.9812             nan     0.5000   -0.0073
##      4        0.9307             nan     0.5000    0.0068
##      5        0.9090             nan     0.5000    0.0000
##      6        0.8713             nan     0.5000    0.0079
##      7        0.8532             nan     0.5000   -0.0045
##      8        0.8321             nan     0.5000    0.0040
##      9        0.8277             nan     0.5000   -0.0108
##     10        0.8230             nan     0.5000   -0.0127
##     20        0.7599             nan     0.5000   -0.0186
##     40        0.6000             nan     0.5000   -0.0095
##     60        0.4873             nan     0.5000   -0.0010
##     80        0.4147             nan     0.5000   -0.0075
##    100        0.3523             nan     0.5000   -0.0062
##    120        0.2874             nan     0.5000   -0.0044
##    140        0.2574             nan     0.5000   -0.0010
##    160        0.2177             nan     0.5000   -0.0023
##    180        0.1802             nan     0.5000   -0.0015
##    200        0.1516             nan     0.5000   -0.0017
##    220        0.1314             nan     0.5000   -0.0023
##    240        0.1157             nan     0.5000   -0.0021
##    260        0.1006             nan     0.5000   -0.0017
##    280        0.0859             nan     0.5000   -0.0003
##    300        0.0778             nan     0.5000   -0.0021
##    320        0.0685             nan     0.5000   -0.0023
##    340        0.0605             nan     0.5000   -0.0013
##    360        0.0540             nan     0.5000   -0.0008
##    380        0.0463             nan     0.5000   -0.0005
##    400        0.0412             nan     0.5000   -0.0003
##    420        0.0366             nan     0.5000   -0.0005
##    440        0.0331             nan     0.5000   -0.0003
##    460        0.0295             nan     0.5000   -0.0003
##    480        0.0258             nan     0.5000   -0.0004
##    500        0.0236             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1287             nan     1.0000    0.0494
##      2        1.0668             nan     1.0000    0.0179
##      3        1.0106             nan     1.0000    0.0143
##      4        1.0023             nan     1.0000   -0.0058
##      5        0.9831             nan     1.0000    0.0061
##      6        0.9859             nan     1.0000   -0.0281
##      7        0.9819             nan     1.0000   -0.0051
##      8        0.9921             nan     1.0000   -0.0256
##      9        1.0257             nan     1.0000   -0.0590
##     10        0.9614             nan     1.0000    0.0343
##     20        0.8856             nan     1.0000    0.0449
##     40  1094509.5811             nan     1.0000   -0.0009
##     60  1094509.5383             nan     1.0000    0.0008
##     80  1094509.5348             nan     1.0000    0.0004
##    100  1094509.5390             nan     1.0000    0.0008
##    120  1094509.7372             nan     1.0000   -0.0036
##    140 16076000.7384             nan     1.0000   -0.0009
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1333             nan     1.0000    0.0391
##      2        1.0601             nan     1.0000   -0.0007
##      3        1.0413             nan     1.0000   -0.0195
##      4        0.9997             nan     1.0000    0.0239
##      5        0.9970             nan     1.0000   -0.0227
##      6        0.9715             nan     1.0000    0.0081
##      7        0.9668             nan     1.0000   -0.0111
##      8        0.9512             nan     1.0000    0.0007
##      9        0.9634             nan     1.0000   -0.0342
##     10        1.0059             nan     1.0000   -0.0635
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1380             nan     1.0000    0.0792
##      2        1.0665             nan     1.0000    0.0196
##      3        1.0049             nan     1.0000    0.0194
##      4        0.9749             nan     1.0000    0.0083
##      5        0.9889             nan     1.0000   -0.0384
##      6        0.9761             nan     1.0000   -0.0079
##      7        0.9758             nan     1.0000   -0.0330
##      8        0.9578             nan     1.0000   -0.0028
##      9        0.9828             nan     1.0000   -0.0395
##     10        0.9456             nan     1.0000    0.0186
##     20        1.0953             nan     1.0000   -0.1321
##     40  2218295.2123             nan     1.0000   -0.0123
##     60  2218295.1634             nan     1.0000   -0.0086
##     80           inf             nan     1.0000      -inf
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0715             nan     1.0000    0.0962
##      2        0.9911             nan     1.0000    0.0021
##      3        0.9845             nan     1.0000   -0.0333
##      4        0.9260             nan     1.0000    0.0188
##      5        0.9444             nan     1.0000   -0.0453
##      6        0.8899             nan     1.0000    0.0128
##      7        0.8801             nan     1.0000   -0.0126
##      8        0.8672             nan     1.0000   -0.0067
##      9        0.8792             nan     1.0000   -0.0342
##     10        0.8701             nan     1.0000   -0.0140
##     20        0.8512             nan     1.0000   -0.0423
##     40        1.6950             nan     1.0000   -0.0187
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0667             nan     1.0000    0.0654
##      2        0.9865             nan     1.0000    0.0263
##      3        0.9662             nan     1.0000   -0.0150
##      4        0.9433             nan     1.0000   -0.0215
##      5        0.9423             nan     1.0000   -0.0432
##      6        0.9556             nan     1.0000   -0.0306
##      7        0.9289             nan     1.0000    0.0006
##      8        0.9398             nan     1.0000   -0.0417
##      9        0.9303             nan     1.0000   -0.0371
##     10       51.5332             nan     1.0000  -25.3041
##     20   811941.2650             nan     1.0000   -0.0540
##     40   811941.1825             nan     1.0000   -0.0030
##     60   811941.2057             nan     1.0000   -0.0360
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0643             nan     1.0000    0.1110
##      2        0.9831             nan     1.0000    0.0256
##      3        0.9665             nan     1.0000   -0.0139
##      4        0.9402             nan     1.0000    0.0046
##      5        0.9319             nan     1.0000   -0.0135
##      6        0.9292             nan     1.0000   -0.0218
##      7        0.9318             nan     1.0000   -0.0291
##      8        0.8942             nan     1.0000    0.0071
##      9        0.8801             nan     1.0000   -0.0045
##     10        0.8617             nan     1.0000   -0.0239
##     20        0.9150             nan     1.0000   -0.0245
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0588             nan     1.0000    0.0532
##      2        0.9933             nan     1.0000    0.0052
##      3        0.9738             nan     1.0000   -0.0165
##      4        0.9180             nan     1.0000    0.0009
##      5        0.8976             nan     1.0000   -0.0213
##      6        0.8912             nan     1.0000   -0.0188
##      7        0.8701             nan     1.0000   -0.0164
##      8        0.8799             nan     1.0000   -0.0364
##      9        0.8689             nan     1.0000   -0.0252
##     10        0.8866             nan     1.0000   -0.0462
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1219             nan     1.0000   -0.0318
##      2        1.0535             nan     1.0000    0.0042
##      3        0.9900             nan     1.0000    0.0031
##      4        0.9788             nan     1.0000   -0.0237
##      5        1.0803             nan     1.0000   -0.1086
##      6        1.1023             nan     1.0000   -0.0708
##      7        1.1784             nan     1.0000   -0.1156
##      8        1.1877             nan     1.0000   -0.0603
##      9        1.2133             nan     1.0000   -0.0890
##     10        1.3639             nan     1.0000   -0.2459
##     20        1.4180             nan     1.0000   -0.0525
##     40  7914568.2503             nan     1.0000    0.0013
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0626             nan     1.0000    0.0713
##      2        0.9704             nan     1.0000    0.0216
##      3        0.9749             nan     1.0000   -0.0424
##      4        0.9081             nan     1.0000    0.0139
##      5        0.8993             nan     1.0000   -0.0187
##      6        0.9980             nan     1.0000   -0.1545
##      7        0.9970             nan     1.0000   -0.0330
##      8        3.3058             nan     1.0000   -2.3891
##      9        3.3085             nan     1.0000   -0.0402
##     10        3.3481             nan     1.0000   -0.0904
##     20        5.0314             nan     1.0000   -0.3085
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0002
##      3        1.2935             nan     0.0010    0.0001
##      4        1.2931             nan     0.0010    0.0002
##      5        1.2928             nan     0.0010    0.0002
##      6        1.2923             nan     0.0010    0.0002
##      7        1.2920             nan     0.0010    0.0001
##      8        1.2916             nan     0.0010    0.0002
##      9        1.2913             nan     0.0010    0.0002
##     10        1.2909             nan     0.0010    0.0002
##     20        1.2874             nan     0.0010    0.0002
##     40        1.2805             nan     0.0010    0.0002
##     60        1.2741             nan     0.0010    0.0001
##     80        1.2676             nan     0.0010    0.0001
##    100        1.2614             nan     0.0010    0.0002
##    120        1.2553             nan     0.0010    0.0001
##    140        1.2495             nan     0.0010    0.0001
##    160        1.2440             nan     0.0010    0.0001
##    180        1.2385             nan     0.0010    0.0001
##    200        1.2332             nan     0.0010    0.0001
##    220        1.2282             nan     0.0010    0.0001
##    240        1.2230             nan     0.0010    0.0001
##    260        1.2184             nan     0.0010    0.0001
##    280        1.2139             nan     0.0010    0.0001
##    300        1.2093             nan     0.0010    0.0001
##    320        1.2050             nan     0.0010    0.0001
##    340        1.2007             nan     0.0010    0.0001
##    360        1.1965             nan     0.0010    0.0001
##    380        1.1925             nan     0.0010    0.0001
##    400        1.1884             nan     0.0010    0.0001
##    420        1.1845             nan     0.0010    0.0001
##    440        1.1807             nan     0.0010    0.0001
##    460        1.1771             nan     0.0010    0.0001
##    480        1.1734             nan     0.0010    0.0001
##    500        1.1698             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0002
##      3        1.2935             nan     0.0010    0.0002
##      4        1.2931             nan     0.0010    0.0002
##      5        1.2928             nan     0.0010    0.0002
##      6        1.2924             nan     0.0010    0.0002
##      7        1.2920             nan     0.0010    0.0002
##      8        1.2917             nan     0.0010    0.0001
##      9        1.2913             nan     0.0010    0.0002
##     10        1.2910             nan     0.0010    0.0002
##     20        1.2875             nan     0.0010    0.0002
##     40        1.2806             nan     0.0010    0.0001
##     60        1.2740             nan     0.0010    0.0002
##     80        1.2676             nan     0.0010    0.0001
##    100        1.2613             nan     0.0010    0.0001
##    120        1.2554             nan     0.0010    0.0001
##    140        1.2496             nan     0.0010    0.0002
##    160        1.2439             nan     0.0010    0.0001
##    180        1.2385             nan     0.0010    0.0001
##    200        1.2331             nan     0.0010    0.0001
##    220        1.2279             nan     0.0010    0.0001
##    240        1.2229             nan     0.0010    0.0001
##    260        1.2181             nan     0.0010    0.0001
##    280        1.2134             nan     0.0010    0.0001
##    300        1.2089             nan     0.0010    0.0001
##    320        1.2045             nan     0.0010    0.0001
##    340        1.2001             nan     0.0010    0.0001
##    360        1.1961             nan     0.0010    0.0001
##    380        1.1921             nan     0.0010    0.0001
##    400        1.1882             nan     0.0010    0.0001
##    420        1.1841             nan     0.0010    0.0001
##    440        1.1803             nan     0.0010    0.0001
##    460        1.1765             nan     0.0010    0.0001
##    480        1.1727             nan     0.0010    0.0001
##    500        1.1691             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2938             nan     0.0010    0.0001
##      3        1.2935             nan     0.0010    0.0001
##      4        1.2931             nan     0.0010    0.0002
##      5        1.2928             nan     0.0010    0.0002
##      6        1.2924             nan     0.0010    0.0001
##      7        1.2920             nan     0.0010    0.0002
##      8        1.2917             nan     0.0010    0.0002
##      9        1.2913             nan     0.0010    0.0002
##     10        1.2909             nan     0.0010    0.0002
##     20        1.2875             nan     0.0010    0.0001
##     40        1.2804             nan     0.0010    0.0002
##     60        1.2738             nan     0.0010    0.0001
##     80        1.2674             nan     0.0010    0.0001
##    100        1.2612             nan     0.0010    0.0001
##    120        1.2551             nan     0.0010    0.0001
##    140        1.2494             nan     0.0010    0.0001
##    160        1.2437             nan     0.0010    0.0001
##    180        1.2383             nan     0.0010    0.0001
##    200        1.2331             nan     0.0010    0.0001
##    220        1.2281             nan     0.0010    0.0001
##    240        1.2232             nan     0.0010    0.0001
##    260        1.2185             nan     0.0010    0.0001
##    280        1.2140             nan     0.0010    0.0001
##    300        1.2093             nan     0.0010    0.0001
##    320        1.2049             nan     0.0010    0.0001
##    340        1.2007             nan     0.0010    0.0001
##    360        1.1965             nan     0.0010    0.0001
##    380        1.1924             nan     0.0010    0.0001
##    400        1.1883             nan     0.0010    0.0001
##    420        1.1844             nan     0.0010    0.0001
##    440        1.1805             nan     0.0010    0.0001
##    460        1.1767             nan     0.0010    0.0001
##    480        1.1731             nan     0.0010    0.0001
##    500        1.1695             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2930             nan     0.0010    0.0002
##      4        1.2925             nan     0.0010    0.0002
##      5        1.2920             nan     0.0010    0.0002
##      6        1.2915             nan     0.0010    0.0002
##      7        1.2910             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2849             nan     0.0010    0.0002
##     40        1.2759             nan     0.0010    0.0002
##     60        1.2671             nan     0.0010    0.0002
##     80        1.2585             nan     0.0010    0.0002
##    100        1.2503             nan     0.0010    0.0002
##    120        1.2423             nan     0.0010    0.0002
##    140        1.2345             nan     0.0010    0.0002
##    160        1.2269             nan     0.0010    0.0002
##    180        1.2198             nan     0.0010    0.0002
##    200        1.2130             nan     0.0010    0.0001
##    220        1.2061             nan     0.0010    0.0001
##    240        1.1991             nan     0.0010    0.0002
##    260        1.1927             nan     0.0010    0.0001
##    280        1.1863             nan     0.0010    0.0001
##    300        1.1800             nan     0.0010    0.0001
##    320        1.1738             nan     0.0010    0.0001
##    340        1.1678             nan     0.0010    0.0001
##    360        1.1619             nan     0.0010    0.0001
##    380        1.1562             nan     0.0010    0.0001
##    400        1.1508             nan     0.0010    0.0001
##    420        1.1455             nan     0.0010    0.0001
##    440        1.1403             nan     0.0010    0.0001
##    460        1.1353             nan     0.0010    0.0001
##    480        1.1304             nan     0.0010    0.0001
##    500        1.1257             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2921             nan     0.0010    0.0002
##      6        1.2916             nan     0.0010    0.0002
##      7        1.2911             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2848             nan     0.0010    0.0002
##     40        1.2759             nan     0.0010    0.0002
##     60        1.2672             nan     0.0010    0.0002
##     80        1.2587             nan     0.0010    0.0002
##    100        1.2506             nan     0.0010    0.0002
##    120        1.2425             nan     0.0010    0.0002
##    140        1.2348             nan     0.0010    0.0002
##    160        1.2272             nan     0.0010    0.0002
##    180        1.2200             nan     0.0010    0.0002
##    200        1.2131             nan     0.0010    0.0001
##    220        1.2062             nan     0.0010    0.0002
##    240        1.1995             nan     0.0010    0.0001
##    260        1.1929             nan     0.0010    0.0001
##    280        1.1864             nan     0.0010    0.0001
##    300        1.1802             nan     0.0010    0.0001
##    320        1.1743             nan     0.0010    0.0001
##    340        1.1685             nan     0.0010    0.0001
##    360        1.1627             nan     0.0010    0.0001
##    380        1.1571             nan     0.0010    0.0001
##    400        1.1517             nan     0.0010    0.0001
##    420        1.1464             nan     0.0010    0.0001
##    440        1.1410             nan     0.0010    0.0001
##    460        1.1361             nan     0.0010    0.0001
##    480        1.1313             nan     0.0010    0.0001
##    500        1.1265             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2932             nan     0.0010    0.0002
##      4        1.2927             nan     0.0010    0.0003
##      5        1.2922             nan     0.0010    0.0002
##      6        1.2918             nan     0.0010    0.0002
##      7        1.2913             nan     0.0010    0.0002
##      8        1.2908             nan     0.0010    0.0002
##      9        1.2903             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2852             nan     0.0010    0.0002
##     40        1.2758             nan     0.0010    0.0002
##     60        1.2669             nan     0.0010    0.0002
##     80        1.2584             nan     0.0010    0.0002
##    100        1.2502             nan     0.0010    0.0002
##    120        1.2421             nan     0.0010    0.0002
##    140        1.2344             nan     0.0010    0.0002
##    160        1.2269             nan     0.0010    0.0002
##    180        1.2195             nan     0.0010    0.0002
##    200        1.2126             nan     0.0010    0.0002
##    220        1.2058             nan     0.0010    0.0002
##    240        1.1990             nan     0.0010    0.0001
##    260        1.1924             nan     0.0010    0.0001
##    280        1.1860             nan     0.0010    0.0001
##    300        1.1797             nan     0.0010    0.0001
##    320        1.1739             nan     0.0010    0.0001
##    340        1.1679             nan     0.0010    0.0001
##    360        1.1622             nan     0.0010    0.0001
##    380        1.1567             nan     0.0010    0.0001
##    400        1.1512             nan     0.0010    0.0001
##    420        1.1458             nan     0.0010    0.0001
##    440        1.1406             nan     0.0010    0.0001
##    460        1.1355             nan     0.0010    0.0001
##    480        1.1306             nan     0.0010    0.0001
##    500        1.1259             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2934             nan     0.0010    0.0002
##      3        1.2929             nan     0.0010    0.0003
##      4        1.2923             nan     0.0010    0.0002
##      5        1.2918             nan     0.0010    0.0002
##      6        1.2913             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0003
##      8        1.2902             nan     0.0010    0.0002
##      9        1.2896             nan     0.0010    0.0003
##     10        1.2890             nan     0.0010    0.0002
##     20        1.2837             nan     0.0010    0.0003
##     40        1.2730             nan     0.0010    0.0002
##     60        1.2628             nan     0.0010    0.0002
##     80        1.2529             nan     0.0010    0.0002
##    100        1.2433             nan     0.0010    0.0002
##    120        1.2339             nan     0.0010    0.0002
##    140        1.2248             nan     0.0010    0.0002
##    160        1.2162             nan     0.0010    0.0002
##    180        1.2077             nan     0.0010    0.0002
##    200        1.1995             nan     0.0010    0.0002
##    220        1.1914             nan     0.0010    0.0002
##    240        1.1835             nan     0.0010    0.0002
##    260        1.1759             nan     0.0010    0.0002
##    280        1.1686             nan     0.0010    0.0001
##    300        1.1614             nan     0.0010    0.0002
##    320        1.1544             nan     0.0010    0.0002
##    340        1.1477             nan     0.0010    0.0001
##    360        1.1413             nan     0.0010    0.0001
##    380        1.1349             nan     0.0010    0.0001
##    400        1.1286             nan     0.0010    0.0001
##    420        1.1229             nan     0.0010    0.0001
##    440        1.1171             nan     0.0010    0.0001
##    460        1.1115             nan     0.0010    0.0001
##    480        1.1059             nan     0.0010    0.0001
##    500        1.1003             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2939             nan     0.0010    0.0002
##      2        1.2933             nan     0.0010    0.0002
##      3        1.2927             nan     0.0010    0.0003
##      4        1.2922             nan     0.0010    0.0002
##      5        1.2917             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2905             nan     0.0010    0.0002
##      8        1.2899             nan     0.0010    0.0003
##      9        1.2893             nan     0.0010    0.0003
##     10        1.2887             nan     0.0010    0.0003
##     20        1.2831             nan     0.0010    0.0002
##     40        1.2729             nan     0.0010    0.0002
##     60        1.2628             nan     0.0010    0.0002
##     80        1.2527             nan     0.0010    0.0002
##    100        1.2429             nan     0.0010    0.0002
##    120        1.2336             nan     0.0010    0.0002
##    140        1.2248             nan     0.0010    0.0002
##    160        1.2159             nan     0.0010    0.0002
##    180        1.2072             nan     0.0010    0.0002
##    200        1.1989             nan     0.0010    0.0001
##    220        1.1909             nan     0.0010    0.0001
##    240        1.1831             nan     0.0010    0.0001
##    260        1.1752             nan     0.0010    0.0002
##    280        1.1681             nan     0.0010    0.0002
##    300        1.1611             nan     0.0010    0.0001
##    320        1.1542             nan     0.0010    0.0001
##    340        1.1475             nan     0.0010    0.0001
##    360        1.1411             nan     0.0010    0.0002
##    380        1.1348             nan     0.0010    0.0001
##    400        1.1287             nan     0.0010    0.0001
##    420        1.1226             nan     0.0010    0.0001
##    440        1.1165             nan     0.0010    0.0001
##    460        1.1108             nan     0.0010    0.0001
##    480        1.1052             nan     0.0010    0.0001
##    500        1.0998             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0003
##      2        1.2935             nan     0.0010    0.0003
##      3        1.2930             nan     0.0010    0.0002
##      4        1.2924             nan     0.0010    0.0002
##      5        1.2919             nan     0.0010    0.0002
##      6        1.2914             nan     0.0010    0.0003
##      7        1.2908             nan     0.0010    0.0003
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2897             nan     0.0010    0.0002
##     10        1.2892             nan     0.0010    0.0002
##     20        1.2837             nan     0.0010    0.0003
##     40        1.2729             nan     0.0010    0.0002
##     60        1.2627             nan     0.0010    0.0002
##     80        1.2527             nan     0.0010    0.0002
##    100        1.2432             nan     0.0010    0.0002
##    120        1.2338             nan     0.0010    0.0002
##    140        1.2249             nan     0.0010    0.0001
##    160        1.2163             nan     0.0010    0.0002
##    180        1.2079             nan     0.0010    0.0002
##    200        1.1995             nan     0.0010    0.0002
##    220        1.1918             nan     0.0010    0.0002
##    240        1.1840             nan     0.0010    0.0002
##    260        1.1764             nan     0.0010    0.0001
##    280        1.1691             nan     0.0010    0.0001
##    300        1.1619             nan     0.0010    0.0002
##    320        1.1550             nan     0.0010    0.0002
##    340        1.1482             nan     0.0010    0.0002
##    360        1.1415             nan     0.0010    0.0002
##    380        1.1352             nan     0.0010    0.0001
##    400        1.1289             nan     0.0010    0.0001
##    420        1.1227             nan     0.0010    0.0001
##    440        1.1167             nan     0.0010    0.0001
##    460        1.1109             nan     0.0010    0.0001
##    480        1.1054             nan     0.0010    0.0001
##    500        1.1000             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2603             nan     0.1000    0.0151
##      2        1.2299             nan     0.1000    0.0132
##      3        1.2058             nan     0.1000    0.0107
##      4        1.1846             nan     0.1000    0.0082
##      5        1.1660             nan     0.1000    0.0064
##      6        1.1480             nan     0.1000    0.0066
##      7        1.1328             nan     0.1000    0.0063
##      8        1.1160             nan     0.1000    0.0052
##      9        1.1025             nan     0.1000    0.0056
##     10        1.0922             nan     0.1000    0.0042
##     20        1.0031             nan     0.1000    0.0032
##     40        0.9187             nan     0.1000    0.0017
##     60        0.8847             nan     0.1000    0.0001
##     80        0.8610             nan     0.1000   -0.0008
##    100        0.8423             nan     0.1000   -0.0011
##    120        0.8273             nan     0.1000   -0.0004
##    140        0.8166             nan     0.1000   -0.0003
##    160        0.8049             nan     0.1000   -0.0000
##    180        0.7975             nan     0.1000   -0.0012
##    200        0.7921             nan     0.1000   -0.0008
##    220        0.7840             nan     0.1000   -0.0010
##    240        0.7760             nan     0.1000   -0.0005
##    260        0.7687             nan     0.1000   -0.0011
##    280        0.7607             nan     0.1000   -0.0012
##    300        0.7536             nan     0.1000   -0.0003
##    320        0.7468             nan     0.1000   -0.0014
##    340        0.7405             nan     0.1000   -0.0007
##    360        0.7347             nan     0.1000   -0.0005
##    380        0.7280             nan     0.1000   -0.0004
##    400        0.7261             nan     0.1000   -0.0002
##    420        0.7224             nan     0.1000   -0.0006
##    440        0.7188             nan     0.1000   -0.0012
##    460        0.7143             nan     0.1000   -0.0016
##    480        0.7090             nan     0.1000   -0.0007
##    500        0.7061             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2612             nan     0.1000    0.0166
##      2        1.2355             nan     0.1000    0.0138
##      3        1.2077             nan     0.1000    0.0113
##      4        1.1878             nan     0.1000    0.0085
##      5        1.1717             nan     0.1000    0.0058
##      6        1.1565             nan     0.1000    0.0076
##      7        1.1384             nan     0.1000    0.0072
##      8        1.1221             nan     0.1000    0.0066
##      9        1.1092             nan     0.1000    0.0049
##     10        1.0964             nan     0.1000    0.0043
##     20        1.0037             nan     0.1000    0.0014
##     40        0.9268             nan     0.1000    0.0018
##     60        0.8839             nan     0.1000   -0.0009
##     80        0.8596             nan     0.1000   -0.0004
##    100        0.8404             nan     0.1000   -0.0015
##    120        0.8289             nan     0.1000    0.0000
##    140        0.8178             nan     0.1000   -0.0014
##    160        0.8057             nan     0.1000   -0.0003
##    180        0.7983             nan     0.1000   -0.0015
##    200        0.7902             nan     0.1000   -0.0018
##    220        0.7829             nan     0.1000   -0.0009
##    240        0.7752             nan     0.1000   -0.0005
##    260        0.7687             nan     0.1000   -0.0011
##    280        0.7622             nan     0.1000   -0.0012
##    300        0.7549             nan     0.1000   -0.0013
##    320        0.7471             nan     0.1000   -0.0013
##    340        0.7392             nan     0.1000   -0.0004
##    360        0.7342             nan     0.1000   -0.0018
##    380        0.7279             nan     0.1000   -0.0018
##    400        0.7235             nan     0.1000   -0.0004
##    420        0.7168             nan     0.1000   -0.0005
##    440        0.7097             nan     0.1000   -0.0007
##    460        0.7055             nan     0.1000   -0.0011
##    480        0.7028             nan     0.1000   -0.0006
##    500        0.6978             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2592             nan     0.1000    0.0171
##      2        1.2324             nan     0.1000    0.0132
##      3        1.2086             nan     0.1000    0.0105
##      4        1.1892             nan     0.1000    0.0046
##      5        1.1667             nan     0.1000    0.0107
##      6        1.1488             nan     0.1000    0.0074
##      7        1.1343             nan     0.1000    0.0059
##      8        1.1196             nan     0.1000    0.0049
##      9        1.1079             nan     0.1000    0.0052
##     10        1.0942             nan     0.1000    0.0034
##     20        1.0021             nan     0.1000    0.0001
##     40        0.9234             nan     0.1000    0.0006
##     60        0.8846             nan     0.1000    0.0002
##     80        0.8591             nan     0.1000   -0.0014
##    100        0.8397             nan     0.1000   -0.0007
##    120        0.8297             nan     0.1000   -0.0013
##    140        0.8165             nan     0.1000   -0.0010
##    160        0.8076             nan     0.1000   -0.0005
##    180        0.7968             nan     0.1000   -0.0005
##    200        0.7884             nan     0.1000   -0.0010
##    220        0.7797             nan     0.1000   -0.0008
##    240        0.7730             nan     0.1000   -0.0012
##    260        0.7671             nan     0.1000   -0.0007
##    280        0.7598             nan     0.1000   -0.0006
##    300        0.7521             nan     0.1000   -0.0012
##    320        0.7456             nan     0.1000   -0.0011
##    340        0.7411             nan     0.1000   -0.0013
##    360        0.7348             nan     0.1000   -0.0005
##    380        0.7287             nan     0.1000   -0.0008
##    400        0.7238             nan     0.1000   -0.0016
##    420        0.7166             nan     0.1000   -0.0002
##    440        0.7117             nan     0.1000   -0.0011
##    460        0.7092             nan     0.1000   -0.0004
##    480        0.7041             nan     0.1000   -0.0004
##    500        0.6970             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2499             nan     0.1000    0.0221
##      2        1.2095             nan     0.1000    0.0179
##      3        1.1830             nan     0.1000    0.0111
##      4        1.1528             nan     0.1000    0.0136
##      5        1.1292             nan     0.1000    0.0110
##      6        1.1078             nan     0.1000    0.0085
##      7        1.0860             nan     0.1000    0.0092
##      8        1.0733             nan     0.1000    0.0051
##      9        1.0559             nan     0.1000    0.0047
##     10        1.0379             nan     0.1000    0.0082
##     20        0.9409             nan     0.1000    0.0011
##     40        0.8595             nan     0.1000   -0.0005
##     60        0.8104             nan     0.1000   -0.0024
##     80        0.7759             nan     0.1000   -0.0012
##    100        0.7471             nan     0.1000    0.0000
##    120        0.7244             nan     0.1000   -0.0008
##    140        0.7059             nan     0.1000    0.0000
##    160        0.6822             nan     0.1000   -0.0011
##    180        0.6636             nan     0.1000   -0.0009
##    200        0.6444             nan     0.1000   -0.0003
##    220        0.6251             nan     0.1000   -0.0013
##    240        0.6067             nan     0.1000   -0.0008
##    260        0.5895             nan     0.1000   -0.0014
##    280        0.5704             nan     0.1000   -0.0008
##    300        0.5547             nan     0.1000   -0.0000
##    320        0.5410             nan     0.1000   -0.0011
##    340        0.5262             nan     0.1000   -0.0022
##    360        0.5105             nan     0.1000   -0.0010
##    380        0.4978             nan     0.1000   -0.0016
##    400        0.4907             nan     0.1000   -0.0009
##    420        0.4809             nan     0.1000   -0.0014
##    440        0.4696             nan     0.1000   -0.0014
##    460        0.4580             nan     0.1000   -0.0003
##    480        0.4479             nan     0.1000   -0.0010
##    500        0.4385             nan     0.1000   -0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2482             nan     0.1000    0.0190
##      2        1.2137             nan     0.1000    0.0180
##      3        1.1783             nan     0.1000    0.0148
##      4        1.1497             nan     0.1000    0.0114
##      5        1.1213             nan     0.1000    0.0076
##      6        1.0985             nan     0.1000    0.0098
##      7        1.0782             nan     0.1000    0.0079
##      8        1.0619             nan     0.1000    0.0051
##      9        1.0484             nan     0.1000    0.0033
##     10        1.0311             nan     0.1000    0.0069
##     20        0.9401             nan     0.1000    0.0020
##     40        0.8604             nan     0.1000   -0.0009
##     60        0.8086             nan     0.1000   -0.0011
##     80        0.7747             nan     0.1000   -0.0012
##    100        0.7445             nan     0.1000   -0.0024
##    120        0.7219             nan     0.1000    0.0000
##    140        0.6984             nan     0.1000   -0.0019
##    160        0.6782             nan     0.1000   -0.0014
##    180        0.6601             nan     0.1000   -0.0011
##    200        0.6418             nan     0.1000   -0.0013
##    220        0.6264             nan     0.1000   -0.0019
##    240        0.6087             nan     0.1000   -0.0009
##    260        0.5925             nan     0.1000   -0.0009
##    280        0.5790             nan     0.1000   -0.0007
##    300        0.5679             nan     0.1000   -0.0003
##    320        0.5539             nan     0.1000   -0.0012
##    340        0.5377             nan     0.1000   -0.0009
##    360        0.5249             nan     0.1000   -0.0005
##    380        0.5100             nan     0.1000   -0.0012
##    400        0.4997             nan     0.1000   -0.0012
##    420        0.4897             nan     0.1000   -0.0011
##    440        0.4763             nan     0.1000   -0.0017
##    460        0.4627             nan     0.1000   -0.0009
##    480        0.4534             nan     0.1000   -0.0005
##    500        0.4440             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2474             nan     0.1000    0.0204
##      2        1.2068             nan     0.1000    0.0155
##      3        1.1718             nan     0.1000    0.0152
##      4        1.1451             nan     0.1000    0.0143
##      5        1.1202             nan     0.1000    0.0099
##      6        1.0979             nan     0.1000    0.0080
##      7        1.0799             nan     0.1000    0.0079
##      8        1.0606             nan     0.1000    0.0078
##      9        1.0438             nan     0.1000    0.0034
##     10        1.0271             nan     0.1000    0.0066
##     20        0.9344             nan     0.1000    0.0019
##     40        0.8555             nan     0.1000   -0.0011
##     60        0.8087             nan     0.1000   -0.0014
##     80        0.7762             nan     0.1000   -0.0010
##    100        0.7510             nan     0.1000   -0.0018
##    120        0.7297             nan     0.1000   -0.0020
##    140        0.7101             nan     0.1000   -0.0020
##    160        0.6885             nan     0.1000   -0.0023
##    180        0.6685             nan     0.1000   -0.0010
##    200        0.6497             nan     0.1000   -0.0008
##    220        0.6300             nan     0.1000   -0.0015
##    240        0.6130             nan     0.1000   -0.0007
##    260        0.5987             nan     0.1000   -0.0014
##    280        0.5841             nan     0.1000   -0.0012
##    300        0.5706             nan     0.1000   -0.0010
##    320        0.5595             nan     0.1000   -0.0007
##    340        0.5450             nan     0.1000   -0.0007
##    360        0.5334             nan     0.1000   -0.0011
##    380        0.5199             nan     0.1000   -0.0007
##    400        0.5082             nan     0.1000   -0.0012
##    420        0.4966             nan     0.1000   -0.0006
##    440        0.4843             nan     0.1000   -0.0008
##    460        0.4723             nan     0.1000   -0.0007
##    480        0.4590             nan     0.1000   -0.0009
##    500        0.4491             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2437             nan     0.1000    0.0233
##      2        1.2022             nan     0.1000    0.0171
##      3        1.1612             nan     0.1000    0.0148
##      4        1.1291             nan     0.1000    0.0127
##      5        1.1039             nan     0.1000    0.0061
##      6        1.0756             nan     0.1000    0.0105
##      7        1.0588             nan     0.1000    0.0054
##      8        1.0371             nan     0.1000    0.0080
##      9        1.0211             nan     0.1000    0.0050
##     10        1.0082             nan     0.1000    0.0052
##     20        0.9041             nan     0.1000    0.0006
##     40        0.8097             nan     0.1000   -0.0003
##     60        0.7529             nan     0.1000   -0.0017
##     80        0.7102             nan     0.1000   -0.0010
##    100        0.6763             nan     0.1000   -0.0018
##    120        0.6362             nan     0.1000   -0.0019
##    140        0.6029             nan     0.1000   -0.0009
##    160        0.5717             nan     0.1000   -0.0018
##    180        0.5466             nan     0.1000   -0.0000
##    200        0.5210             nan     0.1000   -0.0006
##    220        0.4974             nan     0.1000   -0.0022
##    240        0.4726             nan     0.1000   -0.0012
##    260        0.4529             nan     0.1000   -0.0001
##    280        0.4344             nan     0.1000   -0.0003
##    300        0.4197             nan     0.1000   -0.0010
##    320        0.4034             nan     0.1000   -0.0010
##    340        0.3857             nan     0.1000   -0.0005
##    360        0.3702             nan     0.1000   -0.0011
##    380        0.3557             nan     0.1000   -0.0000
##    400        0.3419             nan     0.1000   -0.0008
##    420        0.3279             nan     0.1000   -0.0005
##    440        0.3146             nan     0.1000   -0.0005
##    460        0.3021             nan     0.1000   -0.0014
##    480        0.2907             nan     0.1000   -0.0007
##    500        0.2786             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2437             nan     0.1000    0.0237
##      2        1.1979             nan     0.1000    0.0172
##      3        1.1534             nan     0.1000    0.0190
##      4        1.1187             nan     0.1000    0.0147
##      5        1.0952             nan     0.1000    0.0091
##      6        1.0703             nan     0.1000    0.0085
##      7        1.0473             nan     0.1000    0.0090
##      8        1.0263             nan     0.1000    0.0088
##      9        1.0074             nan     0.1000    0.0056
##     10        0.9948             nan     0.1000    0.0036
##     20        0.8935             nan     0.1000   -0.0019
##     40        0.8005             nan     0.1000   -0.0013
##     60        0.7512             nan     0.1000   -0.0019
##     80        0.7116             nan     0.1000   -0.0007
##    100        0.6761             nan     0.1000   -0.0010
##    120        0.6400             nan     0.1000   -0.0016
##    140        0.6092             nan     0.1000   -0.0020
##    160        0.5808             nan     0.1000   -0.0009
##    180        0.5585             nan     0.1000   -0.0012
##    200        0.5367             nan     0.1000   -0.0018
##    220        0.5095             nan     0.1000   -0.0017
##    240        0.4858             nan     0.1000   -0.0009
##    260        0.4684             nan     0.1000   -0.0014
##    280        0.4498             nan     0.1000   -0.0016
##    300        0.4298             nan     0.1000   -0.0003
##    320        0.4132             nan     0.1000   -0.0004
##    340        0.3944             nan     0.1000   -0.0013
##    360        0.3792             nan     0.1000   -0.0008
##    380        0.3651             nan     0.1000   -0.0001
##    400        0.3487             nan     0.1000   -0.0007
##    420        0.3367             nan     0.1000   -0.0009
##    440        0.3260             nan     0.1000   -0.0010
##    460        0.3141             nan     0.1000   -0.0009
##    480        0.3026             nan     0.1000   -0.0007
##    500        0.2925             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2359             nan     0.1000    0.0247
##      2        1.1973             nan     0.1000    0.0146
##      3        1.1614             nan     0.1000    0.0159
##      4        1.1285             nan     0.1000    0.0139
##      5        1.0977             nan     0.1000    0.0144
##      6        1.0726             nan     0.1000    0.0100
##      7        1.0527             nan     0.1000    0.0078
##      8        1.0352             nan     0.1000    0.0052
##      9        1.0134             nan     0.1000    0.0091
##     10        0.9943             nan     0.1000    0.0047
##     20        0.8969             nan     0.1000    0.0007
##     40        0.8081             nan     0.1000   -0.0007
##     60        0.7537             nan     0.1000   -0.0007
##     80        0.7178             nan     0.1000   -0.0028
##    100        0.6802             nan     0.1000   -0.0006
##    120        0.6459             nan     0.1000   -0.0030
##    140        0.6105             nan     0.1000   -0.0015
##    160        0.5795             nan     0.1000   -0.0014
##    180        0.5519             nan     0.1000   -0.0013
##    200        0.5269             nan     0.1000   -0.0004
##    220        0.5047             nan     0.1000   -0.0005
##    240        0.4875             nan     0.1000   -0.0012
##    260        0.4657             nan     0.1000   -0.0012
##    280        0.4429             nan     0.1000   -0.0012
##    300        0.4233             nan     0.1000   -0.0009
##    320        0.4079             nan     0.1000   -0.0011
##    340        0.3894             nan     0.1000   -0.0008
##    360        0.3760             nan     0.1000   -0.0005
##    380        0.3620             nan     0.1000   -0.0006
##    400        0.3460             nan     0.1000    0.0001
##    420        0.3306             nan     0.1000   -0.0017
##    440        0.3191             nan     0.1000   -0.0007
##    460        0.3054             nan     0.1000   -0.0008
##    480        0.2931             nan     0.1000   -0.0006
##    500        0.2817             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2291             nan     0.2000    0.0304
##      2        1.1919             nan     0.2000    0.0155
##      3        1.1563             nan     0.2000    0.0159
##      4        1.1235             nan     0.2000    0.0137
##      5        1.1010             nan     0.2000    0.0089
##      6        1.0765             nan     0.2000    0.0098
##      7        1.0631             nan     0.2000    0.0022
##      8        1.0431             nan     0.2000    0.0079
##      9        1.0226             nan     0.2000    0.0080
##     10        1.0131             nan     0.2000    0.0046
##     20        0.9315             nan     0.2000   -0.0003
##     40        0.8701             nan     0.2000   -0.0026
##     60        0.8358             nan     0.2000   -0.0016
##     80        0.8122             nan     0.2000   -0.0012
##    100        0.7914             nan     0.2000   -0.0025
##    120        0.7755             nan     0.2000   -0.0023
##    140        0.7617             nan     0.2000   -0.0018
##    160        0.7478             nan     0.2000   -0.0009
##    180        0.7412             nan     0.2000   -0.0007
##    200        0.7330             nan     0.2000   -0.0009
##    220        0.7178             nan     0.2000   -0.0001
##    240        0.7130             nan     0.2000   -0.0010
##    260        0.7057             nan     0.2000   -0.0021
##    280        0.6982             nan     0.2000   -0.0032
##    300        0.6878             nan     0.2000   -0.0019
##    320        0.6823             nan     0.2000   -0.0026
##    340        0.6765             nan     0.2000   -0.0027
##    360        0.6659             nan     0.2000   -0.0007
##    380        0.6612             nan     0.2000   -0.0023
##    400        0.6555             nan     0.2000   -0.0025
##    420        0.6501             nan     0.2000   -0.0017
##    440        0.6415             nan     0.2000   -0.0020
##    460        0.6392             nan     0.2000   -0.0005
##    480        0.6314             nan     0.2000   -0.0006
##    500        0.6250             nan     0.2000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2248             nan     0.2000    0.0273
##      2        1.1789             nan     0.2000    0.0184
##      3        1.1471             nan     0.2000    0.0144
##      4        1.1128             nan     0.2000    0.0141
##      5        1.0899             nan     0.2000    0.0100
##      6        1.0682             nan     0.2000    0.0088
##      7        1.0477             nan     0.2000    0.0054
##      8        1.0302             nan     0.2000    0.0068
##      9        1.0200             nan     0.2000    0.0031
##     10        1.0063             nan     0.2000    0.0040
##     20        0.9252             nan     0.2000   -0.0010
##     40        0.8687             nan     0.2000   -0.0022
##     60        0.8377             nan     0.2000   -0.0006
##     80        0.8126             nan     0.2000   -0.0021
##    100        0.7959             nan     0.2000   -0.0011
##    120        0.7778             nan     0.2000   -0.0030
##    140        0.7622             nan     0.2000   -0.0011
##    160        0.7490             nan     0.2000   -0.0007
##    180        0.7367             nan     0.2000   -0.0020
##    200        0.7284             nan     0.2000   -0.0014
##    220        0.7193             nan     0.2000   -0.0006
##    240        0.7114             nan     0.2000   -0.0001
##    260        0.6997             nan     0.2000   -0.0003
##    280        0.6920             nan     0.2000   -0.0022
##    300        0.6846             nan     0.2000   -0.0013
##    320        0.6774             nan     0.2000   -0.0017
##    340        0.6744             nan     0.2000   -0.0026
##    360        0.6650             nan     0.2000   -0.0005
##    380        0.6610             nan     0.2000   -0.0026
##    400        0.6546             nan     0.2000   -0.0020
##    420        0.6474             nan     0.2000   -0.0014
##    440        0.6400             nan     0.2000   -0.0023
##    460        0.6360             nan     0.2000   -0.0026
##    480        0.6315             nan     0.2000   -0.0020
##    500        0.6270             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2302             nan     0.2000    0.0286
##      2        1.1861             nan     0.2000    0.0131
##      3        1.1541             nan     0.2000    0.0143
##      4        1.1192             nan     0.2000    0.0147
##      5        1.0951             nan     0.2000    0.0110
##      6        1.0714             nan     0.2000    0.0080
##      7        1.0491             nan     0.2000    0.0079
##      8        1.0336             nan     0.2000    0.0025
##      9        1.0160             nan     0.2000    0.0052
##     10        1.0015             nan     0.2000    0.0057
##     20        0.9323             nan     0.2000   -0.0001
##     40        0.8653             nan     0.2000    0.0007
##     60        0.8369             nan     0.2000   -0.0013
##     80        0.8115             nan     0.2000   -0.0010
##    100        0.7916             nan     0.2000   -0.0020
##    120        0.7769             nan     0.2000   -0.0012
##    140        0.7596             nan     0.2000   -0.0018
##    160        0.7476             nan     0.2000   -0.0028
##    180        0.7366             nan     0.2000   -0.0016
##    200        0.7287             nan     0.2000   -0.0012
##    220        0.7223             nan     0.2000   -0.0021
##    240        0.7107             nan     0.2000   -0.0002
##    260        0.7002             nan     0.2000   -0.0005
##    280        0.6974             nan     0.2000   -0.0026
##    300        0.6888             nan     0.2000   -0.0020
##    320        0.6766             nan     0.2000   -0.0003
##    340        0.6709             nan     0.2000   -0.0007
##    360        0.6615             nan     0.2000   -0.0016
##    380        0.6549             nan     0.2000   -0.0011
##    400        0.6488             nan     0.2000   -0.0021
##    420        0.6414             nan     0.2000   -0.0021
##    440        0.6338             nan     0.2000   -0.0016
##    460        0.6271             nan     0.2000   -0.0010
##    480        0.6230             nan     0.2000   -0.0016
##    500        0.6170             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2072             nan     0.2000    0.0359
##      2        1.1400             nan     0.2000    0.0326
##      3        1.0897             nan     0.2000    0.0182
##      4        1.0491             nan     0.2000    0.0179
##      5        1.0191             nan     0.2000    0.0105
##      6        0.9972             nan     0.2000    0.0081
##      7        0.9808             nan     0.2000    0.0009
##      8        0.9669             nan     0.2000    0.0011
##      9        0.9579             nan     0.2000    0.0004
##     10        0.9461             nan     0.2000   -0.0001
##     20        0.8593             nan     0.2000   -0.0031
##     40        0.7708             nan     0.2000   -0.0009
##     60        0.7231             nan     0.2000   -0.0009
##     80        0.6777             nan     0.2000   -0.0072
##    100        0.6361             nan     0.2000   -0.0025
##    120        0.6023             nan     0.2000   -0.0026
##    140        0.5740             nan     0.2000   -0.0029
##    160        0.5442             nan     0.2000   -0.0024
##    180        0.5237             nan     0.2000   -0.0019
##    200        0.4971             nan     0.2000   -0.0010
##    220        0.4745             nan     0.2000   -0.0022
##    240        0.4527             nan     0.2000   -0.0011
##    260        0.4332             nan     0.2000   -0.0014
##    280        0.4172             nan     0.2000   -0.0022
##    300        0.3983             nan     0.2000   -0.0013
##    320        0.3807             nan     0.2000   -0.0021
##    340        0.3676             nan     0.2000   -0.0020
##    360        0.3544             nan     0.2000   -0.0012
##    380        0.3395             nan     0.2000   -0.0011
##    400        0.3282             nan     0.2000   -0.0002
##    420        0.3149             nan     0.2000   -0.0006
##    440        0.3022             nan     0.2000   -0.0004
##    460        0.2939             nan     0.2000   -0.0011
##    480        0.2819             nan     0.2000   -0.0014
##    500        0.2687             nan     0.2000   -0.0018
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2030             nan     0.2000    0.0381
##      2        1.1397             nan     0.2000    0.0290
##      3        1.0979             nan     0.2000    0.0200
##      4        1.0580             nan     0.2000    0.0146
##      5        1.0247             nan     0.2000    0.0085
##      6        0.9977             nan     0.2000    0.0091
##      7        0.9786             nan     0.2000    0.0063
##      8        0.9602             nan     0.2000    0.0055
##      9        0.9478             nan     0.2000    0.0002
##     10        0.9344             nan     0.2000    0.0019
##     20        0.8572             nan     0.2000    0.0022
##     40        0.7849             nan     0.2000   -0.0028
##     60        0.7366             nan     0.2000   -0.0037
##     80        0.6915             nan     0.2000   -0.0022
##    100        0.6463             nan     0.2000   -0.0017
##    120        0.6182             nan     0.2000   -0.0024
##    140        0.5870             nan     0.2000   -0.0025
##    160        0.5591             nan     0.2000   -0.0030
##    180        0.5311             nan     0.2000   -0.0015
##    200        0.5099             nan     0.2000   -0.0015
##    220        0.4873             nan     0.2000   -0.0033
##    240        0.4684             nan     0.2000   -0.0032
##    260        0.4459             nan     0.2000   -0.0017
##    280        0.4236             nan     0.2000   -0.0010
##    300        0.4045             nan     0.2000   -0.0035
##    320        0.3872             nan     0.2000   -0.0003
##    340        0.3751             nan     0.2000   -0.0029
##    360        0.3546             nan     0.2000   -0.0003
##    380        0.3419             nan     0.2000   -0.0007
##    400        0.3289             nan     0.2000   -0.0005
##    420        0.3138             nan     0.2000   -0.0010
##    440        0.3035             nan     0.2000   -0.0013
##    460        0.2902             nan     0.2000   -0.0010
##    480        0.2793             nan     0.2000   -0.0009
##    500        0.2713             nan     0.2000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2134             nan     0.2000    0.0414
##      2        1.1475             nan     0.2000    0.0279
##      3        1.0974             nan     0.2000    0.0185
##      4        1.0577             nan     0.2000    0.0137
##      5        1.0243             nan     0.2000    0.0075
##      6        0.9991             nan     0.2000    0.0059
##      7        0.9829             nan     0.2000    0.0042
##      8        0.9648             nan     0.2000    0.0051
##      9        0.9450             nan     0.2000    0.0059
##     10        0.9293             nan     0.2000    0.0052
##     20        0.8535             nan     0.2000   -0.0016
##     40        0.7836             nan     0.2000   -0.0023
##     60        0.7268             nan     0.2000   -0.0009
##     80        0.6820             nan     0.2000   -0.0031
##    100        0.6481             nan     0.2000   -0.0016
##    120        0.6164             nan     0.2000   -0.0047
##    140        0.5800             nan     0.2000   -0.0014
##    160        0.5435             nan     0.2000    0.0007
##    180        0.5179             nan     0.2000   -0.0014
##    200        0.4960             nan     0.2000   -0.0024
##    220        0.4754             nan     0.2000   -0.0009
##    240        0.4590             nan     0.2000   -0.0014
##    260        0.4399             nan     0.2000   -0.0021
##    280        0.4147             nan     0.2000   -0.0004
##    300        0.4063             nan     0.2000   -0.0012
##    320        0.3909             nan     0.2000   -0.0021
##    340        0.3702             nan     0.2000   -0.0009
##    360        0.3573             nan     0.2000   -0.0013
##    380        0.3449             nan     0.2000   -0.0032
##    400        0.3324             nan     0.2000   -0.0005
##    420        0.3197             nan     0.2000   -0.0008
##    440        0.3083             nan     0.2000   -0.0016
##    460        0.2973             nan     0.2000   -0.0012
##    480        0.2853             nan     0.2000   -0.0001
##    500        0.2727             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2005             nan     0.2000    0.0452
##      2        1.1340             nan     0.2000    0.0301
##      3        1.0762             nan     0.2000    0.0237
##      4        1.0319             nan     0.2000    0.0148
##      5        0.9997             nan     0.2000    0.0101
##      6        0.9675             nan     0.2000    0.0111
##      7        0.9435             nan     0.2000    0.0069
##      8        0.9235             nan     0.2000    0.0054
##      9        0.9069             nan     0.2000   -0.0002
##     10        0.8919             nan     0.2000    0.0028
##     20        0.8139             nan     0.2000   -0.0034
##     40        0.7131             nan     0.2000   -0.0007
##     60        0.6373             nan     0.2000   -0.0016
##     80        0.5790             nan     0.2000   -0.0022
##    100        0.5365             nan     0.2000   -0.0034
##    120        0.4985             nan     0.2000   -0.0036
##    140        0.4592             nan     0.2000   -0.0034
##    160        0.4206             nan     0.2000   -0.0022
##    180        0.3853             nan     0.2000   -0.0026
##    200        0.3563             nan     0.2000   -0.0010
##    220        0.3328             nan     0.2000   -0.0025
##    240        0.3068             nan     0.2000   -0.0008
##    260        0.2871             nan     0.2000   -0.0009
##    280        0.2666             nan     0.2000   -0.0024
##    300        0.2474             nan     0.2000   -0.0006
##    320        0.2274             nan     0.2000   -0.0000
##    340        0.2152             nan     0.2000   -0.0019
##    360        0.1991             nan     0.2000   -0.0014
##    380        0.1883             nan     0.2000   -0.0020
##    400        0.1758             nan     0.2000   -0.0009
##    420        0.1663             nan     0.2000   -0.0007
##    440        0.1579             nan     0.2000   -0.0006
##    460        0.1501             nan     0.2000   -0.0006
##    480        0.1414             nan     0.2000   -0.0005
##    500        0.1327             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1992             nan     0.2000    0.0425
##      2        1.1208             nan     0.2000    0.0368
##      3        1.0640             nan     0.2000    0.0288
##      4        1.0268             nan     0.2000    0.0153
##      5        0.9953             nan     0.2000    0.0139
##      6        0.9804             nan     0.2000    0.0014
##      7        0.9663             nan     0.2000   -0.0012
##      8        0.9452             nan     0.2000    0.0055
##      9        0.9273             nan     0.2000   -0.0012
##     10        0.9107             nan     0.2000    0.0007
##     20        0.8217             nan     0.2000   -0.0023
##     40        0.7124             nan     0.2000   -0.0030
##     60        0.6491             nan     0.2000   -0.0027
##     80        0.5864             nan     0.2000   -0.0015
##    100        0.5251             nan     0.2000   -0.0003
##    120        0.4810             nan     0.2000   -0.0031
##    140        0.4324             nan     0.2000   -0.0009
##    160        0.3931             nan     0.2000   -0.0005
##    180        0.3600             nan     0.2000   -0.0003
##    200        0.3305             nan     0.2000   -0.0023
##    220        0.3108             nan     0.2000   -0.0014
##    240        0.2847             nan     0.2000   -0.0020
##    260        0.2668             nan     0.2000   -0.0011
##    280        0.2467             nan     0.2000   -0.0019
##    300        0.2307             nan     0.2000   -0.0012
##    320        0.2174             nan     0.2000   -0.0007
##    340        0.2004             nan     0.2000   -0.0011
##    360        0.1919             nan     0.2000   -0.0017
##    380        0.1799             nan     0.2000   -0.0018
##    400        0.1677             nan     0.2000   -0.0010
##    420        0.1576             nan     0.2000   -0.0002
##    440        0.1479             nan     0.2000   -0.0009
##    460        0.1403             nan     0.2000   -0.0013
##    480        0.1321             nan     0.2000   -0.0008
##    500        0.1239             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1873             nan     0.2000    0.0529
##      2        1.1245             nan     0.2000    0.0252
##      3        1.0770             nan     0.2000    0.0177
##      4        1.0364             nan     0.2000    0.0138
##      5        1.0000             nan     0.2000    0.0094
##      6        0.9700             nan     0.2000    0.0078
##      7        0.9474             nan     0.2000    0.0078
##      8        0.9260             nan     0.2000    0.0067
##      9        0.9104             nan     0.2000    0.0009
##     10        0.8967             nan     0.2000    0.0025
##     20        0.7951             nan     0.2000   -0.0010
##     40        0.6893             nan     0.2000   -0.0039
##     60        0.6149             nan     0.2000   -0.0022
##     80        0.5554             nan     0.2000   -0.0072
##    100        0.5153             nan     0.2000   -0.0019
##    120        0.4668             nan     0.2000   -0.0053
##    140        0.4283             nan     0.2000   -0.0022
##    160        0.3927             nan     0.2000   -0.0012
##    180        0.3667             nan     0.2000   -0.0023
##    200        0.3371             nan     0.2000   -0.0010
##    220        0.3138             nan     0.2000   -0.0013
##    240        0.2946             nan     0.2000   -0.0025
##    260        0.2747             nan     0.2000   -0.0009
##    280        0.2574             nan     0.2000   -0.0018
##    300        0.2394             nan     0.2000   -0.0006
##    320        0.2234             nan     0.2000   -0.0012
##    340        0.2083             nan     0.2000   -0.0005
##    360        0.1949             nan     0.2000   -0.0005
##    380        0.1840             nan     0.2000   -0.0003
##    400        0.1732             nan     0.2000   -0.0014
##    420        0.1625             nan     0.2000   -0.0009
##    440        0.1531             nan     0.2000   -0.0009
##    460        0.1430             nan     0.2000   -0.0007
##    480        0.1344             nan     0.2000   -0.0005
##    500        0.1266             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2075             nan     0.3000    0.0455
##      2        1.1512             nan     0.3000    0.0275
##      3        1.1095             nan     0.3000    0.0126
##      4        1.0829             nan     0.3000    0.0053
##      5        1.0410             nan     0.3000    0.0214
##      6        1.0173             nan     0.3000    0.0103
##      7        0.9980             nan     0.3000    0.0062
##      8        0.9824             nan     0.3000    0.0057
##      9        0.9650             nan     0.3000    0.0074
##     10        0.9558             nan     0.3000   -0.0001
##     20        0.8963             nan     0.3000   -0.0013
##     40        0.8342             nan     0.3000   -0.0040
##     60        0.7955             nan     0.3000   -0.0002
##     80        0.7671             nan     0.3000   -0.0030
##    100        0.7504             nan     0.3000   -0.0043
##    120        0.7436             nan     0.3000   -0.0078
##    140        0.7311             nan     0.3000   -0.0016
##    160        0.7178             nan     0.3000   -0.0020
##    180        0.7035             nan     0.3000   -0.0038
##    200        0.6938             nan     0.3000   -0.0053
##    220        0.6824             nan     0.3000   -0.0016
##    240        0.6669             nan     0.3000   -0.0020
##    260        0.6606             nan     0.3000   -0.0039
##    280        0.6480             nan     0.3000   -0.0007
##    300        0.6330             nan     0.3000   -0.0023
##    320        0.6218             nan     0.3000   -0.0037
##    340        0.6135             nan     0.3000   -0.0010
##    360        0.6060             nan     0.3000   -0.0018
##    380        0.5968             nan     0.3000   -0.0034
##    400        0.5916             nan     0.3000   -0.0026
##    420        0.5781             nan     0.3000   -0.0006
##    440        0.5689             nan     0.3000   -0.0019
##    460        0.5653             nan     0.3000   -0.0017
##    480        0.5560             nan     0.3000   -0.0017
##    500        0.5462             nan     0.3000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2022             nan     0.3000    0.0383
##      2        1.1468             nan     0.3000    0.0184
##      3        1.0830             nan     0.3000    0.0209
##      4        1.0475             nan     0.3000    0.0120
##      5        1.0288             nan     0.3000    0.0053
##      6        1.0021             nan     0.3000    0.0061
##      7        0.9895             nan     0.3000    0.0034
##      8        0.9774             nan     0.3000    0.0042
##      9        0.9675             nan     0.3000    0.0026
##     10        0.9577             nan     0.3000    0.0011
##     20        0.8958             nan     0.3000   -0.0033
##     40        0.8543             nan     0.3000   -0.0026
##     60        0.8191             nan     0.3000   -0.0054
##     80        0.7859             nan     0.3000   -0.0033
##    100        0.7611             nan     0.3000   -0.0043
##    120        0.7433             nan     0.3000   -0.0039
##    140        0.7296             nan     0.3000   -0.0033
##    160        0.7159             nan     0.3000   -0.0005
##    180        0.7080             nan     0.3000   -0.0041
##    200        0.6960             nan     0.3000   -0.0027
##    220        0.6863             nan     0.3000   -0.0054
##    240        0.6749             nan     0.3000   -0.0014
##    260        0.6635             nan     0.3000   -0.0024
##    280        0.6529             nan     0.3000   -0.0019
##    300        0.6414             nan     0.3000   -0.0023
##    320        0.6324             nan     0.3000   -0.0026
##    340        0.6206             nan     0.3000   -0.0026
##    360        0.6151             nan     0.3000   -0.0008
##    380        0.6008             nan     0.3000   -0.0009
##    400        0.5920             nan     0.3000   -0.0035
##    420        0.5821             nan     0.3000   -0.0034
##    440        0.5808             nan     0.3000   -0.0021
##    460        0.5709             nan     0.3000   -0.0016
##    480        0.5635             nan     0.3000   -0.0017
##    500        0.5579             nan     0.3000   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2029             nan     0.3000    0.0425
##      2        1.1540             nan     0.3000    0.0207
##      3        1.1150             nan     0.3000    0.0199
##      4        1.0742             nan     0.3000    0.0195
##      5        1.0487             nan     0.3000    0.0032
##      6        1.0152             nan     0.3000    0.0111
##      7        0.9942             nan     0.3000    0.0075
##      8        0.9790             nan     0.3000    0.0034
##      9        0.9625             nan     0.3000    0.0062
##     10        0.9533             nan     0.3000   -0.0002
##     20        0.8916             nan     0.3000   -0.0072
##     40        0.8410             nan     0.3000   -0.0035
##     60        0.8123             nan     0.3000    0.0002
##     80        0.7842             nan     0.3000   -0.0012
##    100        0.7727             nan     0.3000   -0.0046
##    120        0.7557             nan     0.3000   -0.0025
##    140        0.7371             nan     0.3000   -0.0025
##    160        0.7216             nan     0.3000   -0.0025
##    180        0.7015             nan     0.3000   -0.0024
##    200        0.6931             nan     0.3000   -0.0029
##    220        0.6771             nan     0.3000   -0.0024
##    240        0.6652             nan     0.3000   -0.0036
##    260        0.6518             nan     0.3000   -0.0006
##    280        0.6419             nan     0.3000   -0.0035
##    300        0.6353             nan     0.3000   -0.0044
##    320        0.6298             nan     0.3000   -0.0031
##    340        0.6234             nan     0.3000   -0.0056
##    360        0.6179             nan     0.3000   -0.0009
##    380        0.6074             nan     0.3000   -0.0036
##    400        0.5985             nan     0.3000   -0.0027
##    420        0.5914             nan     0.3000   -0.0027
##    440        0.5866             nan     0.3000   -0.0034
##    460        0.5780             nan     0.3000   -0.0034
##    480        0.5691             nan     0.3000   -0.0032
##    500        0.5657             nan     0.3000   -0.0034
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1747             nan     0.3000    0.0587
##      2        1.0993             nan     0.3000    0.0381
##      3        1.0529             nan     0.3000    0.0105
##      4        1.0104             nan     0.3000    0.0184
##      5        0.9683             nan     0.3000    0.0140
##      6        0.9444             nan     0.3000    0.0053
##      7        0.9246             nan     0.3000    0.0014
##      8        0.9117             nan     0.3000    0.0032
##      9        0.9000             nan     0.3000    0.0012
##     10        0.8866             nan     0.3000    0.0034
##     20        0.8041             nan     0.3000   -0.0054
##     40        0.7379             nan     0.3000   -0.0061
##     60        0.6870             nan     0.3000   -0.0032
##     80        0.6361             nan     0.3000   -0.0018
##    100        0.5913             nan     0.3000   -0.0022
##    120        0.5533             nan     0.3000   -0.0011
##    140        0.5167             nan     0.3000   -0.0045
##    160        0.4861             nan     0.3000   -0.0033
##    180        0.4565             nan     0.3000   -0.0023
##    200        0.4253             nan     0.3000   -0.0025
##    220        0.3959             nan     0.3000   -0.0012
##    240        0.3686             nan     0.3000   -0.0006
##    260        0.3486             nan     0.3000   -0.0013
##    280        0.3288             nan     0.3000   -0.0024
##    300        0.3122             nan     0.3000   -0.0017
##    320        0.2929             nan     0.3000   -0.0010
##    340        0.2787             nan     0.3000   -0.0006
##    360        0.2614             nan     0.3000   -0.0033
##    380        0.2471             nan     0.3000   -0.0019
##    400        0.2311             nan     0.3000   -0.0006
##    420        0.2193             nan     0.3000   -0.0023
##    440        0.2087             nan     0.3000   -0.0006
##    460        0.1980             nan     0.3000   -0.0007
##    480        0.1894             nan     0.3000   -0.0012
##    500        0.1806             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1784             nan     0.3000    0.0525
##      2        1.1178             nan     0.3000    0.0209
##      3        1.0500             nan     0.3000    0.0289
##      4        1.0145             nan     0.3000    0.0125
##      5        0.9825             nan     0.3000    0.0102
##      6        0.9546             nan     0.3000    0.0053
##      7        0.9341             nan     0.3000    0.0049
##      8        0.9129             nan     0.3000    0.0073
##      9        0.9024             nan     0.3000   -0.0019
##     10        0.8930             nan     0.3000    0.0016
##     20        0.8171             nan     0.3000    0.0010
##     40        0.7297             nan     0.3000   -0.0068
##     60        0.6712             nan     0.3000   -0.0042
##     80        0.6134             nan     0.3000   -0.0023
##    100        0.5664             nan     0.3000   -0.0028
##    120        0.5169             nan     0.3000   -0.0033
##    140        0.4936             nan     0.3000   -0.0043
##    160        0.4661             nan     0.3000   -0.0048
##    180        0.4341             nan     0.3000   -0.0033
##    200        0.4049             nan     0.3000   -0.0042
##    220        0.3799             nan     0.3000   -0.0023
##    240        0.3530             nan     0.3000   -0.0030
##    260        0.3355             nan     0.3000   -0.0019
##    280        0.3208             nan     0.3000   -0.0041
##    300        0.3020             nan     0.3000   -0.0043
##    320        0.2855             nan     0.3000   -0.0030
##    340        0.2675             nan     0.3000   -0.0019
##    360        0.2536             nan     0.3000   -0.0007
##    380        0.2417             nan     0.3000   -0.0023
##    400        0.2311             nan     0.3000   -0.0017
##    420        0.2191             nan     0.3000   -0.0006
##    440        0.2055             nan     0.3000   -0.0021
##    460        0.1955             nan     0.3000   -0.0015
##    480        0.1840             nan     0.3000   -0.0002
##    500        0.1763             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1669             nan     0.3000    0.0447
##      2        1.0854             nan     0.3000    0.0377
##      3        1.0371             nan     0.3000    0.0164
##      4        1.0069             nan     0.3000    0.0103
##      5        0.9698             nan     0.3000    0.0166
##      6        0.9500             nan     0.3000    0.0055
##      7        0.9297             nan     0.3000    0.0057
##      8        0.9152             nan     0.3000    0.0046
##      9        0.9071             nan     0.3000   -0.0051
##     10        0.8902             nan     0.3000    0.0018
##     20        0.8317             nan     0.3000   -0.0028
##     40        0.7433             nan     0.3000   -0.0034
##     60        0.6825             nan     0.3000   -0.0026
##     80        0.6150             nan     0.3000   -0.0050
##    100        0.5776             nan     0.3000   -0.0049
##    120        0.5333             nan     0.3000   -0.0057
##    140        0.4929             nan     0.3000   -0.0011
##    160        0.4616             nan     0.3000   -0.0038
##    180        0.4384             nan     0.3000   -0.0030
##    200        0.4072             nan     0.3000   -0.0019
##    220        0.3739             nan     0.3000    0.0001
##    240        0.3559             nan     0.3000   -0.0021
##    260        0.3308             nan     0.3000   -0.0017
##    280        0.3145             nan     0.3000   -0.0021
##    300        0.2893             nan     0.3000   -0.0028
##    320        0.2726             nan     0.3000   -0.0042
##    340        0.2571             nan     0.3000   -0.0011
##    360        0.2448             nan     0.3000   -0.0016
##    380        0.2308             nan     0.3000   -0.0018
##    400        0.2219             nan     0.3000   -0.0023
##    420        0.2091             nan     0.3000   -0.0026
##    440        0.1987             nan     0.3000   -0.0013
##    460        0.1901             nan     0.3000   -0.0013
##    480        0.1849             nan     0.3000   -0.0006
##    500        0.1771             nan     0.3000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1463             nan     0.3000    0.0759
##      2        1.0702             nan     0.3000    0.0323
##      3        1.0261             nan     0.3000    0.0041
##      4        0.9689             nan     0.3000    0.0232
##      5        0.9313             nan     0.3000    0.0086
##      6        0.9116             nan     0.3000    0.0018
##      7        0.8905             nan     0.3000   -0.0004
##      8        0.8805             nan     0.3000   -0.0042
##      9        0.8564             nan     0.3000    0.0060
##     10        0.8414             nan     0.3000   -0.0013
##     20        0.7490             nan     0.3000   -0.0054
##     40        0.6407             nan     0.3000   -0.0010
##     60        0.5728             nan     0.3000   -0.0109
##     80        0.5055             nan     0.3000   -0.0087
##    100        0.4232             nan     0.3000   -0.0013
##    120        0.3781             nan     0.3000   -0.0012
##    140        0.3408             nan     0.3000   -0.0032
##    160        0.3020             nan     0.3000   -0.0024
##    180        0.2733             nan     0.3000   -0.0031
##    200        0.2456             nan     0.3000   -0.0023
##    220        0.2206             nan     0.3000   -0.0005
##    240        0.2040             nan     0.3000   -0.0015
##    260        0.1834             nan     0.3000   -0.0016
##    280        0.1655             nan     0.3000   -0.0006
##    300        0.1522             nan     0.3000   -0.0008
##    320        0.1407             nan     0.3000   -0.0009
##    340        0.1280             nan     0.3000   -0.0015
##    360        0.1176             nan     0.3000   -0.0010
##    380        0.1068             nan     0.3000   -0.0008
##    400        0.0970             nan     0.3000   -0.0003
##    420        0.0905             nan     0.3000   -0.0006
##    440        0.0841             nan     0.3000   -0.0006
##    460        0.0784             nan     0.3000   -0.0004
##    480        0.0735             nan     0.3000   -0.0003
##    500        0.0683             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1473             nan     0.3000    0.0614
##      2        1.0589             nan     0.3000    0.0282
##      3        0.9924             nan     0.3000    0.0268
##      4        0.9584             nan     0.3000    0.0085
##      5        0.9321             nan     0.3000    0.0034
##      6        0.9130             nan     0.3000   -0.0001
##      7        0.8952             nan     0.3000   -0.0013
##      8        0.8837             nan     0.3000   -0.0014
##      9        0.8726             nan     0.3000   -0.0021
##     10        0.8588             nan     0.3000   -0.0001
##     20        0.7720             nan     0.3000   -0.0105
##     40        0.6569             nan     0.3000   -0.0038
##     60        0.5673             nan     0.3000   -0.0069
##     80        0.5062             nan     0.3000   -0.0073
##    100        0.4461             nan     0.3000   -0.0068
##    120        0.3924             nan     0.3000   -0.0040
##    140        0.3518             nan     0.3000   -0.0027
##    160        0.3136             nan     0.3000   -0.0017
##    180        0.2826             nan     0.3000   -0.0026
##    200        0.2478             nan     0.3000   -0.0018
##    220        0.2228             nan     0.3000   -0.0018
##    240        0.2054             nan     0.3000   -0.0019
##    260        0.1876             nan     0.3000   -0.0010
##    280        0.1675             nan     0.3000   -0.0014
##    300        0.1542             nan     0.3000   -0.0012
##    320        0.1391             nan     0.3000   -0.0005
##    340        0.1265             nan     0.3000   -0.0007
##    360        0.1146             nan     0.3000   -0.0008
##    380        0.1041             nan     0.3000   -0.0004
##    400        0.0953             nan     0.3000   -0.0003
##    420        0.0873             nan     0.3000   -0.0009
##    440        0.0826             nan     0.3000   -0.0006
##    460        0.0755             nan     0.3000   -0.0008
##    480        0.0707             nan     0.3000   -0.0007
##    500        0.0663             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1471             nan     0.3000    0.0588
##      2        1.0751             nan     0.3000    0.0282
##      3        1.0243             nan     0.3000    0.0193
##      4        0.9835             nan     0.3000    0.0131
##      5        0.9450             nan     0.3000    0.0084
##      6        0.9224             nan     0.3000    0.0019
##      7        0.9063             nan     0.3000    0.0014
##      8        0.8856             nan     0.3000   -0.0003
##      9        0.8699             nan     0.3000   -0.0015
##     10        0.8544             nan     0.3000   -0.0017
##     20        0.7754             nan     0.3000   -0.0104
##     40        0.6828             nan     0.3000   -0.0043
##     60        0.5827             nan     0.3000   -0.0090
##     80        0.5111             nan     0.3000   -0.0055
##    100        0.4516             nan     0.3000   -0.0028
##    120        0.4021             nan     0.3000   -0.0048
##    140        0.3570             nan     0.3000   -0.0039
##    160        0.3219             nan     0.3000    0.0002
##    180        0.2942             nan     0.3000   -0.0016
##    200        0.2588             nan     0.3000   -0.0019
##    220        0.2276             nan     0.3000   -0.0024
##    240        0.2026             nan     0.3000   -0.0020
##    260        0.1837             nan     0.3000   -0.0008
##    280        0.1672             nan     0.3000   -0.0006
##    300        0.1496             nan     0.3000   -0.0003
##    320        0.1379             nan     0.3000   -0.0013
##    340        0.1237             nan     0.3000   -0.0008
##    360        0.1131             nan     0.3000   -0.0009
##    380        0.1063             nan     0.3000   -0.0005
##    400        0.0959             nan     0.3000   -0.0005
##    420        0.0873             nan     0.3000   -0.0014
##    440        0.0798             nan     0.3000   -0.0008
##    460        0.0753             nan     0.3000   -0.0003
##    480        0.0702             nan     0.3000   -0.0003
##    500        0.0637             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1710             nan     0.5000    0.0485
##      2        1.0951             nan     0.5000    0.0313
##      3        1.0462             nan     0.5000    0.0165
##      4        0.9888             nan     0.5000    0.0194
##      5        0.9618             nan     0.5000    0.0122
##      6        0.9436             nan     0.5000    0.0038
##      7        0.9296             nan     0.5000   -0.0021
##      8        0.9156             nan     0.5000    0.0041
##      9        0.9057             nan     0.5000    0.0031
##     10        0.9012             nan     0.5000   -0.0062
##     20        0.8565             nan     0.5000    0.0018
##     40        0.8041             nan     0.5000   -0.0046
##     60        2.0564             nan     0.5000   -0.0076
##     80        2.0228             nan     0.5000   -0.0020
##    100        2.0211             nan     0.5000   -0.0005
##    120        2.0114             nan     0.5000   -0.0001
##    140        1.9957             nan     0.5000   -0.0031
##    160        1.9941             nan     0.5000   -0.0006
##    180        1.9848             nan     0.5000    0.0003
##    200        1.9704             nan     0.5000    0.0021
##    220        1.9625             nan     0.5000   -0.0099
##    240        1.9519             nan     0.5000   -0.0028
##    260        1.9459             nan     0.5000    0.0006
##    280        1.9394             nan     0.5000   -0.0001
##    300        1.9301             nan     0.5000    0.0006
##    320        1.9247             nan     0.5000   -0.0000
##    340        1.9223             nan     0.5000    0.0001
##    360        1.9227             nan     0.5000    0.0004
##    380        1.9108             nan     0.5000    0.0002
##    400        1.9089             nan     0.5000   -0.0018
##    420        1.9048             nan     0.5000   -0.0004
##    440        1.9000             nan     0.5000    0.0002
##    460        1.8952             nan     0.5000   -0.0013
##    480        1.8856             nan     0.5000   -0.0002
##    500        1.8757             nan     0.5000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1522             nan     0.5000    0.0565
##      2        1.0921             nan     0.5000    0.0231
##      3        1.0420             nan     0.5000    0.0222
##      4        1.0127             nan     0.5000    0.0117
##      5        0.9912             nan     0.5000    0.0076
##      6        0.9741             nan     0.5000    0.0018
##      7        0.9590             nan     0.5000   -0.0025
##      8        0.9485             nan     0.5000   -0.0044
##      9        0.9372             nan     0.5000   -0.0002
##     10        0.9181             nan     0.5000    0.0069
##     20        0.8734             nan     0.5000   -0.0028
##     40        0.8228             nan     0.5000    0.0031
##     60        0.7829             nan     0.5000   -0.0036
##     80        0.7386             nan     0.5000   -0.0016
##    100        0.7175             nan     0.5000   -0.0035
##    120        0.7004             nan     0.5000   -0.0090
##    140        0.6751             nan     0.5000   -0.0076
##    160        0.6697             nan     0.5000   -0.0053
##    180        0.6535             nan     0.5000   -0.0037
##    200        0.6431             nan     0.5000   -0.0026
##    220        0.6263             nan     0.5000   -0.0022
##    240        0.6094             nan     0.5000   -0.0102
##    260        0.5916             nan     0.5000    0.0000
##    280        0.5828             nan     0.5000   -0.0084
##    300        0.5732             nan     0.5000   -0.0053
##    320        0.5687             nan     0.5000   -0.0048
##    340        0.5584             nan     0.5000   -0.0083
##    360        0.5566             nan     0.5000   -0.0087
##    380        0.5411             nan     0.5000   -0.0034
##    400        0.5274             nan     0.5000   -0.0077
##    420        0.5146             nan     0.5000   -0.0051
##    440        0.5087             nan     0.5000   -0.0032
##    460        0.4985             nan     0.5000   -0.0062
##    480        0.4880             nan     0.5000   -0.0055
##    500        0.4812             nan     0.5000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1682             nan     0.5000    0.0670
##      2        1.0964             nan     0.5000    0.0222
##      3        1.0396             nan     0.5000    0.0237
##      4        0.9984             nan     0.5000    0.0136
##      5        0.9667             nan     0.5000    0.0121
##      6        0.9445             nan     0.5000    0.0077
##      7        0.9374             nan     0.5000   -0.0021
##      8        0.9301             nan     0.5000   -0.0094
##      9        0.9159             nan     0.5000    0.0032
##     10        0.9062             nan     0.5000    0.0042
##     20        0.8683             nan     0.5000   -0.0036
##     40        0.8099             nan     0.5000   -0.0039
##     60        0.7688             nan     0.5000   -0.0031
##     80        0.7452             nan     0.5000   -0.0045
##    100        0.7263             nan     0.5000   -0.0092
##    120        0.7038             nan     0.5000   -0.0036
##    140        0.7906             nan     0.5000   -0.0007
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1192             nan     0.5000    0.0736
##      2        1.0411             nan     0.5000    0.0186
##      3        0.9880             nan     0.5000    0.0169
##      4        0.9645             nan     0.5000   -0.0067
##      5        0.9337             nan     0.5000    0.0064
##      6        0.9089             nan     0.5000    0.0008
##      7        0.8906             nan     0.5000   -0.0000
##      8        0.8742             nan     0.5000    0.0025
##      9        0.8689             nan     0.5000   -0.0045
##     10        0.8640             nan     0.5000   -0.0042
##     20        0.7757             nan     0.5000   -0.0023
##     40        0.6850             nan     0.5000   -0.0097
##     60        0.6303             nan     0.5000   -0.0078
##     80        0.5686             nan     0.5000   -0.0047
##    100        0.5130             nan     0.5000   -0.0065
##    120        0.4798             nan     0.5000   -0.0092
##    140        0.7864             nan     0.5000   -0.0099
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1221             nan     0.5000    0.0619
##      2        1.0238             nan     0.5000    0.0404
##      3        0.9756             nan     0.5000    0.0082
##      4        0.9498             nan     0.5000    0.0073
##      5        0.9302             nan     0.5000    0.0020
##      6        0.9183             nan     0.5000   -0.0135
##      7        0.9026             nan     0.5000   -0.0067
##      8        0.8800             nan     0.5000   -0.0002
##      9        0.8692             nan     0.5000   -0.0029
##     10        0.8674             nan     0.5000   -0.0141
##     20        0.7942             nan     0.5000   -0.0087
##     40        0.7189             nan     0.5000   -0.0153
##     60        0.6610             nan     0.5000   -0.0109
##     80        0.5680             nan     0.5000   -0.0086
##    100        0.5293             nan     0.5000   -0.0033
##    120        0.4820             nan     0.5000   -0.0098
##    140        0.4093             nan     0.5000   -0.0075
##    160        0.3754             nan     0.5000   -0.0040
##    180        0.3349             nan     0.5000   -0.0053
##    200        0.2904             nan     0.5000   -0.0031
##    220        0.2601             nan     0.5000   -0.0042
##    240        0.2379             nan     0.5000   -0.0029
##    260        0.2127             nan     0.5000   -0.0014
##    280        0.1970             nan     0.5000   -0.0010
##    300        0.1742             nan     0.5000   -0.0010
##    320        0.1611             nan     0.5000   -0.0035
##    340        0.1470             nan     0.5000   -0.0012
##    360        0.1451             nan     0.5000   -0.0023
##    380        0.1340             nan     0.5000   -0.0018
##    400        0.1161             nan     0.5000   -0.0009
##    420        0.1042             nan     0.5000   -0.0017
##    440        0.0948             nan     0.5000   -0.0005
##    460        0.0897             nan     0.5000   -0.0016
##    480        0.0825             nan     0.5000   -0.0022
##    500        0.0782             nan     0.5000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1195             nan     0.5000    0.0730
##      2        1.0288             nan     0.5000    0.0349
##      3        0.9716             nan     0.5000    0.0225
##      4        0.9409             nan     0.5000    0.0033
##      5        0.9317             nan     0.5000   -0.0110
##      6        0.9064             nan     0.5000   -0.0021
##      7        0.8937             nan     0.5000    0.0009
##      8        0.8884             nan     0.5000   -0.0129
##      9        0.8693             nan     0.5000   -0.0009
##     10        0.8585             nan     0.5000   -0.0052
##     20        0.7966             nan     0.5000   -0.0053
##     40        0.7055             nan     0.5000   -0.0141
##     60        0.6576             nan     0.5000   -0.0126
##     80        0.5744             nan     0.5000   -0.0038
##    100        0.4981             nan     0.5000   -0.0038
##    120        0.4504             nan     0.5000   -0.0135
##    140        0.3979             nan     0.5000   -0.0058
##    160        0.3592             nan     0.5000   -0.0029
##    180        0.3334             nan     0.5000   -0.0031
##    200        0.2936             nan     0.5000   -0.0031
##    220        0.2594             nan     0.5000   -0.0014
##    240        0.2366             nan     0.5000   -0.0040
##    260        0.2177             nan     0.5000   -0.0024
##    280        0.2015             nan     0.5000   -0.0030
##    300        0.1861             nan     0.5000   -0.0013
##    320        0.1679             nan     0.5000   -0.0048
##    340        0.1550             nan     0.5000   -0.0020
##    360        0.1415             nan     0.5000   -0.0011
##    380        0.1264             nan     0.5000   -0.0023
##    400        0.1116             nan     0.5000   -0.0005
##    420        0.1063             nan     0.5000   -0.0018
##    440        0.1000             nan     0.5000   -0.0006
##    460        0.0948             nan     0.5000   -0.0012
##    480        0.0878             nan     0.5000   -0.0014
##    500        0.0809             nan     0.5000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0901             nan     0.5000    0.0955
##      2        0.9834             nan     0.5000    0.0418
##      3        0.9382             nan     0.5000    0.0057
##      4        0.9014             nan     0.5000    0.0099
##      5        0.8886             nan     0.5000   -0.0175
##      6        0.8750             nan     0.5000   -0.0187
##      7        0.8603             nan     0.5000   -0.0101
##      8        0.8424             nan     0.5000   -0.0090
##      9        0.8259             nan     0.5000   -0.0000
##     10        0.8167             nan     0.5000   -0.0069
##     20        0.7540             nan     0.5000    0.0068
##     40        0.6025             nan     0.5000   -0.0053
##     60        0.5664             nan     0.5000   -0.0817
##     80        0.4150             nan     0.5000   -0.0045
##    100        0.3249             nan     0.5000   -0.0033
##    120        0.2645             nan     0.5000   -0.0036
##    140        0.2123             nan     0.5000   -0.0004
##    160        0.1798             nan     0.5000   -0.0030
##    180        0.1534             nan     0.5000   -0.0004
##    200        0.1320             nan     0.5000   -0.0021
##    220        0.1129             nan     0.5000   -0.0009
##    240        0.0983             nan     0.5000   -0.0017
##    260        0.0837             nan     0.5000   -0.0018
##    280        0.0763             nan     0.5000   -0.0018
##    300        0.0653             nan     0.5000   -0.0015
##    320        0.0572             nan     0.5000   -0.0004
##    340        0.0498             nan     0.5000   -0.0009
##    360        0.0441             nan     0.5000   -0.0005
##    380        0.0389             nan     0.5000   -0.0004
##    400        0.0345             nan     0.5000   -0.0004
##    420        0.0303             nan     0.5000   -0.0007
##    440        0.0276             nan     0.5000   -0.0000
##    460        0.0253             nan     0.5000   -0.0003
##    480        0.0225             nan     0.5000   -0.0006
##    500        0.0204             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0986             nan     0.5000    0.1028
##      2        0.9880             nan     0.5000    0.0413
##      3        0.9513             nan     0.5000   -0.0022
##      4        0.9311             nan     0.5000   -0.0073
##      5        0.8997             nan     0.5000   -0.0002
##      6        0.8785             nan     0.5000   -0.0042
##      7        0.8578             nan     0.5000   -0.0048
##      8        0.8412             nan     0.5000   -0.0040
##      9        0.8308             nan     0.5000   -0.0134
##     10        0.8212             nan     0.5000   -0.0126
##     20        0.7138             nan     0.5000   -0.0005
##     40        0.5579             nan     0.5000   -0.0046
##     60        0.4566             nan     0.5000    0.0012
##     80        0.3976             nan     0.5000   -0.0070
##    100        0.3212             nan     0.5000   -0.0006
##    120        0.2628             nan     0.5000   -0.0031
##    140        0.2137             nan     0.5000   -0.0034
##    160        0.1802             nan     0.5000   -0.0029
##    180        0.1556             nan     0.5000   -0.0019
##    200        0.1334             nan     0.5000   -0.0021
##    220        0.1191             nan     0.5000   -0.0036
##    240        0.1041             nan     0.5000   -0.0004
##    260        0.0910             nan     0.5000   -0.0015
##    280        0.0793             nan     0.5000   -0.0016
##    300        0.0688             nan     0.5000   -0.0013
##    320        0.0624             nan     0.5000   -0.0003
##    340        0.0552             nan     0.5000   -0.0012
##    360        0.0496             nan     0.5000   -0.0010
##    380        0.0425             nan     0.5000   -0.0007
##    400        0.0371             nan     0.5000   -0.0002
##    420        0.0328             nan     0.5000   -0.0002
##    440        0.0281             nan     0.5000   -0.0000
##    460        0.0259             nan     0.5000   -0.0005
##    480        0.0233             nan     0.5000   -0.0001
##    500        0.0206             nan     0.5000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0964             nan     0.5000    0.0644
##      2        0.9911             nan     0.5000    0.0386
##      3        0.9418             nan     0.5000    0.0068
##      4        0.8993             nan     0.5000    0.0111
##      5        0.8862             nan     0.5000   -0.0183
##      6        0.8551             nan     0.5000    0.0015
##      7        0.8397             nan     0.5000   -0.0038
##      8        0.8244             nan     0.5000   -0.0073
##      9        0.8108             nan     0.5000   -0.0065
##     10        0.8059             nan     0.5000   -0.0068
##     20        0.7262             nan     0.5000   -0.0109
##     40        0.6202             nan     0.5000   -0.0056
##     60        0.5056             nan     0.5000   -0.0055
##     80        0.4096             nan     0.5000   -0.0088
##    100        0.3324             nan     0.5000   -0.0029
##    120        0.2587             nan     0.5000   -0.0002
##    140        0.2131             nan     0.5000   -0.0037
##    160        0.1820             nan     0.5000   -0.0035
##    180        0.1541             nan     0.5000   -0.0021
##    200        0.1369             nan     0.5000   -0.0015
##    220        0.1177             nan     0.5000   -0.0023
##    240        0.0985             nan     0.5000   -0.0022
##    260        0.0850             nan     0.5000   -0.0011
##    280        0.0731             nan     0.5000    0.0000
##    300        0.0636             nan     0.5000   -0.0012
##    320        0.0564             nan     0.5000   -0.0007
##    340        0.0515             nan     0.5000   -0.0004
##    360        0.0465             nan     0.5000   -0.0006
##    380        0.0413             nan     0.5000   -0.0003
##    400        0.0360             nan     0.5000   -0.0005
##    420        0.0319             nan     0.5000   -0.0003
##    440        0.0281             nan     0.5000   -0.0002
##    460        0.0240             nan     0.5000   -0.0003
##    480        0.0213             nan     0.5000   -0.0005
##    500        0.0192             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1365             nan     1.0000    0.0458
##      2        1.0480             nan     1.0000    0.0309
##      3        1.0218             nan     1.0000   -0.0043
##      4        0.9830             nan     1.0000    0.0087
##      5        0.9648             nan     1.0000    0.0018
##      6        0.9655             nan     1.0000   -0.0185
##      7        0.9652             nan     1.0000   -0.0210
##      8        0.9555             nan     1.0000   -0.0102
##      9        1.0261             nan     1.0000   -0.0902
##     10        1.0386             nan     1.0000   -0.0252
##     20       36.3092             nan     1.0000   -0.0007
##     40       36.2980             nan     1.0000   -0.0343
##     60       36.6792             nan     1.0000    0.0036
##     80    37649.1222             nan     1.0000   -0.0013
##    100    37649.0938             nan     1.0000    0.0006
##    120    37649.0398             nan     1.0000   -0.0013
##    140   816676.4265             nan     1.0000   -0.0002
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1278             nan     1.0000    0.0655
##      2        1.0817             nan     1.0000    0.0106
##      3        1.0540             nan     1.0000   -0.0297
##      4        1.0351             nan     1.0000   -0.0245
##      5        1.0163             nan     1.0000   -0.0050
##      6        1.0152             nan     1.0000   -0.0308
##      7        0.9993             nan     1.0000   -0.0023
##      8        0.9775             nan     1.0000   -0.0108
##      9        0.9921             nan     1.0000   -0.0377
##     10        0.9652             nan     1.0000   -0.0099
##     20        0.9753             nan     1.0000   -0.0147
##     40        2.8221             nan     1.0000    0.0062
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000   -0.0194
##    160     2995.3187             nan     1.0000    0.0005
##    180     3389.0624             nan     1.0000   -0.0040
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1375             nan     1.0000    0.0708
##      2        1.0896             nan     1.0000    0.0013
##      3        1.0181             nan     1.0000    0.0417
##      4        0.9913             nan     1.0000    0.0006
##      5        0.9867             nan     1.0000   -0.0242
##      6        1.0111             nan     1.0000   -0.0347
##      7        1.0029             nan     1.0000   -0.0065
##      8        0.9940             nan     1.0000   -0.0158
##      9        0.9719             nan     1.0000    0.0041
##     10        0.9829             nan     1.0000   -0.0287
##     20        0.8916             nan     1.0000   -0.0063
##     40      163.8002             nan     1.0000   -0.0483
##     60      164.7028             nan     1.0000    0.0050
##     80      164.7520             nan     1.0000    0.0138
##    100      174.2501             nan     1.0000   -0.0005
##    120      174.2137             nan     1.0000   -0.0020
##    140      174.1954             nan     1.0000   -0.0065
##    160      174.1781             nan     1.0000   -0.0067
##    180      173.9277             nan     1.0000    0.0021
##    200      173.9052             nan     1.0000   -0.0023
##    220      173.7962             nan     1.0000    0.0190
##    240      173.7537             nan     1.0000   -0.0040
##    260      173.6956             nan     1.0000    0.0174
##    280      173.6691             nan     1.0000   -0.0000
##    300      173.5915             nan     1.0000    0.0072
##    320      173.5599             nan     1.0000   -0.0023
##    340      173.4880             nan     1.0000   -0.0000
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0755             nan     1.0000    0.0691
##      2        0.9830             nan     1.0000    0.0268
##      3        0.9686             nan     1.0000   -0.0199
##      4        0.9692             nan     1.0000   -0.0235
##      5        0.9841             nan     1.0000   -0.0407
##      6        0.9626             nan     1.0000   -0.0110
##      7        0.9487             nan     1.0000   -0.0136
##      8        0.9535             nan     1.0000   -0.0381
##      9        0.9640             nan     1.0000   -0.0471
##     10        0.9484             nan     1.0000   -0.0237
##     20        0.9341             nan     1.0000   -0.0285
##     40       14.1276             nan     1.0000   -7.7784
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0644             nan     1.0000    0.0944
##      2        0.9965             nan     1.0000    0.0115
##      3        0.9450             nan     1.0000    0.0132
##      4        0.9524             nan     1.0000   -0.0391
##      5        0.9419             nan     1.0000   -0.0132
##      6        1.2691             nan     1.0000   -0.3626
##      7        1.2764             nan     1.0000   -0.0551
##      8        1.2291             nan     1.0000    0.0164
##      9 1114478821.4872             nan     1.0000 -557239410.2070
##     10 1114478821.4777             nan     1.0000   -0.0116
##     20 1114478821.4788             nan     1.0000   -0.0614
##     40 1114478821.3185             nan     1.0000   -0.0433
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0685             nan     1.0000    0.0988
##      2        1.0128             nan     1.0000    0.0074
##      3        0.9975             nan     1.0000   -0.0298
##      4        0.9792             nan     1.0000   -0.0284
##      5        0.9852             nan     1.0000   -0.0501
##      6        1.0977             nan     1.0000   -0.1047
##      7        1.1714             nan     1.0000   -0.1644
##      8           inf             nan     1.0000      -inf
##      9           inf             nan     1.0000       nan
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000   -0.0078
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0148             nan     1.0000    0.1035
##      2        0.9669             nan     1.0000   -0.0045
##      3        0.9533             nan     1.0000   -0.0423
##      4        0.9436             nan     1.0000   -0.0392
##      5        0.9279             nan     1.0000   -0.0210
##      6        1.0522             nan     1.0000   -0.1711
##      7        0.9779             nan     1.0000   -0.0190
##      8        0.9504             nan     1.0000   -0.0288
##      9        0.9987             nan     1.0000   -0.0959
##     10        0.9713             nan     1.0000   -0.0450
##     20        0.9327             nan     1.0000   -0.0713
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0419             nan     1.0000    0.1137
##      2        0.9745             nan     1.0000    0.0049
##      3        0.9600             nan     1.0000   -0.0482
##      4        0.9150             nan     1.0000   -0.0105
##      5        0.8893             nan     1.0000   -0.0186
##      6        0.8974             nan     1.0000   -0.0564
##      7        0.8918             nan     1.0000   -0.0280
##      8        0.8706             nan     1.0000   -0.0292
##      9        0.8461             nan     1.0000   -0.0261
##     10        0.9082             nan     1.0000   -0.0997
##     20   163902.6421             nan     1.0000 -163901.7564
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0603             nan     1.0000    0.0999
##      2        0.9657             nan     1.0000    0.0221
##      3        0.9547             nan     1.0000   -0.0362
##      4        0.9437             nan     1.0000   -0.0320
##      5        0.9648             nan     1.0000   -0.0803
##      6        0.9240             nan     1.0000   -0.0155
##      7        0.9186             nan     1.0000   -0.0305
##      8        0.9079             nan     1.0000   -0.0186
##      9        0.9323             nan     1.0000   -0.0526
##     10        1.0034             nan     1.0000   -0.1166
##     20        1.0640             nan     1.0000   -0.1789
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0001
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2791             nan     0.0010    0.0002
##     60        1.2724             nan     0.0010    0.0001
##     80        1.2659             nan     0.0010    0.0001
##    100        1.2595             nan     0.0010    0.0001
##    120        1.2533             nan     0.0010    0.0001
##    140        1.2476             nan     0.0010    0.0001
##    160        1.2418             nan     0.0010    0.0001
##    180        1.2364             nan     0.0010    0.0001
##    200        1.2309             nan     0.0010    0.0001
##    220        1.2256             nan     0.0010    0.0001
##    240        1.2206             nan     0.0010    0.0001
##    260        1.2157             nan     0.0010    0.0001
##    280        1.2112             nan     0.0010    0.0001
##    300        1.2066             nan     0.0010    0.0001
##    320        1.2019             nan     0.0010    0.0001
##    340        1.1976             nan     0.0010    0.0001
##    360        1.1934             nan     0.0010    0.0001
##    380        1.1892             nan     0.0010    0.0001
##    400        1.1851             nan     0.0010    0.0001
##    420        1.1812             nan     0.0010    0.0001
##    440        1.1771             nan     0.0010    0.0001
##    460        1.1732             nan     0.0010    0.0001
##    480        1.1695             nan     0.0010    0.0001
##    500        1.1657             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0001
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2860             nan     0.0010    0.0001
##     40        1.2791             nan     0.0010    0.0001
##     60        1.2725             nan     0.0010    0.0002
##     80        1.2660             nan     0.0010    0.0001
##    100        1.2597             nan     0.0010    0.0001
##    120        1.2538             nan     0.0010    0.0001
##    140        1.2480             nan     0.0010    0.0001
##    160        1.2423             nan     0.0010    0.0001
##    180        1.2367             nan     0.0010    0.0001
##    200        1.2314             nan     0.0010    0.0001
##    220        1.2262             nan     0.0010    0.0001
##    240        1.2210             nan     0.0010    0.0001
##    260        1.2162             nan     0.0010    0.0001
##    280        1.2115             nan     0.0010    0.0001
##    300        1.2069             nan     0.0010    0.0001
##    320        1.2023             nan     0.0010    0.0001
##    340        1.1978             nan     0.0010    0.0001
##    360        1.1934             nan     0.0010    0.0001
##    380        1.1892             nan     0.0010    0.0001
##    400        1.1850             nan     0.0010    0.0001
##    420        1.1811             nan     0.0010    0.0001
##    440        1.1770             nan     0.0010    0.0001
##    460        1.1731             nan     0.0010    0.0001
##    480        1.1693             nan     0.0010    0.0001
##    500        1.1656             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2931             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2791             nan     0.0010    0.0002
##     60        1.2723             nan     0.0010    0.0002
##     80        1.2659             nan     0.0010    0.0001
##    100        1.2597             nan     0.0010    0.0001
##    120        1.2538             nan     0.0010    0.0001
##    140        1.2479             nan     0.0010    0.0002
##    160        1.2422             nan     0.0010    0.0001
##    180        1.2367             nan     0.0010    0.0001
##    200        1.2313             nan     0.0010    0.0001
##    220        1.2260             nan     0.0010    0.0001
##    240        1.2209             nan     0.0010    0.0001
##    260        1.2161             nan     0.0010    0.0001
##    280        1.2111             nan     0.0010    0.0001
##    300        1.2065             nan     0.0010    0.0001
##    320        1.2020             nan     0.0010    0.0001
##    340        1.1975             nan     0.0010    0.0001
##    360        1.1931             nan     0.0010    0.0001
##    380        1.1889             nan     0.0010    0.0001
##    400        1.1848             nan     0.0010    0.0001
##    420        1.1809             nan     0.0010    0.0001
##    440        1.1768             nan     0.0010    0.0001
##    460        1.1729             nan     0.0010    0.0001
##    480        1.1692             nan     0.0010    0.0001
##    500        1.1655             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2840             nan     0.0010    0.0002
##     40        1.2749             nan     0.0010    0.0002
##     60        1.2661             nan     0.0010    0.0002
##     80        1.2575             nan     0.0010    0.0002
##    100        1.2493             nan     0.0010    0.0002
##    120        1.2412             nan     0.0010    0.0002
##    140        1.2334             nan     0.0010    0.0002
##    160        1.2259             nan     0.0010    0.0002
##    180        1.2187             nan     0.0010    0.0002
##    200        1.2113             nan     0.0010    0.0002
##    220        1.2042             nan     0.0010    0.0002
##    240        1.1972             nan     0.0010    0.0002
##    260        1.1908             nan     0.0010    0.0001
##    280        1.1842             nan     0.0010    0.0001
##    300        1.1780             nan     0.0010    0.0001
##    320        1.1718             nan     0.0010    0.0002
##    340        1.1658             nan     0.0010    0.0001
##    360        1.1602             nan     0.0010    0.0001
##    380        1.1545             nan     0.0010    0.0001
##    400        1.1490             nan     0.0010    0.0001
##    420        1.1436             nan     0.0010    0.0001
##    440        1.1382             nan     0.0010    0.0001
##    460        1.1329             nan     0.0010    0.0001
##    480        1.1277             nan     0.0010    0.0001
##    500        1.1228             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2837             nan     0.0010    0.0002
##     40        1.2746             nan     0.0010    0.0002
##     60        1.2657             nan     0.0010    0.0002
##     80        1.2570             nan     0.0010    0.0002
##    100        1.2486             nan     0.0010    0.0002
##    120        1.2405             nan     0.0010    0.0002
##    140        1.2326             nan     0.0010    0.0002
##    160        1.2248             nan     0.0010    0.0002
##    180        1.2178             nan     0.0010    0.0001
##    200        1.2106             nan     0.0010    0.0002
##    220        1.2034             nan     0.0010    0.0001
##    240        1.1965             nan     0.0010    0.0002
##    260        1.1899             nan     0.0010    0.0001
##    280        1.1835             nan     0.0010    0.0001
##    300        1.1772             nan     0.0010    0.0001
##    320        1.1710             nan     0.0010    0.0001
##    340        1.1650             nan     0.0010    0.0001
##    360        1.1593             nan     0.0010    0.0001
##    380        1.1537             nan     0.0010    0.0001
##    400        1.1482             nan     0.0010    0.0001
##    420        1.1427             nan     0.0010    0.0001
##    440        1.1374             nan     0.0010    0.0001
##    460        1.1326             nan     0.0010    0.0001
##    480        1.1274             nan     0.0010    0.0001
##    500        1.1225             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2840             nan     0.0010    0.0002
##     40        1.2749             nan     0.0010    0.0002
##     60        1.2661             nan     0.0010    0.0002
##     80        1.2573             nan     0.0010    0.0002
##    100        1.2490             nan     0.0010    0.0001
##    120        1.2411             nan     0.0010    0.0001
##    140        1.2334             nan     0.0010    0.0002
##    160        1.2255             nan     0.0010    0.0002
##    180        1.2181             nan     0.0010    0.0002
##    200        1.2110             nan     0.0010    0.0002
##    220        1.2039             nan     0.0010    0.0002
##    240        1.1971             nan     0.0010    0.0001
##    260        1.1905             nan     0.0010    0.0002
##    280        1.1841             nan     0.0010    0.0001
##    300        1.1776             nan     0.0010    0.0001
##    320        1.1715             nan     0.0010    0.0001
##    340        1.1658             nan     0.0010    0.0001
##    360        1.1602             nan     0.0010    0.0001
##    380        1.1547             nan     0.0010    0.0001
##    400        1.1492             nan     0.0010    0.0001
##    420        1.1439             nan     0.0010    0.0001
##    440        1.1386             nan     0.0010    0.0001
##    460        1.1336             nan     0.0010    0.0001
##    480        1.1286             nan     0.0010    0.0001
##    500        1.1237             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2905             nan     0.0010    0.0003
##      6        1.2899             nan     0.0010    0.0002
##      7        1.2893             nan     0.0010    0.0002
##      8        1.2887             nan     0.0010    0.0002
##      9        1.2882             nan     0.0010    0.0002
##     10        1.2876             nan     0.0010    0.0003
##     20        1.2822             nan     0.0010    0.0002
##     40        1.2716             nan     0.0010    0.0002
##     60        1.2612             nan     0.0010    0.0002
##     80        1.2512             nan     0.0010    0.0002
##    100        1.2412             nan     0.0010    0.0002
##    120        1.2320             nan     0.0010    0.0002
##    140        1.2227             nan     0.0010    0.0002
##    160        1.2137             nan     0.0010    0.0002
##    180        1.2054             nan     0.0010    0.0002
##    200        1.1971             nan     0.0010    0.0002
##    220        1.1889             nan     0.0010    0.0002
##    240        1.1812             nan     0.0010    0.0002
##    260        1.1736             nan     0.0010    0.0002
##    280        1.1662             nan     0.0010    0.0002
##    300        1.1589             nan     0.0010    0.0001
##    320        1.1519             nan     0.0010    0.0002
##    340        1.1448             nan     0.0010    0.0002
##    360        1.1381             nan     0.0010    0.0001
##    380        1.1316             nan     0.0010    0.0001
##    400        1.1253             nan     0.0010    0.0001
##    420        1.1191             nan     0.0010    0.0001
##    440        1.1133             nan     0.0010    0.0001
##    460        1.1075             nan     0.0010    0.0001
##    480        1.1019             nan     0.0010    0.0001
##    500        1.0963             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0002
##      7        1.2894             nan     0.0010    0.0003
##      8        1.2889             nan     0.0010    0.0002
##      9        1.2883             nan     0.0010    0.0003
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2824             nan     0.0010    0.0002
##     40        1.2717             nan     0.0010    0.0002
##     60        1.2613             nan     0.0010    0.0002
##     80        1.2514             nan     0.0010    0.0002
##    100        1.2416             nan     0.0010    0.0002
##    120        1.2323             nan     0.0010    0.0002
##    140        1.2231             nan     0.0010    0.0002
##    160        1.2145             nan     0.0010    0.0002
##    180        1.2059             nan     0.0010    0.0002
##    200        1.1976             nan     0.0010    0.0002
##    220        1.1894             nan     0.0010    0.0002
##    240        1.1815             nan     0.0010    0.0001
##    260        1.1740             nan     0.0010    0.0001
##    280        1.1668             nan     0.0010    0.0002
##    300        1.1596             nan     0.0010    0.0001
##    320        1.1528             nan     0.0010    0.0001
##    340        1.1461             nan     0.0010    0.0001
##    360        1.1395             nan     0.0010    0.0001
##    380        1.1330             nan     0.0010    0.0001
##    400        1.1265             nan     0.0010    0.0001
##    420        1.1205             nan     0.0010    0.0001
##    440        1.1142             nan     0.0010    0.0001
##    460        1.1085             nan     0.0010    0.0001
##    480        1.1028             nan     0.0010    0.0001
##    500        1.0972             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0002
##      7        1.2894             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0002
##      9        1.2884             nan     0.0010    0.0003
##     10        1.2879             nan     0.0010    0.0002
##     20        1.2824             nan     0.0010    0.0002
##     40        1.2719             nan     0.0010    0.0002
##     60        1.2616             nan     0.0010    0.0002
##     80        1.2518             nan     0.0010    0.0002
##    100        1.2421             nan     0.0010    0.0002
##    120        1.2327             nan     0.0010    0.0002
##    140        1.2238             nan     0.0010    0.0002
##    160        1.2150             nan     0.0010    0.0002
##    180        1.2065             nan     0.0010    0.0002
##    200        1.1981             nan     0.0010    0.0002
##    220        1.1901             nan     0.0010    0.0002
##    240        1.1823             nan     0.0010    0.0002
##    260        1.1744             nan     0.0010    0.0002
##    280        1.1670             nan     0.0010    0.0001
##    300        1.1597             nan     0.0010    0.0002
##    320        1.1526             nan     0.0010    0.0001
##    340        1.1457             nan     0.0010    0.0002
##    360        1.1391             nan     0.0010    0.0001
##    380        1.1326             nan     0.0010    0.0001
##    400        1.1268             nan     0.0010    0.0001
##    420        1.1205             nan     0.0010    0.0001
##    440        1.1145             nan     0.0010    0.0001
##    460        1.1088             nan     0.0010    0.0001
##    480        1.1033             nan     0.0010    0.0001
##    500        1.0981             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2557             nan     0.1000    0.0163
##      2        1.2347             nan     0.1000    0.0085
##      3        1.2076             nan     0.1000    0.0121
##      4        1.1818             nan     0.1000    0.0107
##      5        1.1630             nan     0.1000    0.0069
##      6        1.1431             nan     0.1000    0.0076
##      7        1.1249             nan     0.1000    0.0088
##      8        1.1112             nan     0.1000    0.0068
##      9        1.0979             nan     0.1000    0.0038
##     10        1.0888             nan     0.1000    0.0029
##     20        0.9983             nan     0.1000    0.0009
##     40        0.9157             nan     0.1000    0.0010
##     60        0.8750             nan     0.1000   -0.0002
##     80        0.8495             nan     0.1000   -0.0011
##    100        0.8301             nan     0.1000   -0.0004
##    120        0.8150             nan     0.1000   -0.0003
##    140        0.8026             nan     0.1000   -0.0006
##    160        0.7952             nan     0.1000   -0.0014
##    180        0.7839             nan     0.1000   -0.0008
##    200        0.7759             nan     0.1000   -0.0010
##    220        0.7669             nan     0.1000   -0.0010
##    240        0.7617             nan     0.1000   -0.0005
##    260        0.7566             nan     0.1000   -0.0009
##    280        0.7497             nan     0.1000   -0.0011
##    300        0.7432             nan     0.1000   -0.0009
##    320        0.7362             nan     0.1000   -0.0008
##    340        0.7299             nan     0.1000   -0.0004
##    360        0.7244             nan     0.1000   -0.0007
##    380        0.7205             nan     0.1000   -0.0011
##    400        0.7163             nan     0.1000   -0.0007
##    420        0.7093             nan     0.1000   -0.0006
##    440        0.7048             nan     0.1000   -0.0010
##    460        0.7007             nan     0.1000   -0.0002
##    480        0.6951             nan     0.1000   -0.0007
##    500        0.6904             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2602             nan     0.1000    0.0158
##      2        1.2294             nan     0.1000    0.0136
##      3        1.2051             nan     0.1000    0.0099
##      4        1.1833             nan     0.1000    0.0104
##      5        1.1643             nan     0.1000    0.0086
##      6        1.1508             nan     0.1000    0.0068
##      7        1.1374             nan     0.1000    0.0052
##      8        1.1242             nan     0.1000    0.0047
##      9        1.1123             nan     0.1000    0.0042
##     10        1.0966             nan     0.1000    0.0072
##     20        1.0007             nan     0.1000    0.0036
##     40        0.9138             nan     0.1000    0.0007
##     60        0.8723             nan     0.1000   -0.0007
##     80        0.8461             nan     0.1000    0.0004
##    100        0.8270             nan     0.1000   -0.0007
##    120        0.8125             nan     0.1000   -0.0010
##    140        0.8015             nan     0.1000   -0.0006
##    160        0.7896             nan     0.1000   -0.0006
##    180        0.7804             nan     0.1000   -0.0009
##    200        0.7687             nan     0.1000   -0.0009
##    220        0.7616             nan     0.1000   -0.0005
##    240        0.7553             nan     0.1000   -0.0010
##    260        0.7487             nan     0.1000   -0.0006
##    280        0.7433             nan     0.1000   -0.0007
##    300        0.7375             nan     0.1000   -0.0011
##    320        0.7318             nan     0.1000   -0.0011
##    340        0.7282             nan     0.1000   -0.0018
##    360        0.7203             nan     0.1000   -0.0009
##    380        0.7149             nan     0.1000   -0.0009
##    400        0.7097             nan     0.1000   -0.0003
##    420        0.7049             nan     0.1000   -0.0007
##    440        0.7004             nan     0.1000   -0.0013
##    460        0.6973             nan     0.1000   -0.0009
##    480        0.6909             nan     0.1000   -0.0008
##    500        0.6851             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2569             nan     0.1000    0.0158
##      2        1.2274             nan     0.1000    0.0164
##      3        1.2002             nan     0.1000    0.0115
##      4        1.1799             nan     0.1000    0.0089
##      5        1.1621             nan     0.1000    0.0072
##      6        1.1452             nan     0.1000    0.0067
##      7        1.1279             nan     0.1000    0.0074
##      8        1.1137             nan     0.1000    0.0068
##      9        1.0985             nan     0.1000    0.0048
##     10        1.0832             nan     0.1000    0.0053
##     20        0.9942             nan     0.1000    0.0026
##     40        0.9133             nan     0.1000    0.0004
##     60        0.8785             nan     0.1000   -0.0008
##     80        0.8521             nan     0.1000   -0.0003
##    100        0.8319             nan     0.1000   -0.0016
##    120        0.8185             nan     0.1000   -0.0012
##    140        0.8048             nan     0.1000   -0.0001
##    160        0.7940             nan     0.1000   -0.0010
##    180        0.7864             nan     0.1000   -0.0012
##    200        0.7759             nan     0.1000   -0.0004
##    220        0.7702             nan     0.1000   -0.0014
##    240        0.7642             nan     0.1000   -0.0008
##    260        0.7594             nan     0.1000   -0.0008
##    280        0.7520             nan     0.1000   -0.0009
##    300        0.7471             nan     0.1000   -0.0013
##    320        0.7400             nan     0.1000   -0.0005
##    340        0.7342             nan     0.1000   -0.0003
##    360        0.7285             nan     0.1000   -0.0009
##    380        0.7239             nan     0.1000   -0.0013
##    400        0.7206             nan     0.1000   -0.0013
##    420        0.7141             nan     0.1000   -0.0010
##    440        0.7088             nan     0.1000   -0.0011
##    460        0.7018             nan     0.1000   -0.0008
##    480        0.6992             nan     0.1000   -0.0015
##    500        0.6944             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2490             nan     0.1000    0.0228
##      2        1.2165             nan     0.1000    0.0158
##      3        1.1807             nan     0.1000    0.0140
##      4        1.1507             nan     0.1000    0.0139
##      5        1.1214             nan     0.1000    0.0127
##      6        1.0964             nan     0.1000    0.0104
##      7        1.0753             nan     0.1000    0.0096
##      8        1.0570             nan     0.1000    0.0082
##      9        1.0404             nan     0.1000    0.0069
##     10        1.0262             nan     0.1000    0.0059
##     20        0.9261             nan     0.1000    0.0005
##     40        0.8415             nan     0.1000   -0.0015
##     60        0.7930             nan     0.1000   -0.0010
##     80        0.7591             nan     0.1000   -0.0011
##    100        0.7356             nan     0.1000   -0.0016
##    120        0.7090             nan     0.1000   -0.0010
##    140        0.6899             nan     0.1000   -0.0007
##    160        0.6657             nan     0.1000   -0.0018
##    180        0.6454             nan     0.1000   -0.0007
##    200        0.6267             nan     0.1000   -0.0011
##    220        0.6102             nan     0.1000   -0.0026
##    240        0.5909             nan     0.1000   -0.0006
##    260        0.5715             nan     0.1000   -0.0016
##    280        0.5509             nan     0.1000   -0.0020
##    300        0.5338             nan     0.1000   -0.0012
##    320        0.5202             nan     0.1000   -0.0011
##    340        0.5035             nan     0.1000   -0.0007
##    360        0.4922             nan     0.1000   -0.0008
##    380        0.4789             nan     0.1000   -0.0008
##    400        0.4669             nan     0.1000   -0.0005
##    420        0.4569             nan     0.1000   -0.0004
##    440        0.4458             nan     0.1000   -0.0006
##    460        0.4336             nan     0.1000   -0.0013
##    480        0.4229             nan     0.1000   -0.0006
##    500        0.4139             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2464             nan     0.1000    0.0212
##      2        1.2143             nan     0.1000    0.0148
##      3        1.1797             nan     0.1000    0.0151
##      4        1.1505             nan     0.1000    0.0114
##      5        1.1230             nan     0.1000    0.0112
##      6        1.0990             nan     0.1000    0.0078
##      7        1.0795             nan     0.1000    0.0078
##      8        1.0573             nan     0.1000    0.0101
##      9        1.0402             nan     0.1000    0.0052
##     10        1.0276             nan     0.1000    0.0048
##     20        0.9266             nan     0.1000    0.0011
##     40        0.8371             nan     0.1000   -0.0010
##     60        0.7866             nan     0.1000   -0.0002
##     80        0.7510             nan     0.1000   -0.0008
##    100        0.7204             nan     0.1000   -0.0022
##    120        0.6957             nan     0.1000   -0.0009
##    140        0.6750             nan     0.1000   -0.0016
##    160        0.6518             nan     0.1000   -0.0013
##    180        0.6314             nan     0.1000   -0.0006
##    200        0.6145             nan     0.1000   -0.0015
##    220        0.5951             nan     0.1000   -0.0010
##    240        0.5743             nan     0.1000   -0.0009
##    260        0.5593             nan     0.1000   -0.0016
##    280        0.5438             nan     0.1000   -0.0004
##    300        0.5297             nan     0.1000   -0.0010
##    320        0.5146             nan     0.1000   -0.0007
##    340        0.5030             nan     0.1000   -0.0008
##    360        0.4923             nan     0.1000   -0.0011
##    380        0.4828             nan     0.1000   -0.0010
##    400        0.4709             nan     0.1000   -0.0013
##    420        0.4585             nan     0.1000   -0.0009
##    440        0.4477             nan     0.1000   -0.0007
##    460        0.4369             nan     0.1000   -0.0007
##    480        0.4274             nan     0.1000   -0.0009
##    500        0.4187             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2481             nan     0.1000    0.0196
##      2        1.2097             nan     0.1000    0.0186
##      3        1.1804             nan     0.1000    0.0122
##      4        1.1550             nan     0.1000    0.0124
##      5        1.1280             nan     0.1000    0.0113
##      6        1.1089             nan     0.1000    0.0094
##      7        1.0853             nan     0.1000    0.0089
##      8        1.0650             nan     0.1000    0.0058
##      9        1.0457             nan     0.1000    0.0057
##     10        1.0312             nan     0.1000    0.0054
##     20        0.9328             nan     0.1000    0.0019
##     40        0.8419             nan     0.1000   -0.0010
##     60        0.7884             nan     0.1000   -0.0019
##     80        0.7601             nan     0.1000   -0.0024
##    100        0.7290             nan     0.1000   -0.0005
##    120        0.7059             nan     0.1000   -0.0017
##    140        0.6824             nan     0.1000   -0.0010
##    160        0.6643             nan     0.1000   -0.0007
##    180        0.6427             nan     0.1000   -0.0009
##    200        0.6245             nan     0.1000   -0.0013
##    220        0.6105             nan     0.1000   -0.0009
##    240        0.5943             nan     0.1000   -0.0024
##    260        0.5791             nan     0.1000   -0.0016
##    280        0.5635             nan     0.1000   -0.0009
##    300        0.5467             nan     0.1000   -0.0001
##    320        0.5316             nan     0.1000   -0.0008
##    340        0.5170             nan     0.1000   -0.0015
##    360        0.5037             nan     0.1000   -0.0012
##    380        0.4914             nan     0.1000   -0.0014
##    400        0.4810             nan     0.1000   -0.0010
##    420        0.4711             nan     0.1000   -0.0007
##    440        0.4618             nan     0.1000   -0.0016
##    460        0.4496             nan     0.1000   -0.0010
##    480        0.4375             nan     0.1000   -0.0005
##    500        0.4284             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2331             nan     0.1000    0.0275
##      2        1.1828             nan     0.1000    0.0224
##      3        1.1496             nan     0.1000    0.0144
##      4        1.1115             nan     0.1000    0.0172
##      5        1.0802             nan     0.1000    0.0128
##      6        1.0571             nan     0.1000    0.0100
##      7        1.0335             nan     0.1000    0.0083
##      8        1.0078             nan     0.1000    0.0083
##      9        0.9922             nan     0.1000    0.0054
##     10        0.9762             nan     0.1000    0.0056
##     20        0.8713             nan     0.1000    0.0012
##     40        0.7775             nan     0.1000    0.0000
##     60        0.7195             nan     0.1000   -0.0014
##     80        0.6730             nan     0.1000   -0.0011
##    100        0.6277             nan     0.1000   -0.0015
##    120        0.5931             nan     0.1000   -0.0010
##    140        0.5594             nan     0.1000   -0.0013
##    160        0.5333             nan     0.1000   -0.0027
##    180        0.5030             nan     0.1000   -0.0009
##    200        0.4787             nan     0.1000   -0.0005
##    220        0.4566             nan     0.1000   -0.0016
##    240        0.4347             nan     0.1000   -0.0010
##    260        0.4186             nan     0.1000   -0.0013
##    280        0.3991             nan     0.1000   -0.0007
##    300        0.3824             nan     0.1000   -0.0012
##    320        0.3665             nan     0.1000   -0.0011
##    340        0.3499             nan     0.1000   -0.0009
##    360        0.3354             nan     0.1000   -0.0008
##    380        0.3220             nan     0.1000   -0.0008
##    400        0.3076             nan     0.1000   -0.0008
##    420        0.2977             nan     0.1000   -0.0007
##    440        0.2874             nan     0.1000   -0.0003
##    460        0.2772             nan     0.1000   -0.0008
##    480        0.2655             nan     0.1000   -0.0009
##    500        0.2575             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2419             nan     0.1000    0.0245
##      2        1.1925             nan     0.1000    0.0187
##      3        1.1476             nan     0.1000    0.0106
##      4        1.1174             nan     0.1000    0.0122
##      5        1.0886             nan     0.1000    0.0119
##      6        1.0658             nan     0.1000    0.0085
##      7        1.0440             nan     0.1000    0.0063
##      8        1.0225             nan     0.1000    0.0063
##      9        1.0018             nan     0.1000    0.0091
##     10        0.9882             nan     0.1000    0.0052
##     20        0.8804             nan     0.1000    0.0015
##     40        0.7863             nan     0.1000    0.0003
##     60        0.7274             nan     0.1000   -0.0002
##     80        0.6860             nan     0.1000   -0.0026
##    100        0.6526             nan     0.1000   -0.0011
##    120        0.6150             nan     0.1000   -0.0008
##    140        0.5810             nan     0.1000   -0.0006
##    160        0.5535             nan     0.1000   -0.0011
##    180        0.5262             nan     0.1000   -0.0010
##    200        0.5032             nan     0.1000    0.0001
##    220        0.4812             nan     0.1000   -0.0007
##    240        0.4586             nan     0.1000   -0.0010
##    260        0.4404             nan     0.1000   -0.0016
##    280        0.4256             nan     0.1000   -0.0014
##    300        0.4022             nan     0.1000   -0.0008
##    320        0.3837             nan     0.1000   -0.0006
##    340        0.3654             nan     0.1000   -0.0001
##    360        0.3499             nan     0.1000   -0.0006
##    380        0.3361             nan     0.1000   -0.0010
##    400        0.3239             nan     0.1000   -0.0007
##    420        0.3125             nan     0.1000   -0.0004
##    440        0.2989             nan     0.1000   -0.0007
##    460        0.2879             nan     0.1000   -0.0015
##    480        0.2753             nan     0.1000   -0.0003
##    500        0.2651             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2417             nan     0.1000    0.0251
##      2        1.2023             nan     0.1000    0.0182
##      3        1.1648             nan     0.1000    0.0169
##      4        1.1328             nan     0.1000    0.0130
##      5        1.0954             nan     0.1000    0.0164
##      6        1.0705             nan     0.1000    0.0074
##      7        1.0468             nan     0.1000    0.0088
##      8        1.0306             nan     0.1000    0.0043
##      9        1.0113             nan     0.1000    0.0058
##     10        0.9958             nan     0.1000    0.0051
##     20        0.8838             nan     0.1000    0.0004
##     40        0.7849             nan     0.1000   -0.0017
##     60        0.7251             nan     0.1000   -0.0001
##     80        0.6814             nan     0.1000   -0.0008
##    100        0.6436             nan     0.1000   -0.0012
##    120        0.6102             nan     0.1000   -0.0000
##    140        0.5765             nan     0.1000   -0.0001
##    160        0.5481             nan     0.1000    0.0002
##    180        0.5223             nan     0.1000   -0.0008
##    200        0.4986             nan     0.1000   -0.0007
##    220        0.4742             nan     0.1000   -0.0014
##    240        0.4538             nan     0.1000   -0.0016
##    260        0.4318             nan     0.1000   -0.0004
##    280        0.4131             nan     0.1000   -0.0006
##    300        0.3982             nan     0.1000   -0.0009
##    320        0.3809             nan     0.1000   -0.0013
##    340        0.3672             nan     0.1000   -0.0012
##    360        0.3505             nan     0.1000   -0.0005
##    380        0.3366             nan     0.1000   -0.0015
##    400        0.3211             nan     0.1000   -0.0003
##    420        0.3086             nan     0.1000   -0.0005
##    440        0.2971             nan     0.1000   -0.0007
##    460        0.2865             nan     0.1000   -0.0007
##    480        0.2759             nan     0.1000   -0.0008
##    500        0.2650             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2220             nan     0.2000    0.0297
##      2        1.1809             nan     0.2000    0.0167
##      3        1.1455             nan     0.2000    0.0175
##      4        1.1121             nan     0.2000    0.0106
##      5        1.0868             nan     0.2000    0.0102
##      6        1.0595             nan     0.2000    0.0114
##      7        1.0412             nan     0.2000    0.0080
##      8        1.0235             nan     0.2000    0.0060
##      9        1.0073             nan     0.2000    0.0066
##     10        0.9922             nan     0.2000    0.0051
##     20        0.9167             nan     0.2000   -0.0003
##     40        0.8442             nan     0.2000    0.0006
##     60        0.8136             nan     0.2000   -0.0012
##     80        0.7883             nan     0.2000   -0.0010
##    100        0.7670             nan     0.2000   -0.0015
##    120        0.7518             nan     0.2000   -0.0006
##    140        0.7401             nan     0.2000   -0.0020
##    160        0.7258             nan     0.2000   -0.0013
##    180        0.7179             nan     0.2000   -0.0020
##    200        0.7079             nan     0.2000   -0.0018
##    220        0.7023             nan     0.2000   -0.0021
##    240        0.6926             nan     0.2000   -0.0025
##    260        0.6839             nan     0.2000   -0.0019
##    280        0.6771             nan     0.2000   -0.0010
##    300        0.6711             nan     0.2000   -0.0024
##    320        0.6607             nan     0.2000   -0.0036
##    340        0.6550             nan     0.2000   -0.0024
##    360        0.6478             nan     0.2000   -0.0018
##    380        0.6433             nan     0.2000   -0.0017
##    400        0.6372             nan     0.2000   -0.0042
##    420        0.6268             nan     0.2000   -0.0037
##    440        0.6191             nan     0.2000   -0.0018
##    460        0.6149             nan     0.2000   -0.0029
##    480        0.6089             nan     0.2000   -0.0012
##    500        0.6038             nan     0.2000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2255             nan     0.2000    0.0223
##      2        1.1793             nan     0.2000    0.0223
##      3        1.1486             nan     0.2000    0.0146
##      4        1.1188             nan     0.2000    0.0120
##      5        1.0877             nan     0.2000    0.0129
##      6        1.0646             nan     0.2000    0.0095
##      7        1.0443             nan     0.2000    0.0080
##      8        1.0251             nan     0.2000    0.0047
##      9        1.0066             nan     0.2000    0.0054
##     10        0.9933             nan     0.2000    0.0052
##     20        0.9084             nan     0.2000    0.0030
##     40        0.8528             nan     0.2000   -0.0027
##     60        0.8206             nan     0.2000   -0.0024
##     80        0.7969             nan     0.2000   -0.0002
##    100        0.7780             nan     0.2000   -0.0009
##    120        0.7662             nan     0.2000   -0.0015
##    140        0.7559             nan     0.2000   -0.0025
##    160        0.7446             nan     0.2000   -0.0006
##    180        0.7334             nan     0.2000   -0.0009
##    200        0.7197             nan     0.2000   -0.0026
##    220        0.7084             nan     0.2000   -0.0019
##    240        0.6943             nan     0.2000   -0.0012
##    260        0.6864             nan     0.2000   -0.0025
##    280        0.6796             nan     0.2000   -0.0020
##    300        0.6729             nan     0.2000   -0.0041
##    320        0.6669             nan     0.2000   -0.0013
##    340        0.6555             nan     0.2000   -0.0012
##    360        0.6494             nan     0.2000   -0.0023
##    380        0.6390             nan     0.2000   -0.0012
##    400        0.6335             nan     0.2000   -0.0022
##    420        0.6252             nan     0.2000   -0.0014
##    440        0.6206             nan     0.2000   -0.0013
##    460        0.6147             nan     0.2000   -0.0027
##    480        0.6100             nan     0.2000   -0.0041
##    500        0.6028             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2266             nan     0.2000    0.0301
##      2        1.1832             nan     0.2000    0.0192
##      3        1.1504             nan     0.2000    0.0131
##      4        1.1193             nan     0.2000    0.0127
##      5        1.0897             nan     0.2000    0.0136
##      6        1.0640             nan     0.2000    0.0134
##      7        1.0424             nan     0.2000    0.0077
##      8        1.0242             nan     0.2000    0.0062
##      9        1.0101             nan     0.2000    0.0026
##     10        0.9961             nan     0.2000    0.0051
##     20        0.9218             nan     0.2000    0.0017
##     40        0.8427             nan     0.2000   -0.0014
##     60        0.8094             nan     0.2000   -0.0015
##     80        0.7889             nan     0.2000   -0.0019
##    100        0.7738             nan     0.2000   -0.0007
##    120        0.7579             nan     0.2000   -0.0017
##    140        0.7473             nan     0.2000   -0.0003
##    160        0.7379             nan     0.2000   -0.0030
##    180        0.7307             nan     0.2000   -0.0013
##    200        0.7219             nan     0.2000   -0.0006
##    220        0.7140             nan     0.2000   -0.0023
##    240        0.7012             nan     0.2000   -0.0017
##    260        0.6925             nan     0.2000   -0.0027
##    280        0.6866             nan     0.2000   -0.0009
##    300        0.6764             nan     0.2000   -0.0034
##    320        0.6655             nan     0.2000   -0.0015
##    340        0.6571             nan     0.2000   -0.0015
##    360        0.6494             nan     0.2000   -0.0034
##    380        0.6408             nan     0.2000   -0.0018
##    400        0.6352             nan     0.2000   -0.0021
##    420        0.6290             nan     0.2000   -0.0005
##    440        0.6223             nan     0.2000   -0.0014
##    460        0.6147             nan     0.2000   -0.0014
##    480        0.6117             nan     0.2000   -0.0011
##    500        0.6079             nan     0.2000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2102             nan     0.2000    0.0343
##      2        1.1474             nan     0.2000    0.0269
##      3        1.0991             nan     0.2000    0.0215
##      4        1.0634             nan     0.2000    0.0162
##      5        1.0342             nan     0.2000    0.0071
##      6        1.0066             nan     0.2000    0.0092
##      7        0.9836             nan     0.2000    0.0089
##      8        0.9733             nan     0.2000   -0.0002
##      9        0.9584             nan     0.2000    0.0027
##     10        0.9379             nan     0.2000    0.0027
##     20        0.8561             nan     0.2000    0.0005
##     40        0.7679             nan     0.2000   -0.0040
##     60        0.7129             nan     0.2000   -0.0017
##     80        0.6581             nan     0.2000   -0.0013
##    100        0.6208             nan     0.2000   -0.0014
##    120        0.5909             nan     0.2000   -0.0030
##    140        0.5546             nan     0.2000   -0.0016
##    160        0.5200             nan     0.2000   -0.0027
##    180        0.4907             nan     0.2000   -0.0028
##    200        0.4633             nan     0.2000   -0.0026
##    220        0.4428             nan     0.2000   -0.0032
##    240        0.4275             nan     0.2000   -0.0011
##    260        0.4119             nan     0.2000   -0.0023
##    280        0.3931             nan     0.2000   -0.0019
##    300        0.3756             nan     0.2000   -0.0009
##    320        0.3623             nan     0.2000   -0.0002
##    340        0.3466             nan     0.2000   -0.0019
##    360        0.3314             nan     0.2000   -0.0016
##    380        0.3166             nan     0.2000   -0.0004
##    400        0.3033             nan     0.2000   -0.0018
##    420        0.2886             nan     0.2000   -0.0011
##    440        0.2797             nan     0.2000   -0.0008
##    460        0.2661             nan     0.2000   -0.0009
##    480        0.2587             nan     0.2000   -0.0017
##    500        0.2486             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2070             nan     0.2000    0.0379
##      2        1.1516             nan     0.2000    0.0248
##      3        1.1092             nan     0.2000    0.0187
##      4        1.0700             nan     0.2000    0.0160
##      5        1.0316             nan     0.2000    0.0150
##      6        1.0083             nan     0.2000    0.0056
##      7        0.9882             nan     0.2000    0.0076
##      8        0.9665             nan     0.2000    0.0079
##      9        0.9508             nan     0.2000    0.0034
##     10        0.9364             nan     0.2000    0.0017
##     20        0.8534             nan     0.2000   -0.0013
##     40        0.7650             nan     0.2000   -0.0038
##     60        0.7149             nan     0.2000   -0.0023
##     80        0.6748             nan     0.2000   -0.0014
##    100        0.6312             nan     0.2000   -0.0019
##    120        0.5915             nan     0.2000   -0.0021
##    140        0.5672             nan     0.2000   -0.0023
##    160        0.5278             nan     0.2000   -0.0011
##    180        0.4988             nan     0.2000   -0.0010
##    200        0.4740             nan     0.2000   -0.0018
##    220        0.4553             nan     0.2000   -0.0028
##    240        0.4299             nan     0.2000   -0.0005
##    260        0.4148             nan     0.2000   -0.0017
##    280        0.4007             nan     0.2000   -0.0010
##    300        0.3832             nan     0.2000   -0.0002
##    320        0.3706             nan     0.2000   -0.0025
##    340        0.3509             nan     0.2000   -0.0014
##    360        0.3358             nan     0.2000   -0.0022
##    380        0.3234             nan     0.2000   -0.0010
##    400        0.3102             nan     0.2000   -0.0018
##    420        0.2992             nan     0.2000   -0.0027
##    440        0.2890             nan     0.2000   -0.0019
##    460        0.2798             nan     0.2000   -0.0014
##    480        0.2693             nan     0.2000   -0.0012
##    500        0.2564             nan     0.2000   -0.0026
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2118             nan     0.2000    0.0346
##      2        1.1434             nan     0.2000    0.0280
##      3        1.0924             nan     0.2000    0.0234
##      4        1.0475             nan     0.2000    0.0144
##      5        1.0187             nan     0.2000    0.0106
##      6        0.9963             nan     0.2000    0.0072
##      7        0.9709             nan     0.2000    0.0098
##      8        0.9557             nan     0.2000    0.0052
##      9        0.9409             nan     0.2000    0.0041
##     10        0.9263             nan     0.2000    0.0043
##     20        0.8511             nan     0.2000   -0.0027
##     40        0.7763             nan     0.2000   -0.0047
##     60        0.7139             nan     0.2000   -0.0028
##     80        0.6663             nan     0.2000   -0.0017
##    100        0.6277             nan     0.2000   -0.0030
##    120        0.5975             nan     0.2000   -0.0007
##    140        0.5685             nan     0.2000   -0.0004
##    160        0.5383             nan     0.2000   -0.0016
##    180        0.5133             nan     0.2000   -0.0008
##    200        0.4843             nan     0.2000   -0.0017
##    220        0.4676             nan     0.2000   -0.0016
##    240        0.4442             nan     0.2000   -0.0035
##    260        0.4249             nan     0.2000   -0.0020
##    280        0.4041             nan     0.2000   -0.0024
##    300        0.3863             nan     0.2000   -0.0029
##    320        0.3663             nan     0.2000   -0.0013
##    340        0.3506             nan     0.2000   -0.0018
##    360        0.3390             nan     0.2000   -0.0013
##    380        0.3271             nan     0.2000   -0.0016
##    400        0.3100             nan     0.2000   -0.0002
##    420        0.3013             nan     0.2000   -0.0005
##    440        0.2905             nan     0.2000   -0.0031
##    460        0.2779             nan     0.2000   -0.0014
##    480        0.2688             nan     0.2000   -0.0004
##    500        0.2571             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1908             nan     0.2000    0.0427
##      2        1.1187             nan     0.2000    0.0328
##      3        1.0650             nan     0.2000    0.0214
##      4        1.0267             nan     0.2000    0.0093
##      5        0.9934             nan     0.2000    0.0112
##      6        0.9601             nan     0.2000    0.0117
##      7        0.9345             nan     0.2000    0.0101
##      8        0.9118             nan     0.2000    0.0051
##      9        0.8879             nan     0.2000    0.0042
##     10        0.8754             nan     0.2000    0.0005
##     20        0.7870             nan     0.2000   -0.0004
##     40        0.6949             nan     0.2000   -0.0029
##     60        0.6296             nan     0.2000   -0.0008
##     80        0.5605             nan     0.2000   -0.0070
##    100        0.5096             nan     0.2000   -0.0014
##    120        0.4610             nan     0.2000   -0.0023
##    140        0.4165             nan     0.2000   -0.0005
##    160        0.3861             nan     0.2000   -0.0020
##    180        0.3549             nan     0.2000   -0.0016
##    200        0.3326             nan     0.2000   -0.0012
##    220        0.3104             nan     0.2000   -0.0007
##    240        0.2874             nan     0.2000   -0.0024
##    260        0.2687             nan     0.2000   -0.0009
##    280        0.2468             nan     0.2000   -0.0001
##    300        0.2314             nan     0.2000   -0.0011
##    320        0.2187             nan     0.2000   -0.0013
##    340        0.2031             nan     0.2000   -0.0009
##    360        0.1881             nan     0.2000   -0.0008
##    380        0.1739             nan     0.2000   -0.0001
##    400        0.1649             nan     0.2000   -0.0010
##    420        0.1551             nan     0.2000   -0.0005
##    440        0.1484             nan     0.2000   -0.0007
##    460        0.1402             nan     0.2000   -0.0006
##    480        0.1312             nan     0.2000   -0.0008
##    500        0.1230             nan     0.2000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1900             nan     0.2000    0.0409
##      2        1.1268             nan     0.2000    0.0312
##      3        1.0683             nan     0.2000    0.0216
##      4        1.0232             nan     0.2000    0.0134
##      5        1.0006             nan     0.2000    0.0031
##      6        0.9780             nan     0.2000    0.0032
##      7        0.9515             nan     0.2000    0.0063
##      8        0.9309             nan     0.2000    0.0067
##      9        0.9120             nan     0.2000    0.0025
##     10        0.8962             nan     0.2000    0.0042
##     20        0.7978             nan     0.2000   -0.0008
##     40        0.6999             nan     0.2000   -0.0004
##     60        0.6289             nan     0.2000   -0.0044
##     80        0.5685             nan     0.2000   -0.0049
##    100        0.5150             nan     0.2000   -0.0042
##    120        0.4674             nan     0.2000   -0.0015
##    140        0.4313             nan     0.2000   -0.0031
##    160        0.3910             nan     0.2000   -0.0000
##    180        0.3573             nan     0.2000   -0.0008
##    200        0.3318             nan     0.2000   -0.0024
##    220        0.3028             nan     0.2000   -0.0018
##    240        0.2811             nan     0.2000   -0.0014
##    260        0.2596             nan     0.2000   -0.0014
##    280        0.2380             nan     0.2000   -0.0021
##    300        0.2199             nan     0.2000   -0.0016
##    320        0.2029             nan     0.2000   -0.0005
##    340        0.1895             nan     0.2000   -0.0010
##    360        0.1785             nan     0.2000   -0.0014
##    380        0.1692             nan     0.2000   -0.0013
##    400        0.1583             nan     0.2000   -0.0012
##    420        0.1508             nan     0.2000   -0.0008
##    440        0.1411             nan     0.2000   -0.0008
##    460        0.1337             nan     0.2000   -0.0008
##    480        0.1254             nan     0.2000   -0.0007
##    500        0.1177             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1920             nan     0.2000    0.0418
##      2        1.1180             nan     0.2000    0.0225
##      3        1.0644             nan     0.2000    0.0223
##      4        1.0120             nan     0.2000    0.0219
##      5        0.9819             nan     0.2000    0.0075
##      6        0.9536             nan     0.2000    0.0104
##      7        0.9335             nan     0.2000    0.0045
##      8        0.9132             nan     0.2000    0.0001
##      9        0.8984             nan     0.2000    0.0012
##     10        0.8884             nan     0.2000    0.0001
##     20        0.8031             nan     0.2000    0.0004
##     40        0.6966             nan     0.2000   -0.0014
##     60        0.6275             nan     0.2000   -0.0014
##     80        0.5695             nan     0.2000   -0.0021
##    100        0.5205             nan     0.2000   -0.0020
##    120        0.4803             nan     0.2000   -0.0037
##    140        0.4377             nan     0.2000   -0.0023
##    160        0.4066             nan     0.2000   -0.0011
##    180        0.3746             nan     0.2000   -0.0018
##    200        0.3441             nan     0.2000   -0.0006
##    220        0.3166             nan     0.2000   -0.0018
##    240        0.2851             nan     0.2000   -0.0002
##    260        0.2642             nan     0.2000   -0.0013
##    280        0.2455             nan     0.2000   -0.0006
##    300        0.2281             nan     0.2000   -0.0010
##    320        0.2107             nan     0.2000   -0.0018
##    340        0.1967             nan     0.2000   -0.0007
##    360        0.1835             nan     0.2000   -0.0005
##    380        0.1710             nan     0.2000   -0.0017
##    400        0.1605             nan     0.2000   -0.0005
##    420        0.1494             nan     0.2000   -0.0009
##    440        0.1391             nan     0.2000   -0.0010
##    460        0.1309             nan     0.2000   -0.0006
##    480        0.1231             nan     0.2000   -0.0014
##    500        0.1171             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2068             nan     0.3000    0.0403
##      2        1.1444             nan     0.3000    0.0309
##      3        1.1035             nan     0.3000    0.0120
##      4        1.0672             nan     0.3000    0.0128
##      5        1.0325             nan     0.3000    0.0158
##      6        1.0088             nan     0.3000    0.0097
##      7        0.9861             nan     0.3000    0.0095
##      8        0.9720             nan     0.3000    0.0051
##      9        0.9554             nan     0.3000    0.0070
##     10        0.9441             nan     0.3000    0.0049
##     20        0.8742             nan     0.3000   -0.0033
##     40        0.8143             nan     0.3000   -0.0062
##     60        0.7823             nan     0.3000   -0.0028
##     80        0.7689             nan     0.3000   -0.0029
##    100        0.7522             nan     0.3000   -0.0037
##    120        0.7344             nan     0.3000   -0.0031
##    140        0.7177             nan     0.3000   -0.0046
##    160        0.7040             nan     0.3000   -0.0031
##    180        0.6916             nan     0.3000   -0.0022
##    200        0.6739             nan     0.3000   -0.0015
##    220        0.6641             nan     0.3000   -0.0041
##    240        0.6532             nan     0.3000    0.0000
##    260        0.6451             nan     0.3000   -0.0030
##    280        0.6310             nan     0.3000   -0.0007
##    300        0.6250             nan     0.3000   -0.0066
##    320        0.6104             nan     0.3000   -0.0025
##    340        0.6059             nan     0.3000   -0.0034
##    360        0.5933             nan     0.3000   -0.0024
##    380        0.5846             nan     0.3000   -0.0017
##    400        0.5781             nan     0.3000   -0.0034
##    420        0.5738             nan     0.3000   -0.0026
##    440        0.5640             nan     0.3000   -0.0030
##    460        0.5615             nan     0.3000   -0.0052
##    480        0.5508             nan     0.3000   -0.0030
##    500        0.5468             nan     0.3000   -0.0031
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2058             nan     0.3000    0.0397
##      2        1.1563             nan     0.3000    0.0173
##      3        1.1047             nan     0.3000    0.0246
##      4        1.0678             nan     0.3000    0.0177
##      5        1.0338             nan     0.3000    0.0127
##      6        1.0119             nan     0.3000    0.0068
##      7        0.9905             nan     0.3000    0.0043
##      8        0.9741             nan     0.3000    0.0052
##      9        0.9602             nan     0.3000    0.0033
##     10        0.9446             nan     0.3000    0.0037
##     20        0.8796             nan     0.3000    0.0002
##     40        0.8215             nan     0.3000   -0.0025
##     60        0.7848             nan     0.3000   -0.0003
##     80        0.7577             nan     0.3000    0.0005
##    100        0.7353             nan     0.3000   -0.0029
##    120        0.7268             nan     0.3000   -0.0031
##    140        0.7156             nan     0.3000   -0.0051
##    160        0.7009             nan     0.3000    0.0004
##    180        0.6892             nan     0.3000   -0.0047
##    200        0.6739             nan     0.3000   -0.0033
##    220        0.6631             nan     0.3000   -0.0037
##    240        0.6510             nan     0.3000   -0.0021
##    260        0.6480             nan     0.3000   -0.0044
##    280        0.6334             nan     0.3000   -0.0045
##    300        0.6273             nan     0.3000   -0.0044
##    320        0.6187             nan     0.3000   -0.0050
##    340        0.6107             nan     0.3000   -0.0028
##    360        0.6008             nan     0.3000   -0.0013
##    380        0.5921             nan     0.3000   -0.0007
##    400        0.5873             nan     0.3000   -0.0030
##    420        0.5823             nan     0.3000   -0.0019
##    440        0.5762             nan     0.3000   -0.0011
##    460        0.5675             nan     0.3000   -0.0027
##    480        0.5610             nan     0.3000   -0.0028
##    500        0.5542             nan     0.3000   -0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2010             nan     0.3000    0.0432
##      2        1.1399             nan     0.3000    0.0280
##      3        1.1089             nan     0.3000    0.0152
##      4        1.0723             nan     0.3000    0.0111
##      5        1.0399             nan     0.3000    0.0157
##      6        1.0223             nan     0.3000    0.0032
##      7        0.9976             nan     0.3000    0.0092
##      8        0.9854             nan     0.3000   -0.0001
##      9        0.9674             nan     0.3000    0.0074
##     10        0.9540             nan     0.3000    0.0034
##     20        0.8758             nan     0.3000    0.0006
##     40        0.8224             nan     0.3000   -0.0003
##     60        0.7960             nan     0.3000   -0.0024
##     80        0.7702             nan     0.3000   -0.0002
##    100        0.7478             nan     0.3000   -0.0014
##    120        0.7330             nan     0.3000   -0.0025
##    140        0.7175             nan     0.3000   -0.0018
##    160        0.7062             nan     0.3000   -0.0027
##    180        0.6938             nan     0.3000   -0.0045
##    200        0.6814             nan     0.3000   -0.0047
##    220        0.6709             nan     0.3000   -0.0061
##    240        0.6635             nan     0.3000   -0.0024
##    260        0.6542             nan     0.3000   -0.0037
##    280        0.6428             nan     0.3000   -0.0014
##    300        0.6342             nan     0.3000   -0.0016
##    320        0.6293             nan     0.3000   -0.0022
##    340        0.6209             nan     0.3000   -0.0005
##    360        0.6070             nan     0.3000   -0.0009
##    380        0.6010             nan     0.3000   -0.0060
##    400        0.5919             nan     0.3000   -0.0039
##    420        0.5865             nan     0.3000   -0.0036
##    440        0.5819             nan     0.3000   -0.0028
##    460        0.5741             nan     0.3000   -0.0027
##    480        0.5707             nan     0.3000   -0.0025
##    500        0.5647             nan     0.3000   -0.0030
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1714             nan     0.3000    0.0518
##      2        1.1032             nan     0.3000    0.0255
##      3        1.0461             nan     0.3000    0.0224
##      4        1.0002             nan     0.3000    0.0163
##      5        0.9694             nan     0.3000    0.0018
##      6        0.9624             nan     0.3000   -0.0056
##      7        0.9458             nan     0.3000   -0.0026
##      8        0.9289             nan     0.3000    0.0040
##      9        0.9107             nan     0.3000    0.0049
##     10        0.8931             nan     0.3000    0.0054
##     20        0.8057             nan     0.3000   -0.0013
##     40        0.7316             nan     0.3000   -0.0043
##     60        0.6566             nan     0.3000    0.0004
##     80        0.6003             nan     0.3000   -0.0027
##    100        0.5565             nan     0.3000   -0.0048
##    120        0.5147             nan     0.3000   -0.0012
##    140        0.4695             nan     0.3000   -0.0026
##    160        0.4345             nan     0.3000   -0.0013
##    180        0.4055             nan     0.3000   -0.0018
##    200        0.3726             nan     0.3000   -0.0019
##    220        0.3484             nan     0.3000   -0.0028
##    240        0.3148             nan     0.3000   -0.0031
##    260        0.2941             nan     0.3000   -0.0012
##    280        0.2775             nan     0.3000   -0.0016
##    300        0.2592             nan     0.3000   -0.0012
##    320        0.2486             nan     0.3000   -0.0020
##    340        0.2346             nan     0.3000   -0.0019
##    360        0.2209             nan     0.3000   -0.0037
##    380        0.2069             nan     0.3000   -0.0021
##    400        0.1950             nan     0.3000   -0.0012
##    420        0.1844             nan     0.3000   -0.0015
##    440        0.1742             nan     0.3000   -0.0005
##    460        0.1662             nan     0.3000   -0.0017
##    480        0.1576             nan     0.3000   -0.0011
##    500        0.1493             nan     0.3000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1824             nan     0.3000    0.0530
##      2        1.0948             nan     0.3000    0.0376
##      3        1.0375             nan     0.3000    0.0232
##      4        0.9967             nan     0.3000    0.0148
##      5        0.9601             nan     0.3000    0.0102
##      6        0.9376             nan     0.3000    0.0067
##      7        0.9125             nan     0.3000    0.0040
##      8        0.8949             nan     0.3000   -0.0015
##      9        0.8810             nan     0.3000    0.0022
##     10        0.8737             nan     0.3000   -0.0018
##     20        0.7896             nan     0.3000   -0.0057
##     40        0.7024             nan     0.3000   -0.0057
##     60        0.6399             nan     0.3000   -0.0081
##     80        0.5926             nan     0.3000   -0.0028
##    100        0.5504             nan     0.3000   -0.0029
##    120        0.5160             nan     0.3000   -0.0041
##    140        0.4681             nan     0.3000   -0.0049
##    160        0.4369             nan     0.3000   -0.0017
##    180        0.4158             nan     0.3000   -0.0033
##    200        0.3878             nan     0.3000   -0.0051
##    220        0.3584             nan     0.3000   -0.0017
##    240        0.3362             nan     0.3000   -0.0001
##    260        0.3209             nan     0.3000    0.0004
##    280        0.3040             nan     0.3000   -0.0033
##    300        0.2827             nan     0.3000   -0.0030
##    320        0.2633             nan     0.3000   -0.0014
##    340        0.2462             nan     0.3000   -0.0009
##    360        0.2333             nan     0.3000   -0.0025
##    380        0.2219             nan     0.3000   -0.0010
##    400        0.2090             nan     0.3000   -0.0014
##    420        0.1964             nan     0.3000   -0.0015
##    440        0.1903             nan     0.3000   -0.0007
##    460        0.1808             nan     0.3000   -0.0012
##    480        0.1728             nan     0.3000   -0.0015
##    500        0.1659             nan     0.3000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1823             nan     0.3000    0.0442
##      2        1.1129             nan     0.3000    0.0285
##      3        1.0515             nan     0.3000    0.0243
##      4        1.0143             nan     0.3000    0.0032
##      5        0.9940             nan     0.3000   -0.0000
##      6        0.9710             nan     0.3000    0.0069
##      7        0.9441             nan     0.3000    0.0065
##      8        0.9361             nan     0.3000   -0.0075
##      9        0.9166             nan     0.3000    0.0057
##     10        0.9088             nan     0.3000   -0.0056
##     20        0.8252             nan     0.3000   -0.0079
##     40        0.7328             nan     0.3000   -0.0013
##     60        0.6713             nan     0.3000   -0.0028
##     80        0.6223             nan     0.3000   -0.0029
##    100        0.5728             nan     0.3000   -0.0030
##    120        0.5332             nan     0.3000   -0.0040
##    140        0.4945             nan     0.3000   -0.0023
##    160        0.4554             nan     0.3000   -0.0031
##    180        0.4174             nan     0.3000   -0.0014
##    200        0.3906             nan     0.3000   -0.0017
##    220        0.3609             nan     0.3000   -0.0014
##    240        0.3361             nan     0.3000   -0.0015
##    260        0.3206             nan     0.3000   -0.0035
##    280        0.2995             nan     0.3000   -0.0019
##    300        0.2820             nan     0.3000   -0.0012
##    320        0.2692             nan     0.3000   -0.0035
##    340        0.2515             nan     0.3000    0.0000
##    360        0.2339             nan     0.3000   -0.0020
##    380        0.2222             nan     0.3000   -0.0015
##    400        0.2129             nan     0.3000   -0.0016
##    420        0.2033             nan     0.3000   -0.0025
##    440        0.1943             nan     0.3000   -0.0008
##    460        0.1845             nan     0.3000   -0.0023
##    480        0.1755             nan     0.3000   -0.0016
##    500        0.1660             nan     0.3000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1474             nan     0.3000    0.0740
##      2        1.0741             nan     0.3000    0.0186
##      3        1.0087             nan     0.3000    0.0233
##      4        0.9615             nan     0.3000    0.0142
##      5        0.9339             nan     0.3000    0.0026
##      6        0.8991             nan     0.3000    0.0108
##      7        0.8794             nan     0.3000    0.0033
##      8        0.8563             nan     0.3000    0.0035
##      9        0.8367             nan     0.3000    0.0013
##     10        0.8315             nan     0.3000   -0.0076
##     20        0.7451             nan     0.3000   -0.0053
##     40        0.6242             nan     0.3000   -0.0066
##     60        0.6067             nan     0.3000   -0.0065
##     80           inf             nan     0.3000       nan
##    100           inf             nan     0.3000       nan
##    120           inf             nan     0.3000       nan
##    140           inf             nan     0.3000       nan
##    160           inf             nan     0.3000       nan
##    180           inf             nan     0.3000       nan
##    200           inf             nan     0.3000       nan
##    220           inf             nan     0.3000       nan
##    240           inf             nan     0.3000       nan
##    260           inf             nan     0.3000       nan
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1562             nan     0.3000    0.0615
##      2        1.0486             nan     0.3000    0.0437
##      3        0.9957             nan     0.3000    0.0191
##      4        0.9591             nan     0.3000    0.0093
##      5        0.9265             nan     0.3000    0.0042
##      6        0.9007             nan     0.3000    0.0096
##      7        0.8819             nan     0.3000    0.0047
##      8        0.8684             nan     0.3000   -0.0037
##      9        0.8449             nan     0.3000    0.0034
##     10        0.8320             nan     0.3000    0.0009
##     20        0.7565             nan     0.3000   -0.0067
##     40        0.6449             nan     0.3000   -0.0054
##     60        0.5561             nan     0.3000   -0.0065
##     80        0.4738             nan     0.3000   -0.0049
##    100        0.4083             nan     0.3000   -0.0041
##    120        0.3557             nan     0.3000   -0.0019
##    140        0.3016             nan     0.3000   -0.0015
##    160        0.2692             nan     0.3000   -0.0015
##    180        0.2389             nan     0.3000   -0.0034
##    200        0.2188             nan     0.3000   -0.0037
##    220        0.1986             nan     0.3000   -0.0014
##    240        0.1795             nan     0.3000   -0.0037
##    260        0.1638             nan     0.3000   -0.0017
##    280        0.1450             nan     0.3000   -0.0007
##    300        0.1327             nan     0.3000   -0.0008
##    320        0.1231             nan     0.3000   -0.0005
##    340        0.1107             nan     0.3000   -0.0009
##    360        0.0991             nan     0.3000   -0.0005
##    380        0.0912             nan     0.3000   -0.0003
##    400        0.0832             nan     0.3000   -0.0007
##    420        0.0786             nan     0.3000   -0.0005
##    440        0.0713             nan     0.3000   -0.0003
##    460        0.0647             nan     0.3000   -0.0003
##    480        0.0593             nan     0.3000   -0.0006
##    500        0.0549             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1552             nan     0.3000    0.0557
##      2        1.0707             nan     0.3000    0.0201
##      3        1.0232             nan     0.3000    0.0136
##      4        0.9852             nan     0.3000    0.0100
##      5        0.9412             nan     0.3000    0.0149
##      6        0.9118             nan     0.3000    0.0128
##      7        0.8962             nan     0.3000    0.0002
##      8        0.8807             nan     0.3000   -0.0027
##      9        0.8625             nan     0.3000    0.0015
##     10        0.8501             nan     0.3000   -0.0031
##     20        0.7590             nan     0.3000   -0.0031
##     40        0.6451             nan     0.3000   -0.0044
##     60        0.5640             nan     0.3000   -0.0039
##     80        0.4938             nan     0.3000   -0.0059
##    100        0.4369             nan     0.3000   -0.0015
##    120        0.3833             nan     0.3000   -0.0014
##    140        0.3427             nan     0.3000   -0.0039
##    160        0.3027             nan     0.3000   -0.0009
##    180        0.2767             nan     0.3000   -0.0017
##    200        0.2435             nan     0.3000   -0.0017
##    220        0.2228             nan     0.3000   -0.0012
##    240        0.2033             nan     0.3000   -0.0010
##    260        0.1852             nan     0.3000   -0.0031
##    280        0.1688             nan     0.3000   -0.0013
##    300        0.1538             nan     0.3000   -0.0008
##    320        0.1404             nan     0.3000   -0.0007
##    340        0.1259             nan     0.3000   -0.0008
##    360        0.1139             nan     0.3000   -0.0009
##    380        0.1059             nan     0.3000   -0.0015
##    400        0.0947             nan     0.3000   -0.0003
##    420        0.0882             nan     0.3000   -0.0011
##    440        0.0807             nan     0.3000   -0.0005
##    460        0.0736             nan     0.3000   -0.0008
##    480        0.0672             nan     0.3000   -0.0004
##    500        0.0621             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1664             nan     0.5000    0.0587
##      2        1.0954             nan     0.5000    0.0324
##      3        1.0381             nan     0.5000    0.0228
##      4        1.0134             nan     0.5000    0.0060
##      5        0.9795             nan     0.5000    0.0132
##      6        0.9616             nan     0.5000    0.0025
##      7        0.9346             nan     0.5000    0.0059
##      8        0.9234             nan     0.5000    0.0004
##      9        0.9078             nan     0.5000    0.0012
##     10        0.8974             nan     0.5000   -0.0027
##     20        0.8410             nan     0.5000   -0.0042
##     40        0.7949             nan     0.5000   -0.0038
##     60        3.5372             nan     0.5000   -0.0010
##     80        3.5128             nan     0.5000   -0.0007
##    100        3.4973             nan     0.5000    0.0000
##    120        3.4873             nan     0.5000   -0.0065
##    140        3.4807             nan     0.5000   -0.0012
##    160        3.4770             nan     0.5000   -0.0000
##    180        3.6933             nan     0.5000   -0.0013
##    200        3.6924             nan     0.5000   -0.0009
##    220        3.6838             nan     0.5000    0.0002
##    240        3.6794             nan     0.5000    0.0006
##    260        3.6703             nan     0.5000   -0.0002
##    280        3.6621             nan     0.5000   -0.0015
##    300        3.6593             nan     0.5000    0.0001
##    320        3.6609             nan     0.5000   -0.0002
##    340        3.6654             nan     0.5000    0.0000
##    360        3.6498             nan     0.5000    0.0000
##    380        3.4139             nan     0.5000   -0.0001
##    400        3.4100             nan     0.5000   -0.0018
##    420        3.4069             nan     0.5000   -0.0001
##    440        3.4127             nan     0.5000    0.0001
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1528             nan     0.5000    0.0606
##      2        1.0907             nan     0.5000    0.0261
##      3        1.0544             nan     0.5000    0.0134
##      4        1.0048             nan     0.5000    0.0102
##      5        0.9847             nan     0.5000    0.0018
##      6        0.9757             nan     0.5000   -0.0032
##      7        0.9623             nan     0.5000   -0.0016
##      8        0.9403             nan     0.5000    0.0008
##      9        0.9362             nan     0.5000   -0.0060
##     10        0.9266             nan     0.5000   -0.0019
##     20        0.8525             nan     0.5000   -0.0058
##     40        0.8044             nan     0.5000   -0.0043
##     60        0.7699             nan     0.5000   -0.0082
##     80        0.7391             nan     0.5000   -0.0066
##    100        0.7130             nan     0.5000   -0.0047
##    120        0.6859             nan     0.5000   -0.0018
##    140        0.6719             nan     0.5000   -0.0019
##    160        0.6505             nan     0.5000   -0.0081
##    180        0.6286             nan     0.5000   -0.0020
##    200        0.6215             nan     0.5000   -0.0082
##    220        0.5975             nan     0.5000    0.0010
##    240        0.5961             nan     0.5000   -0.0101
##    260        0.5741             nan     0.5000   -0.0046
##    280        0.5568             nan     0.5000   -0.0019
##    300        0.5516             nan     0.5000   -0.0046
##    320        0.5333             nan     0.5000   -0.0021
##    340        0.5289             nan     0.5000   -0.0014
##    360        0.5225             nan     0.5000   -0.0168
##    380        0.5045             nan     0.5000   -0.0020
##    400        0.4971             nan     0.5000   -0.0056
##    420        0.4877             nan     0.5000   -0.0016
##    440        0.4809             nan     0.5000   -0.0002
##    460        0.4722             nan     0.5000   -0.0070
##    480        0.4645             nan     0.5000   -0.0015
##    500        0.4612             nan     0.5000   -0.0034
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1559             nan     0.5000    0.0452
##      2        1.0754             nan     0.5000    0.0119
##      3        1.0264             nan     0.5000    0.0216
##      4        0.9938             nan     0.5000    0.0145
##      5        0.9719             nan     0.5000    0.0062
##      6        0.9553             nan     0.5000    0.0024
##      7        0.9428             nan     0.5000    0.0003
##      8        0.9250             nan     0.5000    0.0006
##      9        0.9115             nan     0.5000    0.0059
##     10        0.8988             nan     0.5000   -0.0008
##     20        0.8471             nan     0.5000   -0.0014
##     40        0.8155             nan     0.5000   -0.0086
##     60        0.7774             nan     0.5000   -0.0028
##     80        0.7456             nan     0.5000   -0.0025
##    100        0.7278             nan     0.5000   -0.0021
##    120        0.7050             nan     0.5000   -0.0062
##    140        0.6900             nan     0.5000   -0.0062
##    160        0.6759             nan     0.5000   -0.0054
##    180        0.6543             nan     0.5000   -0.0080
##    200        0.6449             nan     0.5000   -0.0064
##    220        0.6349             nan     0.5000   -0.0040
##    240        0.6178             nan     0.5000   -0.0051
##    260        0.6118             nan     0.5000   -0.0090
##    280        0.5921             nan     0.5000   -0.0054
##    300        0.5833             nan     0.5000   -0.0006
##    320        0.5815             nan     0.5000   -0.0120
##    340        0.5761             nan     0.5000   -0.0006
##    360        0.5649             nan     0.5000   -0.0076
##    380        0.5513             nan     0.5000   -0.0033
##    400        0.5358             nan     0.5000   -0.0017
##    420        0.5285             nan     0.5000   -0.0018
##    440        0.5186             nan     0.5000   -0.0070
##    460        0.5081             nan     0.5000   -0.0023
##    480        0.5046             nan     0.5000   -0.0049
##    500        0.4900             nan     0.5000   -0.0053
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1357             nan     0.5000    0.0665
##      2        1.0259             nan     0.5000    0.0308
##      3        0.9820             nan     0.5000    0.0143
##      4        0.9357             nan     0.5000    0.0102
##      5        0.9146             nan     0.5000   -0.0014
##      6        0.9037             nan     0.5000   -0.0108
##      7        0.8857             nan     0.5000   -0.0082
##      8        0.8783             nan     0.5000   -0.0065
##      9        0.8707             nan     0.5000   -0.0071
##     10        0.8500             nan     0.5000   -0.0017
##     20        0.7926             nan     0.5000   -0.0181
##     40        0.6990             nan     0.5000   -0.0035
##     60        0.6084             nan     0.5000   -0.0109
##     80        0.5411             nan     0.5000   -0.0046
##    100        1.4444             nan     0.5000   -0.0061
##    120        1.3870             nan     0.5000   -0.0003
##    140        1.2943             nan     0.5000   -0.0000
##    160        1.2197             nan     0.5000   -0.0046
##    180        1.4743             nan     0.5000   -0.0056
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1284             nan     0.5000    0.0781
##      2        1.0407             nan     0.5000    0.0349
##      3        0.9910             nan     0.5000    0.0187
##      4        0.9392             nan     0.5000    0.0128
##      5        0.9265             nan     0.5000   -0.0069
##      6        0.9138             nan     0.5000   -0.0091
##      7        0.8939             nan     0.5000   -0.0010
##      8        0.8853             nan     0.5000   -0.0075
##      9        0.8626             nan     0.5000   -0.0027
##     10        0.8627             nan     0.5000   -0.0232
##     20        0.7961             nan     0.5000   -0.0123
##     40        0.6952             nan     0.5000   -0.0088
##     60        0.6221             nan     0.5000   -0.0065
##     80      171.2534             nan     0.5000   -0.0031
##    100 62762966852.1521             nan     0.5000   -0.0124
##    120 62762966852.0974             nan     0.5000   -0.0057
##    140 62762966852.0733             nan     0.5000   -0.0059
##    160 62762975095.3167             nan     0.5000   -0.0034
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1291             nan     0.5000    0.0722
##      2        1.0396             nan     0.5000    0.0401
##      3        0.9887             nan     0.5000    0.0156
##      4        0.9610             nan     0.5000   -0.0014
##      5        0.9384             nan     0.5000    0.0036
##      6        0.9195             nan     0.5000    0.0015
##      7        0.8943             nan     0.5000    0.0018
##      8        0.8878             nan     0.5000   -0.0064
##      9        0.8606             nan     0.5000    0.0122
##     10        0.8570             nan     0.5000   -0.0089
##     20        0.7676             nan     0.5000   -0.0127
##     40        0.6707             nan     0.5000   -0.0084
##     60        0.6075             nan     0.5000   -0.0035
##     80        0.5596             nan     0.5000   -0.0094
##    100        0.5128             nan     0.5000   -0.0104
##    120        0.4629             nan     0.5000   -0.0079
##    140        0.4078             nan     0.5000   -0.0059
##    160        0.3591             nan     0.5000   -0.0048
##    180        0.3216             nan     0.5000   -0.0044
##    200        0.3025             nan     0.5000   -0.0057
##    220        0.2831             nan     0.5000   -0.0058
##    240        0.2510             nan     0.5000   -0.0020
##    260        0.2349             nan     0.5000   -0.0045
##    280        0.2070             nan     0.5000   -0.0017
##    300        0.1821             nan     0.5000   -0.0024
##    320        0.1649             nan     0.5000   -0.0011
##    340        0.1499             nan     0.5000   -0.0027
##    360        0.1373             nan     0.5000   -0.0023
##    380        0.1279             nan     0.5000   -0.0031
##    400        0.1135             nan     0.5000   -0.0004
##    420        0.1054             nan     0.5000   -0.0017
##    440        0.0981             nan     0.5000   -0.0013
##    460        0.0904             nan     0.5000   -0.0004
##    480        0.0839             nan     0.5000   -0.0020
##    500        0.0762             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0728             nan     0.5000    0.1029
##      2        0.9650             nan     0.5000    0.0291
##      3        0.9332             nan     0.5000   -0.0044
##      4        0.8901             nan     0.5000    0.0035
##      5        0.8657             nan     0.5000   -0.0010
##      6        0.8478             nan     0.5000   -0.0037
##      7        0.8254             nan     0.5000   -0.0015
##      8        0.8204             nan     0.5000   -0.0234
##      9        0.8103             nan     0.5000   -0.0081
##     10        0.7951             nan     0.5000   -0.0192
##     20        0.8923             nan     0.5000   -0.0096
##     40           inf             nan     0.5000       nan
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000    0.0001
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0855             nan     0.5000    0.0954
##      2        1.0182             nan     0.5000    0.0163
##      3        0.9519             nan     0.5000    0.0269
##      4        0.9181             nan     0.5000   -0.0047
##      5        0.8857             nan     0.5000    0.0039
##      6        0.8595             nan     0.5000   -0.0018
##      7        0.8414             nan     0.5000   -0.0175
##      8        0.8443             nan     0.5000   -0.0289
##      9        0.8271             nan     0.5000   -0.0084
##     10        0.8103             nan     0.5000   -0.0096
##     20        0.6887             nan     0.5000   -0.0086
##     40        0.5535             nan     0.5000   -0.0085
##     60        0.4389             nan     0.5000   -0.0086
##     80        0.3564             nan     0.5000   -0.0127
##    100        0.2935             nan     0.5000   -0.0044
##    120        0.2299             nan     0.5000   -0.0028
##    140        0.1900             nan     0.5000   -0.0033
##    160        0.1549             nan     0.5000   -0.0018
##    180        0.1323             nan     0.5000   -0.0006
##    200        0.1091             nan     0.5000   -0.0012
##    220        0.0946             nan     0.5000   -0.0008
##    240        0.0830             nan     0.5000   -0.0008
##    260        0.0723             nan     0.5000   -0.0003
##    280        0.0636             nan     0.5000   -0.0013
##    300        0.0566             nan     0.5000   -0.0003
##    320        0.0514             nan     0.5000   -0.0010
##    340        0.0451             nan     0.5000   -0.0010
##    360        0.0393             nan     0.5000   -0.0016
##    380        0.0349             nan     0.5000   -0.0004
##    400        0.0299             nan     0.5000   -0.0002
##    420        0.0271             nan     0.5000   -0.0006
##    440        0.0239             nan     0.5000   -0.0002
##    460        0.0217             nan     0.5000   -0.0004
##    480        0.0196             nan     0.5000   -0.0004
##    500        0.0174             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0779             nan     0.5000    0.0950
##      2        0.9666             nan     0.5000    0.0478
##      3        0.9289             nan     0.5000    0.0067
##      4        0.8984             nan     0.5000    0.0056
##      5        0.8787             nan     0.5000   -0.0058
##      6        0.8548             nan     0.5000   -0.0045
##      7        0.8320             nan     0.5000   -0.0009
##      8        0.8161             nan     0.5000   -0.0007
##      9        0.7992             nan     0.5000    0.0035
##     10        0.7872             nan     0.5000   -0.0051
##     20        0.6924             nan     0.5000   -0.0082
##     40        0.5611             nan     0.5000   -0.0070
##     60        0.4552             nan     0.5000   -0.0028
##     80        0.3742             nan     0.5000   -0.0062
##    100        0.3155             nan     0.5000   -0.0061
##    120        0.2629             nan     0.5000   -0.0028
##    140        0.2076             nan     0.5000   -0.0016
##    160        0.1753             nan     0.5000   -0.0015
##    180        0.1444             nan     0.5000   -0.0001
##    200        0.1209             nan     0.5000   -0.0016
##    220        0.1062             nan     0.5000   -0.0019
##    240        0.0879             nan     0.5000   -0.0012
##    260        0.0752             nan     0.5000   -0.0001
##    280        0.0655             nan     0.5000   -0.0004
##    300        0.0583             nan     0.5000   -0.0008
##    320        0.0505             nan     0.5000   -0.0007
##    340        0.0423             nan     0.5000   -0.0010
##    360        0.0371             nan     0.5000   -0.0004
##    380        0.0325             nan     0.5000   -0.0002
##    400        0.0294             nan     0.5000   -0.0002
##    420        0.0274             nan     0.5000   -0.0002
##    440        0.0240             nan     0.5000   -0.0006
##    460        0.0206             nan     0.5000   -0.0002
##    480        0.0183             nan     0.5000   -0.0001
##    500        0.0167             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1252             nan     1.0000    0.0759
##      2        1.0839             nan     1.0000    0.0048
##      3        1.0214             nan     1.0000    0.0238
##      4        1.0131             nan     1.0000   -0.0148
##      5        0.9855             nan     1.0000   -0.0132
##      6        0.9775             nan     1.0000   -0.0091
##      7        0.9785             nan     1.0000   -0.0135
##      8        0.9850             nan     1.0000   -0.0218
##      9        0.9549             nan     1.0000    0.0146
##     10        0.9593             nan     1.0000   -0.0161
##     20        0.9170             nan     1.0000   -0.0108
##     40        0.8270             nan     1.0000   -0.0139
##     60        0.8037             nan     1.0000   -0.0177
##     80        2.6195             nan     1.0000    0.0003
##    100        2.6011             nan     1.0000   -0.0041
##    120        2.5644             nan     1.0000   -0.0030
##    140        5.8721             nan     1.0000    0.0014
##    160        5.8955             nan     1.0000   -0.0445
##    180        5.8961             nan     1.0000    0.0056
##    200        5.8798             nan     1.0000    0.0023
##    220        5.8826             nan     1.0000   -0.0019
##    240        5.8829             nan     1.0000   -0.0041
##    260        5.8723             nan     1.0000    0.0006
##    280        5.8874             nan     1.0000   -0.0206
##    300        5.8725             nan     1.0000   -0.0004
##    320        5.8749             nan     1.0000   -0.0053
##    340        5.8525             nan     1.0000   -0.0007
##    360        5.8522             nan     1.0000   -0.0000
##    380        5.8525             nan     1.0000   -0.0002
##    400        5.8626             nan     1.0000   -0.0012
##    420        5.8420             nan     1.0000   -0.0084
##    440        5.8378             nan     1.0000    0.0007
##    460        5.8394             nan     1.0000   -0.0046
##    480        5.8426             nan     1.0000   -0.0096
##    500        5.8245             nan     1.0000   -0.0074
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1142             nan     1.0000    0.0706
##      2        1.0332             nan     1.0000    0.0375
##      3        0.9829             nan     1.0000    0.0100
##      4        0.9747             nan     1.0000   -0.0120
##      5        0.9510             nan     1.0000    0.0030
##      6        0.9440             nan     1.0000   -0.0040
##      7        0.9383             nan     1.0000   -0.0017
##      8        0.9375             nan     1.0000   -0.0161
##      9        0.9155             nan     1.0000    0.0111
##     10        0.9075             nan     1.0000   -0.0019
##     20        0.8959             nan     1.0000   -0.0220
##     40        0.8547             nan     1.0000   -0.0185
##     60     1214.8926             nan     1.0000   -0.0123
##     80     1215.2222             nan     1.0000   -0.0034
##    100     1215.2735             nan     1.0000    0.0344
##    120     1215.2386             nan     1.0000   -0.0004
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1181             nan     1.0000    0.0449
##      2        1.0354             nan     1.0000    0.0403
##      3        0.9951             nan     1.0000    0.0024
##      4        0.9941             nan     1.0000   -0.0196
##      5        0.9659             nan     1.0000    0.0150
##      6        0.9585             nan     1.0000   -0.0064
##      7        0.9310             nan     1.0000    0.0109
##      8        0.9419             nan     1.0000   -0.0199
##      9        0.9267             nan     1.0000    0.0044
##     10        0.9043             nan     1.0000    0.0116
##     20        1.9020             nan     1.0000    0.0005
##     40        1.8518             nan     1.0000   -0.0255
##     60        1.7922             nan     1.0000    0.0005
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0608             nan     1.0000    0.0992
##      2        0.9368             nan     1.0000    0.0401
##      3        0.9120             nan     1.0000   -0.0064
##      4        0.8978             nan     1.0000   -0.0039
##      5        0.8971             nan     1.0000   -0.0315
##      6        0.9016             nan     1.0000   -0.0471
##      7        0.8923             nan     1.0000   -0.0344
##      8        0.9003             nan     1.0000   -0.0158
##      9        0.9251             nan     1.0000   -0.0524
##     10        0.9042             nan     1.0000   -0.0111
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0495             nan     1.0000    0.1020
##      2        0.9848             nan     1.0000    0.0041
##      3        0.9687             nan     1.0000   -0.0193
##      4        0.9401             nan     1.0000   -0.0120
##      5        0.9204             nan     1.0000   -0.0189
##      6        0.9293             nan     1.0000   -0.0438
##      7        0.9040             nan     1.0000   -0.0050
##      8        0.9585             nan     1.0000   -0.0801
##      9        1.3293             nan     1.0000   -0.4286
##     10        1.3682             nan     1.0000   -0.0711
##     20        1.5033             nan     1.0000   -0.1508
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0912             nan     1.0000    0.0999
##      2        0.9630             nan     1.0000    0.0491
##      3        0.9670             nan     1.0000   -0.0498
##      4        0.9627             nan     1.0000   -0.0537
##      5        0.9280             nan     1.0000   -0.0246
##      6        0.9602             nan     1.0000   -0.0624
##      7        0.9620             nan     1.0000   -0.0340
##      8        0.9113             nan     1.0000    0.0130
##      9        0.8892             nan     1.0000   -0.0080
##     10        0.8987             nan     1.0000   -0.0316
##     20        0.9085             nan     1.0000   -0.0371
##     40        0.8190             nan     1.0000    0.0376
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140 5792165582463.2637             nan     1.0000       inf
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0389             nan     1.0000    0.0746
##      2        0.9580             nan     1.0000    0.0129
##      3        0.9224             nan     1.0000   -0.0190
##      4        0.8916             nan     1.0000   -0.0213
##      5        0.8946             nan     1.0000   -0.0444
##      6        0.9223             nan     1.0000   -0.0750
##      7        1.0956             nan     1.0000   -0.2297
##      8           inf             nan     1.0000      -inf
##      9           inf             nan     1.0000   -0.0788
##     10           inf             nan     1.0000   -0.0148
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0280             nan     1.0000    0.1068
##      2        0.9460             nan     1.0000    0.0042
##      3        0.9304             nan     1.0000   -0.0335
##      4        0.8782             nan     1.0000   -0.0003
##      5        0.8594             nan     1.0000   -0.0161
##      6        0.8460             nan     1.0000   -0.0175
##      7        0.8209             nan     1.0000   -0.0143
##      8        0.9540             nan     1.0000   -0.1705
##      9        0.9601             nan     1.0000   -0.0546
##     10        0.9629             nan     1.0000   -0.0307
##     20        0.8806             nan     1.0000   -0.0299
##     40        2.0977             nan     1.0000   -0.1409
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0037             nan     1.0000    0.0884
##      2        0.9525             nan     1.0000   -0.0136
##      3        0.9550             nan     1.0000   -0.0464
##      4        0.9750             nan     1.0000   -0.0635
##      5        0.9232             nan     1.0000   -0.0019
##      6        0.8851             nan     1.0000   -0.0167
##      7        0.8907             nan     1.0000   -0.0377
##      8        0.9736             nan     1.0000   -0.0473
##      9        0.9621             nan     1.0000   -0.0196
##     10        1.0457             nan     1.0000   -0.1380
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0001
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2860             nan     0.0010    0.0002
##     40        1.2787             nan     0.0010    0.0002
##     60        1.2715             nan     0.0010    0.0002
##     80        1.2646             nan     0.0010    0.0002
##    100        1.2582             nan     0.0010    0.0001
##    120        1.2520             nan     0.0010    0.0001
##    140        1.2461             nan     0.0010    0.0001
##    160        1.2402             nan     0.0010    0.0001
##    180        1.2346             nan     0.0010    0.0001
##    200        1.2292             nan     0.0010    0.0001
##    220        1.2239             nan     0.0010    0.0001
##    240        1.2186             nan     0.0010    0.0001
##    260        1.2136             nan     0.0010    0.0001
##    280        1.2088             nan     0.0010    0.0001
##    300        1.2040             nan     0.0010    0.0001
##    320        1.1993             nan     0.0010    0.0001
##    340        1.1950             nan     0.0010    0.0001
##    360        1.1907             nan     0.0010    0.0001
##    380        1.1864             nan     0.0010    0.0001
##    400        1.1823             nan     0.0010    0.0001
##    420        1.1785             nan     0.0010    0.0001
##    440        1.1747             nan     0.0010    0.0001
##    460        1.1708             nan     0.0010    0.0001
##    480        1.1669             nan     0.0010    0.0001
##    500        1.1632             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2921             nan     0.0010    0.0002
##      4        1.2917             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2906             nan     0.0010    0.0002
##      8        1.2902             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0001
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2787             nan     0.0010    0.0002
##     60        1.2717             nan     0.0010    0.0002
##     80        1.2649             nan     0.0010    0.0002
##    100        1.2581             nan     0.0010    0.0001
##    120        1.2518             nan     0.0010    0.0001
##    140        1.2454             nan     0.0010    0.0002
##    160        1.2397             nan     0.0010    0.0001
##    180        1.2340             nan     0.0010    0.0001
##    200        1.2287             nan     0.0010    0.0001
##    220        1.2235             nan     0.0010    0.0001
##    240        1.2182             nan     0.0010    0.0001
##    260        1.2134             nan     0.0010    0.0001
##    280        1.2084             nan     0.0010    0.0001
##    300        1.2036             nan     0.0010    0.0001
##    320        1.1991             nan     0.0010    0.0001
##    340        1.1946             nan     0.0010    0.0001
##    360        1.1902             nan     0.0010    0.0001
##    380        1.1857             nan     0.0010    0.0001
##    400        1.1817             nan     0.0010    0.0001
##    420        1.1778             nan     0.0010    0.0001
##    440        1.1738             nan     0.0010    0.0001
##    460        1.1702             nan     0.0010    0.0001
##    480        1.1665             nan     0.0010    0.0001
##    500        1.1628             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0001
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2903             nan     0.0010    0.0002
##      9        1.2899             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2857             nan     0.0010    0.0002
##     40        1.2786             nan     0.0010    0.0002
##     60        1.2715             nan     0.0010    0.0002
##     80        1.2647             nan     0.0010    0.0002
##    100        1.2582             nan     0.0010    0.0001
##    120        1.2519             nan     0.0010    0.0001
##    140        1.2460             nan     0.0010    0.0001
##    160        1.2401             nan     0.0010    0.0001
##    180        1.2345             nan     0.0010    0.0001
##    200        1.2291             nan     0.0010    0.0001
##    220        1.2237             nan     0.0010    0.0001
##    240        1.2182             nan     0.0010    0.0001
##    260        1.2131             nan     0.0010    0.0001
##    280        1.2081             nan     0.0010    0.0001
##    300        1.2033             nan     0.0010    0.0001
##    320        1.1987             nan     0.0010    0.0001
##    340        1.1945             nan     0.0010    0.0001
##    360        1.1902             nan     0.0010    0.0001
##    380        1.1858             nan     0.0010    0.0001
##    400        1.1818             nan     0.0010    0.0001
##    420        1.1779             nan     0.0010    0.0001
##    440        1.1740             nan     0.0010    0.0001
##    460        1.1702             nan     0.0010    0.0001
##    480        1.1663             nan     0.0010    0.0001
##    500        1.1626             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0003
##     20        1.2838             nan     0.0010    0.0002
##     40        1.2744             nan     0.0010    0.0002
##     60        1.2653             nan     0.0010    0.0002
##     80        1.2566             nan     0.0010    0.0002
##    100        1.2481             nan     0.0010    0.0002
##    120        1.2400             nan     0.0010    0.0002
##    140        1.2320             nan     0.0010    0.0002
##    160        1.2242             nan     0.0010    0.0002
##    180        1.2167             nan     0.0010    0.0002
##    200        1.2096             nan     0.0010    0.0002
##    220        1.2025             nan     0.0010    0.0002
##    240        1.1955             nan     0.0010    0.0002
##    260        1.1889             nan     0.0010    0.0001
##    280        1.1826             nan     0.0010    0.0001
##    300        1.1762             nan     0.0010    0.0001
##    320        1.1698             nan     0.0010    0.0001
##    340        1.1641             nan     0.0010    0.0001
##    360        1.1582             nan     0.0010    0.0001
##    380        1.1526             nan     0.0010    0.0001
##    400        1.1470             nan     0.0010    0.0001
##    420        1.1416             nan     0.0010    0.0001
##    440        1.1364             nan     0.0010    0.0001
##    460        1.1314             nan     0.0010    0.0001
##    480        1.1264             nan     0.0010    0.0001
##    500        1.1215             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2895             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2884             nan     0.0010    0.0003
##     20        1.2835             nan     0.0010    0.0002
##     40        1.2743             nan     0.0010    0.0002
##     60        1.2652             nan     0.0010    0.0002
##     80        1.2564             nan     0.0010    0.0002
##    100        1.2478             nan     0.0010    0.0002
##    120        1.2394             nan     0.0010    0.0002
##    140        1.2313             nan     0.0010    0.0002
##    160        1.2236             nan     0.0010    0.0002
##    180        1.2161             nan     0.0010    0.0002
##    200        1.2087             nan     0.0010    0.0002
##    220        1.2017             nan     0.0010    0.0001
##    240        1.1949             nan     0.0010    0.0001
##    260        1.1882             nan     0.0010    0.0001
##    280        1.1816             nan     0.0010    0.0002
##    300        1.1754             nan     0.0010    0.0001
##    320        1.1692             nan     0.0010    0.0001
##    340        1.1632             nan     0.0010    0.0001
##    360        1.1576             nan     0.0010    0.0001
##    380        1.1519             nan     0.0010    0.0001
##    400        1.1467             nan     0.0010    0.0001
##    420        1.1411             nan     0.0010    0.0001
##    440        1.1360             nan     0.0010    0.0001
##    460        1.1309             nan     0.0010    0.0001
##    480        1.1260             nan     0.0010    0.0001
##    500        1.1212             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0003
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2890             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2837             nan     0.0010    0.0002
##     40        1.2746             nan     0.0010    0.0002
##     60        1.2652             nan     0.0010    0.0002
##     80        1.2562             nan     0.0010    0.0002
##    100        1.2477             nan     0.0010    0.0002
##    120        1.2394             nan     0.0010    0.0002
##    140        1.2314             nan     0.0010    0.0002
##    160        1.2237             nan     0.0010    0.0001
##    180        1.2162             nan     0.0010    0.0001
##    200        1.2089             nan     0.0010    0.0001
##    220        1.2019             nan     0.0010    0.0002
##    240        1.1952             nan     0.0010    0.0002
##    260        1.1885             nan     0.0010    0.0001
##    280        1.1821             nan     0.0010    0.0002
##    300        1.1757             nan     0.0010    0.0001
##    320        1.1697             nan     0.0010    0.0001
##    340        1.1636             nan     0.0010    0.0001
##    360        1.1579             nan     0.0010    0.0001
##    380        1.1525             nan     0.0010    0.0001
##    400        1.1469             nan     0.0010    0.0001
##    420        1.1416             nan     0.0010    0.0001
##    440        1.1364             nan     0.0010    0.0001
##    460        1.1313             nan     0.0010    0.0001
##    480        1.1263             nan     0.0010    0.0001
##    500        1.1215             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2916             nan     0.0010    0.0003
##      4        1.2910             nan     0.0010    0.0002
##      5        1.2905             nan     0.0010    0.0003
##      6        1.2900             nan     0.0010    0.0002
##      7        1.2893             nan     0.0010    0.0002
##      8        1.2887             nan     0.0010    0.0003
##      9        1.2881             nan     0.0010    0.0003
##     10        1.2877             nan     0.0010    0.0002
##     20        1.2821             nan     0.0010    0.0002
##     40        1.2714             nan     0.0010    0.0002
##     60        1.2609             nan     0.0010    0.0002
##     80        1.2508             nan     0.0010    0.0002
##    100        1.2413             nan     0.0010    0.0002
##    120        1.2318             nan     0.0010    0.0002
##    140        1.2227             nan     0.0010    0.0002
##    160        1.2139             nan     0.0010    0.0002
##    180        1.2053             nan     0.0010    0.0002
##    200        1.1971             nan     0.0010    0.0001
##    220        1.1888             nan     0.0010    0.0002
##    240        1.1808             nan     0.0010    0.0002
##    260        1.1731             nan     0.0010    0.0002
##    280        1.1658             nan     0.0010    0.0002
##    300        1.1583             nan     0.0010    0.0001
##    320        1.1510             nan     0.0010    0.0001
##    340        1.1442             nan     0.0010    0.0001
##    360        1.1376             nan     0.0010    0.0001
##    380        1.1311             nan     0.0010    0.0002
##    400        1.1248             nan     0.0010    0.0001
##    420        1.1188             nan     0.0010    0.0001
##    440        1.1129             nan     0.0010    0.0001
##    460        1.1072             nan     0.0010    0.0002
##    480        1.1014             nan     0.0010    0.0001
##    500        1.0959             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2916             nan     0.0010    0.0003
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2905             nan     0.0010    0.0002
##      6        1.2899             nan     0.0010    0.0002
##      7        1.2893             nan     0.0010    0.0002
##      8        1.2888             nan     0.0010    0.0002
##      9        1.2883             nan     0.0010    0.0002
##     10        1.2877             nan     0.0010    0.0003
##     20        1.2824             nan     0.0010    0.0003
##     40        1.2714             nan     0.0010    0.0002
##     60        1.2607             nan     0.0010    0.0002
##     80        1.2507             nan     0.0010    0.0002
##    100        1.2410             nan     0.0010    0.0002
##    120        1.2315             nan     0.0010    0.0002
##    140        1.2223             nan     0.0010    0.0002
##    160        1.2134             nan     0.0010    0.0002
##    180        1.2050             nan     0.0010    0.0002
##    200        1.1971             nan     0.0010    0.0002
##    220        1.1893             nan     0.0010    0.0002
##    240        1.1815             nan     0.0010    0.0002
##    260        1.1738             nan     0.0010    0.0002
##    280        1.1664             nan     0.0010    0.0001
##    300        1.1594             nan     0.0010    0.0001
##    320        1.1523             nan     0.0010    0.0001
##    340        1.1454             nan     0.0010    0.0002
##    360        1.1388             nan     0.0010    0.0001
##    380        1.1323             nan     0.0010    0.0001
##    400        1.1259             nan     0.0010    0.0001
##    420        1.1197             nan     0.0010    0.0001
##    440        1.1136             nan     0.0010    0.0002
##    460        1.1078             nan     0.0010    0.0001
##    480        1.1020             nan     0.0010    0.0001
##    500        1.0963             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2927             nan     0.0010    0.0003
##      2        1.2921             nan     0.0010    0.0002
##      3        1.2916             nan     0.0010    0.0002
##      4        1.2910             nan     0.0010    0.0002
##      5        1.2904             nan     0.0010    0.0003
##      6        1.2898             nan     0.0010    0.0003
##      7        1.2893             nan     0.0010    0.0003
##      8        1.2887             nan     0.0010    0.0002
##      9        1.2881             nan     0.0010    0.0003
##     10        1.2876             nan     0.0010    0.0002
##     20        1.2820             nan     0.0010    0.0002
##     40        1.2712             nan     0.0010    0.0002
##     60        1.2608             nan     0.0010    0.0002
##     80        1.2508             nan     0.0010    0.0002
##    100        1.2412             nan     0.0010    0.0002
##    120        1.2319             nan     0.0010    0.0002
##    140        1.2229             nan     0.0010    0.0002
##    160        1.2140             nan     0.0010    0.0002
##    180        1.2053             nan     0.0010    0.0002
##    200        1.1968             nan     0.0010    0.0002
##    220        1.1887             nan     0.0010    0.0001
##    240        1.1810             nan     0.0010    0.0002
##    260        1.1732             nan     0.0010    0.0001
##    280        1.1659             nan     0.0010    0.0002
##    300        1.1586             nan     0.0010    0.0002
##    320        1.1516             nan     0.0010    0.0001
##    340        1.1448             nan     0.0010    0.0001
##    360        1.1382             nan     0.0010    0.0001
##    380        1.1318             nan     0.0010    0.0001
##    400        1.1256             nan     0.0010    0.0002
##    420        1.1193             nan     0.0010    0.0001
##    440        1.1134             nan     0.0010    0.0001
##    460        1.1075             nan     0.0010    0.0001
##    480        1.1017             nan     0.0010    0.0001
##    500        1.0962             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2574             nan     0.1000    0.0167
##      2        1.2260             nan     0.1000    0.0147
##      3        1.2058             nan     0.1000    0.0086
##      4        1.1798             nan     0.1000    0.0056
##      5        1.1595             nan     0.1000    0.0097
##      6        1.1435             nan     0.1000    0.0062
##      7        1.1291             nan     0.1000    0.0063
##      8        1.1155             nan     0.1000    0.0052
##      9        1.1002             nan     0.1000    0.0063
##     10        1.0883             nan     0.1000    0.0044
##     20        0.9956             nan     0.1000    0.0030
##     40        0.9169             nan     0.1000   -0.0001
##     60        0.8787             nan     0.1000   -0.0009
##     80        0.8483             nan     0.1000   -0.0001
##    100        0.8294             nan     0.1000   -0.0015
##    120        0.8142             nan     0.1000   -0.0020
##    140        0.8017             nan     0.1000   -0.0007
##    160        0.7920             nan     0.1000   -0.0011
##    180        0.7804             nan     0.1000   -0.0005
##    200        0.7695             nan     0.1000   -0.0006
##    220        0.7609             nan     0.1000   -0.0004
##    240        0.7545             nan     0.1000   -0.0011
##    260        0.7467             nan     0.1000   -0.0006
##    280        0.7399             nan     0.1000   -0.0012
##    300        0.7328             nan     0.1000   -0.0011
##    320        0.7265             nan     0.1000   -0.0005
##    340        0.7210             nan     0.1000   -0.0007
##    360        0.7158             nan     0.1000   -0.0003
##    380        0.7096             nan     0.1000   -0.0009
##    400        0.7065             nan     0.1000   -0.0006
##    420        0.7018             nan     0.1000   -0.0010
##    440        0.6969             nan     0.1000   -0.0007
##    460        0.6925             nan     0.1000   -0.0011
##    480        0.6884             nan     0.1000   -0.0008
##    500        0.6817             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2574             nan     0.1000    0.0180
##      2        1.2291             nan     0.1000    0.0137
##      3        1.2016             nan     0.1000    0.0118
##      4        1.1778             nan     0.1000    0.0097
##      5        1.1608             nan     0.1000    0.0075
##      6        1.1417             nan     0.1000    0.0081
##      7        1.1282             nan     0.1000    0.0057
##      8        1.1147             nan     0.1000    0.0049
##      9        1.1023             nan     0.1000    0.0047
##     10        1.0912             nan     0.1000    0.0051
##     20        0.9989             nan     0.1000    0.0013
##     40        0.9131             nan     0.1000    0.0007
##     60        0.8728             nan     0.1000   -0.0012
##     80        0.8468             nan     0.1000   -0.0003
##    100        0.8226             nan     0.1000   -0.0001
##    120        0.8072             nan     0.1000   -0.0005
##    140        0.7946             nan     0.1000   -0.0014
##    160        0.7835             nan     0.1000   -0.0010
##    180        0.7731             nan     0.1000   -0.0006
##    200        0.7658             nan     0.1000   -0.0005
##    220        0.7564             nan     0.1000   -0.0006
##    240        0.7496             nan     0.1000   -0.0015
##    260        0.7443             nan     0.1000   -0.0005
##    280        0.7376             nan     0.1000   -0.0007
##    300        0.7317             nan     0.1000   -0.0008
##    320        0.7284             nan     0.1000   -0.0008
##    340        0.7226             nan     0.1000   -0.0001
##    360        0.7145             nan     0.1000   -0.0005
##    380        0.7098             nan     0.1000   -0.0003
##    400        0.7031             nan     0.1000   -0.0007
##    420        0.6996             nan     0.1000   -0.0009
##    440        0.6950             nan     0.1000   -0.0010
##    460        0.6909             nan     0.1000   -0.0011
##    480        0.6858             nan     0.1000   -0.0006
##    500        0.6803             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2540             nan     0.1000    0.0183
##      2        1.2198             nan     0.1000    0.0135
##      3        1.1933             nan     0.1000    0.0117
##      4        1.1715             nan     0.1000    0.0094
##      5        1.1516             nan     0.1000    0.0078
##      6        1.1340             nan     0.1000    0.0073
##      7        1.1206             nan     0.1000    0.0052
##      8        1.1089             nan     0.1000    0.0048
##      9        1.0932             nan     0.1000    0.0060
##     10        1.0805             nan     0.1000    0.0068
##     20        0.9963             nan     0.1000    0.0027
##     40        0.9111             nan     0.1000   -0.0012
##     60        0.8663             nan     0.1000   -0.0012
##     80        0.8433             nan     0.1000    0.0001
##    100        0.8267             nan     0.1000    0.0001
##    120        0.8104             nan     0.1000   -0.0004
##    140        0.8015             nan     0.1000   -0.0013
##    160        0.7881             nan     0.1000   -0.0014
##    180        0.7796             nan     0.1000   -0.0001
##    200        0.7710             nan     0.1000   -0.0008
##    220        0.7612             nan     0.1000   -0.0007
##    240        0.7540             nan     0.1000   -0.0001
##    260        0.7487             nan     0.1000   -0.0014
##    280        0.7430             nan     0.1000   -0.0019
##    300        0.7354             nan     0.1000   -0.0016
##    320        0.7292             nan     0.1000   -0.0010
##    340        0.7214             nan     0.1000   -0.0014
##    360        0.7152             nan     0.1000   -0.0006
##    380        0.7091             nan     0.1000   -0.0019
##    400        0.7050             nan     0.1000   -0.0010
##    420        0.7001             nan     0.1000   -0.0018
##    440        0.6947             nan     0.1000   -0.0013
##    460        0.6902             nan     0.1000   -0.0009
##    480        0.6864             nan     0.1000   -0.0011
##    500        0.6810             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2496             nan     0.1000    0.0225
##      2        1.2087             nan     0.1000    0.0153
##      3        1.1745             nan     0.1000    0.0161
##      4        1.1475             nan     0.1000    0.0120
##      5        1.1258             nan     0.1000    0.0092
##      6        1.1053             nan     0.1000    0.0090
##      7        1.0880             nan     0.1000    0.0060
##      8        1.0687             nan     0.1000    0.0077
##      9        1.0554             nan     0.1000    0.0046
##     10        1.0405             nan     0.1000    0.0037
##     20        0.9352             nan     0.1000    0.0011
##     40        0.8446             nan     0.1000   -0.0010
##     60        0.7931             nan     0.1000   -0.0020
##     80        0.7573             nan     0.1000   -0.0009
##    100        0.7290             nan     0.1000   -0.0006
##    120        0.7085             nan     0.1000   -0.0010
##    140        0.6831             nan     0.1000   -0.0004
##    160        0.6583             nan     0.1000   -0.0011
##    180        0.6397             nan     0.1000   -0.0019
##    200        0.6190             nan     0.1000   -0.0006
##    220        0.6045             nan     0.1000   -0.0013
##    240        0.5857             nan     0.1000   -0.0013
##    260        0.5720             nan     0.1000   -0.0011
##    280        0.5594             nan     0.1000   -0.0020
##    300        0.5446             nan     0.1000   -0.0025
##    320        0.5318             nan     0.1000   -0.0007
##    340        0.5212             nan     0.1000   -0.0014
##    360        0.5085             nan     0.1000   -0.0016
##    380        0.4963             nan     0.1000   -0.0008
##    400        0.4789             nan     0.1000   -0.0009
##    420        0.4670             nan     0.1000   -0.0008
##    440        0.4544             nan     0.1000   -0.0010
##    460        0.4435             nan     0.1000   -0.0013
##    480        0.4325             nan     0.1000   -0.0018
##    500        0.4219             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2468             nan     0.1000    0.0195
##      2        1.2075             nan     0.1000    0.0199
##      3        1.1683             nan     0.1000    0.0157
##      4        1.1392             nan     0.1000    0.0137
##      5        1.1125             nan     0.1000    0.0117
##      6        1.0917             nan     0.1000    0.0090
##      7        1.0696             nan     0.1000    0.0084
##      8        1.0524             nan     0.1000    0.0065
##      9        1.0340             nan     0.1000    0.0066
##     10        1.0190             nan     0.1000    0.0042
##     20        0.9230             nan     0.1000    0.0026
##     40        0.8410             nan     0.1000    0.0007
##     60        0.7960             nan     0.1000   -0.0004
##     80        0.7569             nan     0.1000   -0.0015
##    100        0.7255             nan     0.1000    0.0000
##    120        0.7006             nan     0.1000   -0.0005
##    140        0.6741             nan     0.1000   -0.0012
##    160        0.6570             nan     0.1000   -0.0012
##    180        0.6388             nan     0.1000   -0.0016
##    200        0.6134             nan     0.1000   -0.0001
##    220        0.5965             nan     0.1000   -0.0003
##    240        0.5784             nan     0.1000   -0.0018
##    260        0.5625             nan     0.1000   -0.0014
##    280        0.5510             nan     0.1000   -0.0017
##    300        0.5358             nan     0.1000   -0.0011
##    320        0.5234             nan     0.1000   -0.0004
##    340        0.5072             nan     0.1000   -0.0011
##    360        0.4936             nan     0.1000   -0.0007
##    380        0.4809             nan     0.1000   -0.0019
##    400        0.4698             nan     0.1000   -0.0011
##    420        0.4606             nan     0.1000   -0.0012
##    440        0.4492             nan     0.1000   -0.0007
##    460        0.4381             nan     0.1000   -0.0011
##    480        0.4265             nan     0.1000   -0.0009
##    500        0.4170             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2371             nan     0.1000    0.0236
##      2        1.1978             nan     0.1000    0.0151
##      3        1.1653             nan     0.1000    0.0164
##      4        1.1424             nan     0.1000    0.0087
##      5        1.1145             nan     0.1000    0.0101
##      6        1.0944             nan     0.1000    0.0068
##      7        1.0747             nan     0.1000    0.0075
##      8        1.0571             nan     0.1000    0.0061
##      9        1.0387             nan     0.1000    0.0063
##     10        1.0251             nan     0.1000    0.0036
##     20        0.9301             nan     0.1000   -0.0004
##     40        0.8430             nan     0.1000   -0.0001
##     60        0.7963             nan     0.1000   -0.0016
##     80        0.7633             nan     0.1000   -0.0015
##    100        0.7376             nan     0.1000   -0.0007
##    120        0.7086             nan     0.1000   -0.0021
##    140        0.6867             nan     0.1000   -0.0014
##    160        0.6602             nan     0.1000   -0.0014
##    180        0.6448             nan     0.1000   -0.0018
##    200        0.6283             nan     0.1000   -0.0012
##    220        0.6082             nan     0.1000   -0.0003
##    240        0.5931             nan     0.1000   -0.0010
##    260        0.5777             nan     0.1000   -0.0012
##    280        0.5611             nan     0.1000   -0.0011
##    300        0.5474             nan     0.1000   -0.0008
##    320        0.5324             nan     0.1000   -0.0013
##    340        0.5208             nan     0.1000   -0.0011
##    360        0.5078             nan     0.1000   -0.0010
##    380        0.4955             nan     0.1000   -0.0005
##    400        0.4825             nan     0.1000   -0.0007
##    420        0.4752             nan     0.1000   -0.0011
##    440        0.4656             nan     0.1000   -0.0018
##    460        0.4545             nan     0.1000   -0.0009
##    480        0.4415             nan     0.1000   -0.0009
##    500        0.4307             nan     0.1000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2377             nan     0.1000    0.0252
##      2        1.1967             nan     0.1000    0.0178
##      3        1.1537             nan     0.1000    0.0180
##      4        1.1194             nan     0.1000    0.0149
##      5        1.0921             nan     0.1000    0.0118
##      6        1.0638             nan     0.1000    0.0099
##      7        1.0387             nan     0.1000    0.0100
##      8        1.0168             nan     0.1000    0.0076
##      9        1.0000             nan     0.1000    0.0054
##     10        0.9816             nan     0.1000    0.0051
##     20        0.8898             nan     0.1000   -0.0003
##     40        0.7955             nan     0.1000   -0.0010
##     60        0.7385             nan     0.1000   -0.0006
##     80        0.6923             nan     0.1000   -0.0020
##    100        0.6488             nan     0.1000   -0.0016
##    120        0.6139             nan     0.1000   -0.0018
##    140        0.5792             nan     0.1000   -0.0024
##    160        0.5496             nan     0.1000   -0.0019
##    180        0.5256             nan     0.1000   -0.0022
##    200        0.5030             nan     0.1000   -0.0013
##    220        0.4825             nan     0.1000   -0.0012
##    240        0.4644             nan     0.1000   -0.0026
##    260        0.4378             nan     0.1000   -0.0007
##    280        0.4198             nan     0.1000   -0.0010
##    300        0.3995             nan     0.1000   -0.0003
##    320        0.3829             nan     0.1000   -0.0013
##    340        0.3698             nan     0.1000   -0.0009
##    360        0.3556             nan     0.1000   -0.0001
##    380        0.3396             nan     0.1000   -0.0013
##    400        0.3258             nan     0.1000   -0.0011
##    420        0.3140             nan     0.1000   -0.0007
##    440        0.3033             nan     0.1000   -0.0004
##    460        0.2908             nan     0.1000   -0.0012
##    480        0.2808             nan     0.1000   -0.0012
##    500        0.2702             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2429             nan     0.1000    0.0247
##      2        1.1981             nan     0.1000    0.0202
##      3        1.1535             nan     0.1000    0.0179
##      4        1.1190             nan     0.1000    0.0112
##      5        1.0890             nan     0.1000    0.0127
##      6        1.0698             nan     0.1000    0.0081
##      7        1.0477             nan     0.1000    0.0089
##      8        1.0291             nan     0.1000    0.0057
##      9        1.0128             nan     0.1000    0.0044
##     10        0.9951             nan     0.1000    0.0069
##     20        0.8904             nan     0.1000    0.0020
##     40        0.7967             nan     0.1000   -0.0003
##     60        0.7392             nan     0.1000   -0.0030
##     80        0.6937             nan     0.1000   -0.0016
##    100        0.6570             nan     0.1000   -0.0018
##    120        0.6231             nan     0.1000   -0.0007
##    140        0.5929             nan     0.1000   -0.0010
##    160        0.5580             nan     0.1000   -0.0009
##    180        0.5300             nan     0.1000   -0.0008
##    200        0.5056             nan     0.1000   -0.0012
##    220        0.4849             nan     0.1000   -0.0011
##    240        0.4640             nan     0.1000   -0.0012
##    260        0.4369             nan     0.1000   -0.0013
##    280        0.4162             nan     0.1000   -0.0005
##    300        0.4012             nan     0.1000   -0.0009
##    320        0.3828             nan     0.1000   -0.0013
##    340        0.3681             nan     0.1000   -0.0003
##    360        0.3532             nan     0.1000   -0.0009
##    380        0.3391             nan     0.1000   -0.0005
##    400        0.3238             nan     0.1000   -0.0006
##    420        0.3103             nan     0.1000   -0.0004
##    440        0.2995             nan     0.1000   -0.0004
##    460        0.2895             nan     0.1000   -0.0007
##    480        0.2804             nan     0.1000   -0.0007
##    500        0.2705             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2425             nan     0.1000    0.0183
##      2        1.1976             nan     0.1000    0.0169
##      3        1.1576             nan     0.1000    0.0157
##      4        1.1216             nan     0.1000    0.0152
##      5        1.0887             nan     0.1000    0.0144
##      6        1.0628             nan     0.1000    0.0098
##      7        1.0410             nan     0.1000    0.0071
##      8        1.0201             nan     0.1000    0.0072
##      9        1.0018             nan     0.1000    0.0069
##     10        0.9851             nan     0.1000    0.0054
##     20        0.8805             nan     0.1000    0.0011
##     40        0.7818             nan     0.1000   -0.0006
##     60        0.7208             nan     0.1000    0.0001
##     80        0.6762             nan     0.1000   -0.0000
##    100        0.6389             nan     0.1000   -0.0009
##    120        0.6040             nan     0.1000   -0.0014
##    140        0.5758             nan     0.1000   -0.0014
##    160        0.5475             nan     0.1000   -0.0007
##    180        0.5223             nan     0.1000   -0.0006
##    200        0.4971             nan     0.1000   -0.0015
##    220        0.4739             nan     0.1000   -0.0004
##    240        0.4549             nan     0.1000   -0.0011
##    260        0.4335             nan     0.1000   -0.0008
##    280        0.4159             nan     0.1000   -0.0009
##    300        0.3979             nan     0.1000   -0.0012
##    320        0.3817             nan     0.1000   -0.0006
##    340        0.3632             nan     0.1000   -0.0015
##    360        0.3477             nan     0.1000   -0.0007
##    380        0.3355             nan     0.1000   -0.0009
##    400        0.3231             nan     0.1000   -0.0013
##    420        0.3087             nan     0.1000   -0.0007
##    440        0.2970             nan     0.1000   -0.0012
##    460        0.2853             nan     0.1000   -0.0017
##    480        0.2759             nan     0.1000   -0.0003
##    500        0.2663             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2342             nan     0.2000    0.0303
##      2        1.1889             nan     0.2000    0.0219
##      3        1.1481             nan     0.2000    0.0166
##      4        1.1128             nan     0.2000    0.0140
##      5        1.0821             nan     0.2000    0.0110
##      6        1.0625             nan     0.2000    0.0088
##      7        1.0380             nan     0.2000    0.0069
##      8        1.0206             nan     0.2000    0.0055
##      9        1.0067             nan     0.2000    0.0059
##     10        0.9948             nan     0.2000    0.0026
##     20        0.9186             nan     0.2000   -0.0007
##     40        0.8544             nan     0.2000    0.0017
##     60        0.8230             nan     0.2000   -0.0052
##     80        0.8051             nan     0.2000   -0.0004
##    100        0.7782             nan     0.2000   -0.0024
##    120        0.7639             nan     0.2000   -0.0020
##    140        0.7498             nan     0.2000   -0.0014
##    160        0.7382             nan     0.2000   -0.0019
##    180        0.7231             nan     0.2000   -0.0019
##    200        0.7091             nan     0.2000   -0.0032
##    220        0.6973             nan     0.2000   -0.0031
##    240        0.6883             nan     0.2000   -0.0014
##    260        0.6805             nan     0.2000   -0.0017
##    280        0.6703             nan     0.2000   -0.0014
##    300        0.6610             nan     0.2000   -0.0016
##    320        0.6545             nan     0.2000   -0.0004
##    340        0.6472             nan     0.2000   -0.0022
##    360        0.6413             nan     0.2000   -0.0014
##    380        0.6353             nan     0.2000   -0.0020
##    400        0.6278             nan     0.2000   -0.0022
##    420        0.6208             nan     0.2000   -0.0004
##    440        0.6164             nan     0.2000   -0.0024
##    460        0.6121             nan     0.2000   -0.0019
##    480        0.6051             nan     0.2000   -0.0012
##    500        0.6016             nan     0.2000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2271             nan     0.2000    0.0330
##      2        1.1735             nan     0.2000    0.0232
##      3        1.1437             nan     0.2000    0.0142
##      4        1.1133             nan     0.2000    0.0122
##      5        1.0862             nan     0.2000    0.0108
##      6        1.0645             nan     0.2000    0.0084
##      7        1.0440             nan     0.2000    0.0082
##      8        1.0217             nan     0.2000    0.0065
##      9        1.0065             nan     0.2000    0.0041
##     10        0.9956             nan     0.2000   -0.0001
##     20        0.9141             nan     0.2000   -0.0001
##     40        0.8418             nan     0.2000   -0.0011
##     60        0.8051             nan     0.2000   -0.0014
##     80        0.7852             nan     0.2000   -0.0025
##    100        0.7699             nan     0.2000   -0.0019
##    120        0.7558             nan     0.2000   -0.0025
##    140        0.7436             nan     0.2000   -0.0013
##    160        0.7316             nan     0.2000   -0.0019
##    180        0.7161             nan     0.2000   -0.0019
##    200        0.7084             nan     0.2000   -0.0022
##    220        0.6951             nan     0.2000   -0.0018
##    240        0.6834             nan     0.2000   -0.0002
##    260        0.6742             nan     0.2000   -0.0010
##    280        0.6651             nan     0.2000   -0.0014
##    300        0.6596             nan     0.2000   -0.0021
##    320        0.6524             nan     0.2000   -0.0012
##    340        0.6422             nan     0.2000   -0.0002
##    360        0.6365             nan     0.2000   -0.0014
##    380        0.6299             nan     0.2000   -0.0010
##    400        0.6235             nan     0.2000   -0.0013
##    420        0.6214             nan     0.2000   -0.0010
##    440        0.6141             nan     0.2000   -0.0038
##    460        0.6071             nan     0.2000   -0.0029
##    480        0.6029             nan     0.2000   -0.0018
##    500        0.5959             nan     0.2000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2261             nan     0.2000    0.0313
##      2        1.1728             nan     0.2000    0.0236
##      3        1.1374             nan     0.2000    0.0171
##      4        1.1105             nan     0.2000    0.0144
##      5        1.0864             nan     0.2000    0.0088
##      6        1.0657             nan     0.2000    0.0076
##      7        1.0461             nan     0.2000    0.0060
##      8        1.0275             nan     0.2000    0.0053
##      9        1.0172             nan     0.2000    0.0015
##     10        1.0026             nan     0.2000    0.0066
##     20        0.9243             nan     0.2000    0.0017
##     40        0.8515             nan     0.2000   -0.0025
##     60        0.8163             nan     0.2000   -0.0015
##     80        0.7917             nan     0.2000   -0.0006
##    100        0.7732             nan     0.2000   -0.0012
##    120        0.7565             nan     0.2000   -0.0015
##    140        0.7444             nan     0.2000   -0.0016
##    160        0.7334             nan     0.2000   -0.0025
##    180        0.7244             nan     0.2000   -0.0020
##    200        0.7077             nan     0.2000   -0.0010
##    220        0.6962             nan     0.2000   -0.0014
##    240        0.6873             nan     0.2000   -0.0014
##    260        0.6765             nan     0.2000   -0.0016
##    280        0.6668             nan     0.2000   -0.0039
##    300        0.6595             nan     0.2000   -0.0016
##    320        0.6516             nan     0.2000   -0.0027
##    340        0.6450             nan     0.2000   -0.0014
##    360        0.6358             nan     0.2000    0.0002
##    380        0.6310             nan     0.2000   -0.0012
##    400        0.6228             nan     0.2000   -0.0010
##    420        0.6165             nan     0.2000   -0.0014
##    440        0.6113             nan     0.2000   -0.0004
##    460        0.6050             nan     0.2000   -0.0017
##    480        0.5968             nan     0.2000   -0.0018
##    500        0.5946             nan     0.2000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2056             nan     0.2000    0.0404
##      2        1.1417             nan     0.2000    0.0279
##      3        1.0956             nan     0.2000    0.0192
##      4        1.0511             nan     0.2000    0.0173
##      5        1.0207             nan     0.2000    0.0126
##      6        0.9988             nan     0.2000    0.0067
##      7        0.9794             nan     0.2000    0.0045
##      8        0.9593             nan     0.2000    0.0045
##      9        0.9440             nan     0.2000    0.0017
##     10        0.9277             nan     0.2000    0.0044
##     20        0.8449             nan     0.2000   -0.0001
##     40        0.7563             nan     0.2000   -0.0018
##     60        0.7028             nan     0.2000   -0.0013
##     80        0.6657             nan     0.2000   -0.0020
##    100        0.6219             nan     0.2000   -0.0034
##    120        0.5809             nan     0.2000   -0.0010
##    140        0.5547             nan     0.2000   -0.0035
##    160        0.5246             nan     0.2000   -0.0035
##    180        0.4958             nan     0.2000   -0.0008
##    200        0.4695             nan     0.2000   -0.0008
##    220        0.4470             nan     0.2000   -0.0022
##    240        0.4396             nan     0.2000   -0.0003
##    260        0.4190             nan     0.2000   -0.0040
##    280        0.3994             nan     0.2000   -0.0004
##    300        0.3845             nan     0.2000   -0.0014
##    320        0.3697             nan     0.2000   -0.0030
##    340        0.3532             nan     0.2000   -0.0007
##    360        0.3395             nan     0.2000   -0.0015
##    380        0.3254             nan     0.2000   -0.0007
##    400        0.3147             nan     0.2000   -0.0017
##    420        0.3041             nan     0.2000   -0.0008
##    440        0.2912             nan     0.2000   -0.0006
##    460        0.2791             nan     0.2000   -0.0023
##    480        0.2680             nan     0.2000   -0.0017
##    500        0.2581             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2022             nan     0.2000    0.0404
##      2        1.1346             nan     0.2000    0.0298
##      3        1.0872             nan     0.2000    0.0206
##      4        1.0559             nan     0.2000    0.0080
##      5        1.0254             nan     0.2000    0.0136
##      6        0.9968             nan     0.2000    0.0084
##      7        0.9744             nan     0.2000    0.0053
##      8        0.9612             nan     0.2000    0.0037
##      9        0.9440             nan     0.2000    0.0036
##     10        0.9294             nan     0.2000    0.0036
##     20        0.8431             nan     0.2000   -0.0005
##     40        0.7631             nan     0.2000   -0.0025
##     60        0.7175             nan     0.2000   -0.0008
##     80        0.6628             nan     0.2000   -0.0031
##    100        0.6316             nan     0.2000   -0.0032
##    120        0.5892             nan     0.2000   -0.0014
##    140        0.5538             nan     0.2000   -0.0025
##    160        0.5331             nan     0.2000   -0.0036
##    180        0.5117             nan     0.2000   -0.0014
##    200        0.4872             nan     0.2000   -0.0017
##    220        0.4668             nan     0.2000   -0.0024
##    240        0.4487             nan     0.2000   -0.0020
##    260        0.4313             nan     0.2000   -0.0007
##    280        0.4079             nan     0.2000   -0.0006
##    300        0.3984             nan     0.2000   -0.0016
##    320        0.3729             nan     0.2000   -0.0000
##    340        0.3566             nan     0.2000   -0.0002
##    360        0.3451             nan     0.2000   -0.0008
##    380        0.3289             nan     0.2000   -0.0011
##    400        0.3119             nan     0.2000   -0.0018
##    420        0.2965             nan     0.2000   -0.0018
##    440        0.2840             nan     0.2000   -0.0008
##    460        0.2688             nan     0.2000   -0.0017
##    480        0.2579             nan     0.2000   -0.0016
##    500        0.2468             nan     0.2000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2021             nan     0.2000    0.0368
##      2        1.1449             nan     0.2000    0.0259
##      3        1.0885             nan     0.2000    0.0296
##      4        1.0574             nan     0.2000    0.0123
##      5        1.0240             nan     0.2000    0.0121
##      6        0.9980             nan     0.2000    0.0106
##      7        0.9817             nan     0.2000    0.0036
##      8        0.9629             nan     0.2000    0.0072
##      9        0.9488             nan     0.2000   -0.0005
##     10        0.9322             nan     0.2000    0.0060
##     20        0.8384             nan     0.2000   -0.0008
##     40        0.7546             nan     0.2000   -0.0010
##     60        0.7046             nan     0.2000   -0.0006
##     80        0.6562             nan     0.2000   -0.0020
##    100        0.6215             nan     0.2000   -0.0020
##    120        0.5862             nan     0.2000   -0.0023
##    140        0.5502             nan     0.2000   -0.0013
##    160        0.5213             nan     0.2000   -0.0058
##    180        0.4955             nan     0.2000   -0.0036
##    200        0.4715             nan     0.2000   -0.0019
##    220        0.4548             nan     0.2000   -0.0018
##    240        0.4336             nan     0.2000   -0.0017
##    260        0.4145             nan     0.2000   -0.0015
##    280        0.3979             nan     0.2000   -0.0024
##    300        0.3801             nan     0.2000   -0.0015
##    320        0.3627             nan     0.2000   -0.0018
##    340        0.3474             nan     0.2000   -0.0023
##    360        0.3317             nan     0.2000   -0.0006
##    380        0.3184             nan     0.2000   -0.0012
##    400        0.3067             nan     0.2000   -0.0011
##    420        0.2920             nan     0.2000   -0.0005
##    440        0.2826             nan     0.2000   -0.0010
##    460        0.2732             nan     0.2000   -0.0015
##    480        0.2629             nan     0.2000   -0.0013
##    500        0.2538             nan     0.2000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2047             nan     0.2000    0.0381
##      2        1.1292             nan     0.2000    0.0325
##      3        1.0778             nan     0.2000    0.0218
##      4        1.0293             nan     0.2000    0.0195
##      5        0.9941             nan     0.2000    0.0144
##      6        0.9695             nan     0.2000    0.0065
##      7        0.9414             nan     0.2000    0.0088
##      8        0.9254             nan     0.2000   -0.0021
##      9        0.9099             nan     0.2000    0.0028
##     10        0.8945             nan     0.2000    0.0036
##     20        0.8065             nan     0.2000   -0.0020
##     40        0.7178             nan     0.2000   -0.0025
##     60        0.6430             nan     0.2000   -0.0016
##     80        0.5879             nan     0.2000   -0.0016
##    100        0.5324             nan     0.2000   -0.0033
##    120        0.4856             nan     0.2000   -0.0018
##    140        0.4368             nan     0.2000   -0.0027
##    160        0.3976             nan     0.2000   -0.0018
##    180        0.3682             nan     0.2000   -0.0026
##    200        0.3419             nan     0.2000   -0.0015
##    220        0.3183             nan     0.2000   -0.0019
##    240        0.2997             nan     0.2000   -0.0031
##    260        0.2783             nan     0.2000   -0.0008
##    280        0.2591             nan     0.2000   -0.0021
##    300        0.2409             nan     0.2000   -0.0007
##    320        0.2232             nan     0.2000   -0.0004
##    340        0.2077             nan     0.2000   -0.0010
##    360        0.1953             nan     0.2000   -0.0010
##    380        0.1821             nan     0.2000   -0.0010
##    400        0.1717             nan     0.2000   -0.0006
##    420        0.1639             nan     0.2000   -0.0005
##    440        0.1563             nan     0.2000   -0.0015
##    460        0.1459             nan     0.2000   -0.0015
##    480        0.1370             nan     0.2000   -0.0008
##    500        0.1304             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1957             nan     0.2000    0.0434
##      2        1.1248             nan     0.2000    0.0273
##      3        1.0665             nan     0.2000    0.0230
##      4        1.0327             nan     0.2000    0.0116
##      5        0.9965             nan     0.2000    0.0121
##      6        0.9613             nan     0.2000    0.0105
##      7        0.9402             nan     0.2000    0.0059
##      8        0.9205             nan     0.2000    0.0037
##      9        0.9065             nan     0.2000    0.0050
##     10        0.8886             nan     0.2000    0.0045
##     20        0.7871             nan     0.2000   -0.0013
##     40        0.6882             nan     0.2000   -0.0017
##     60        0.6150             nan     0.2000   -0.0024
##     80        0.5622             nan     0.2000   -0.0047
##    100        0.5128             nan     0.2000   -0.0025
##    120        0.4763             nan     0.2000   -0.0040
##    140        0.4354             nan     0.2000   -0.0026
##    160        0.3960             nan     0.2000   -0.0033
##    180        0.3649             nan     0.2000   -0.0009
##    200        0.3358             nan     0.2000   -0.0020
##    220        0.3102             nan     0.2000   -0.0008
##    240        0.2868             nan     0.2000   -0.0008
##    260        0.2679             nan     0.2000   -0.0010
##    280        0.2439             nan     0.2000   -0.0011
##    300        0.2281             nan     0.2000   -0.0009
##    320        0.2095             nan     0.2000   -0.0011
##    340        0.1944             nan     0.2000   -0.0005
##    360        0.1838             nan     0.2000   -0.0007
##    380        0.1711             nan     0.2000   -0.0010
##    400        0.1598             nan     0.2000   -0.0004
##    420        0.1521             nan     0.2000   -0.0011
##    440        0.1416             nan     0.2000   -0.0002
##    460        0.1307             nan     0.2000   -0.0004
##    480        0.1225             nan     0.2000   -0.0009
##    500        0.1157             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1999             nan     0.2000    0.0432
##      2        1.1306             nan     0.2000    0.0270
##      3        1.0734             nan     0.2000    0.0288
##      4        1.0361             nan     0.2000    0.0112
##      5        0.9940             nan     0.2000    0.0163
##      6        0.9676             nan     0.2000    0.0078
##      7        0.9445             nan     0.2000    0.0052
##      8        0.9302             nan     0.2000    0.0018
##      9        0.9166             nan     0.2000    0.0004
##     10        0.9022             nan     0.2000    0.0021
##     20        0.8005             nan     0.2000    0.0002
##     40        0.7011             nan     0.2000   -0.0015
##     60        0.6126             nan     0.2000   -0.0030
##     80        0.5597             nan     0.2000   -0.0002
##    100        0.5131             nan     0.2000   -0.0029
##    120        0.4695             nan     0.2000   -0.0014
##    140        0.4275             nan     0.2000   -0.0009
##    160        0.3955             nan     0.2000   -0.0013
##    180        0.3625             nan     0.2000   -0.0010
##    200        0.3331             nan     0.2000   -0.0011
##    220        0.3106             nan     0.2000   -0.0009
##    240        0.2890             nan     0.2000   -0.0025
##    260        0.2694             nan     0.2000   -0.0020
##    280        0.2457             nan     0.2000   -0.0015
##    300        0.2278             nan     0.2000   -0.0012
##    320        0.2132             nan     0.2000   -0.0004
##    340        0.2014             nan     0.2000   -0.0013
##    360        0.1876             nan     0.2000   -0.0015
##    380        0.1761             nan     0.2000   -0.0012
##    400        0.1650             nan     0.2000   -0.0015
##    420        0.1558             nan     0.2000   -0.0006
##    440        0.1439             nan     0.2000   -0.0005
##    460        0.1346             nan     0.2000   -0.0001
##    480        0.1273             nan     0.2000   -0.0006
##    500        0.1199             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1982             nan     0.3000    0.0458
##      2        1.1458             nan     0.3000    0.0116
##      3        1.0941             nan     0.3000    0.0145
##      4        1.0474             nan     0.3000    0.0212
##      5        1.0176             nan     0.3000    0.0115
##      6        1.0033             nan     0.3000   -0.0005
##      7        0.9832             nan     0.3000    0.0043
##      8        0.9615             nan     0.3000    0.0098
##      9        0.9496             nan     0.3000    0.0036
##     10        0.9404             nan     0.3000    0.0006
##     20        0.8710             nan     0.3000   -0.0001
##     40        0.8067             nan     0.3000   -0.0055
##     60        0.7754             nan     0.3000   -0.0043
##     80        0.7548             nan     0.3000   -0.0036
##    100        0.7351             nan     0.3000   -0.0002
##    120        0.7133             nan     0.3000   -0.0024
##    140        0.6981             nan     0.3000   -0.0030
##    160        0.6813             nan     0.3000   -0.0010
##    180        0.6657             nan     0.3000   -0.0012
##    200        0.6565             nan     0.3000   -0.0020
##    220        0.6458             nan     0.3000   -0.0030
##    240        0.6380             nan     0.3000   -0.0022
##    260        0.6289             nan     0.3000   -0.0042
##    280        0.6168             nan     0.3000   -0.0026
##    300        0.6069             nan     0.3000   -0.0036
##    320        0.6038             nan     0.3000   -0.0034
##    340        0.5905             nan     0.3000   -0.0012
##    360        0.5867             nan     0.3000   -0.0030
##    380        0.5802             nan     0.3000   -0.0014
##    400        0.5748             nan     0.3000   -0.0014
##    420        0.5609             nan     0.3000   -0.0020
##    440        0.5561             nan     0.3000   -0.0053
##    460        0.5462             nan     0.3000   -0.0016
##    480        0.5387             nan     0.3000   -0.0017
##    500        0.5319             nan     0.3000   -0.0041
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1884             nan     0.3000    0.0396
##      2        1.1432             nan     0.3000    0.0172
##      3        1.0945             nan     0.3000    0.0211
##      4        1.0619             nan     0.3000    0.0106
##      5        1.0201             nan     0.3000    0.0139
##      6        0.9974             nan     0.3000    0.0048
##      7        0.9821             nan     0.3000    0.0040
##      8        0.9623             nan     0.3000    0.0041
##      9        0.9493             nan     0.3000    0.0021
##     10        0.9415             nan     0.3000    0.0012
##     20        0.8771             nan     0.3000   -0.0015
##     40        0.8162             nan     0.3000   -0.0032
##     60        0.7802             nan     0.3000   -0.0017
##     80        0.7540             nan     0.3000   -0.0036
##    100        0.7332             nan     0.3000   -0.0046
##    120        0.7106             nan     0.3000   -0.0030
##    140        0.6992             nan     0.3000   -0.0013
##    160        0.6896             nan     0.3000   -0.0020
##    180        0.6800             nan     0.3000   -0.0016
##    200        0.6709             nan     0.3000   -0.0018
##    220        0.6613             nan     0.3000   -0.0023
##    240        0.6408             nan     0.3000   -0.0026
##    260        0.6320             nan     0.3000   -0.0048
##    280        0.6196             nan     0.3000   -0.0026
##    300        0.6131             nan     0.3000   -0.0034
##    320        0.6063             nan     0.3000   -0.0001
##    340        0.5936             nan     0.3000   -0.0031
##    360        0.5892             nan     0.3000   -0.0016
##    380        0.5831             nan     0.3000   -0.0026
##    400        0.5774             nan     0.3000   -0.0023
##    420        0.5710             nan     0.3000   -0.0030
##    440        0.5607             nan     0.3000   -0.0011
##    460        0.5569             nan     0.3000   -0.0023
##    480        0.5491             nan     0.3000   -0.0005
##    500        0.5421             nan     0.3000   -0.0047
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1889             nan     0.3000    0.0488
##      2        1.1321             nan     0.3000    0.0244
##      3        1.0939             nan     0.3000    0.0182
##      4        1.0641             nan     0.3000    0.0086
##      5        1.0357             nan     0.3000    0.0087
##      6        1.0118             nan     0.3000    0.0086
##      7        0.9935             nan     0.3000    0.0042
##      8        0.9795             nan     0.3000    0.0024
##      9        0.9652             nan     0.3000    0.0030
##     10        0.9506             nan     0.3000    0.0060
##     20        0.8808             nan     0.3000   -0.0032
##     40        0.8246             nan     0.3000   -0.0037
##     60        0.7949             nan     0.3000   -0.0010
##     80        0.7746             nan     0.3000   -0.0051
##    100        0.7524             nan     0.3000   -0.0026
##    120        0.7307             nan     0.3000   -0.0013
##    140        0.7142             nan     0.3000   -0.0022
##    160        0.6994             nan     0.3000    0.0005
##    180        0.6905             nan     0.3000   -0.0010
##    200        0.6817             nan     0.3000   -0.0065
##    220        0.6714             nan     0.3000   -0.0043
##    240        0.6563             nan     0.3000   -0.0022
##    260        0.6454             nan     0.3000   -0.0017
##    280        0.6343             nan     0.3000   -0.0030
##    300        0.6243             nan     0.3000   -0.0007
##    320        0.6135             nan     0.3000   -0.0016
##    340        0.6086             nan     0.3000   -0.0040
##    360        0.5977             nan     0.3000   -0.0035
##    380        0.5906             nan     0.3000   -0.0026
##    400        0.5806             nan     0.3000   -0.0017
##    420        0.5734             nan     0.3000   -0.0023
##    440        0.5679             nan     0.3000   -0.0012
##    460        0.5667             nan     0.3000   -0.0037
##    480        0.5574             nan     0.3000   -0.0033
##    500        0.5470             nan     0.3000   -0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1784             nan     0.3000    0.0557
##      2        1.0963             nan     0.3000    0.0303
##      3        1.0403             nan     0.3000    0.0204
##      4        0.9965             nan     0.3000    0.0188
##      5        0.9642             nan     0.3000    0.0129
##      6        0.9427             nan     0.3000    0.0032
##      7        0.9205             nan     0.3000    0.0078
##      8        0.8963             nan     0.3000    0.0024
##      9        0.8824             nan     0.3000    0.0016
##     10        0.8686             nan     0.3000    0.0025
##     20        0.8057             nan     0.3000   -0.0061
##     40        0.7046             nan     0.3000   -0.0009
##     60        0.6902             nan     0.3000   -0.0020
##     80        0.6386             nan     0.3000   -0.0049
##    100        0.5979             nan     0.3000   -0.0054
##    120        0.5333             nan     0.3000   -0.0014
##    140        0.4783             nan     0.3000   -0.0029
##    160        0.4419             nan     0.3000   -0.0024
##    180        0.4082             nan     0.3000   -0.0021
##    200        0.3799             nan     0.3000   -0.0028
##    220           inf             nan     0.3000       nan
##    240           inf             nan     0.3000   -0.0025
##    260           inf             nan     0.3000   -0.0024
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000   -0.0017
##    320           inf             nan     0.3000   -0.0030
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000   -0.0010
##    400           inf             nan     0.3000   -0.0005
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000   -0.0007
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1756             nan     0.3000    0.0559
##      2        1.1069             nan     0.3000    0.0220
##      3        1.0565             nan     0.3000    0.0212
##      4        1.0208             nan     0.3000    0.0073
##      5        0.9951             nan     0.3000   -0.0007
##      6        0.9573             nan     0.3000    0.0090
##      7        0.9286             nan     0.3000    0.0102
##      8        0.9087             nan     0.3000    0.0042
##      9        0.8837             nan     0.3000    0.0057
##     10        0.8747             nan     0.3000   -0.0013
##     20        0.8045             nan     0.3000   -0.0015
##     40        0.7238             nan     0.3000   -0.0037
##     60        0.6554             nan     0.3000   -0.0025
##     80        0.6072             nan     0.3000   -0.0031
##    100        0.5695             nan     0.3000   -0.0053
##    120        0.5382             nan     0.3000   -0.0061
##    140        0.4964             nan     0.3000   -0.0012
##    160        0.4623             nan     0.3000   -0.0023
##    180        0.4336             nan     0.3000   -0.0037
##    200        0.4072             nan     0.3000   -0.0025
##    220        0.3772             nan     0.3000   -0.0019
##    240        0.3746             nan     0.3000   -0.0016
##    260        0.3436             nan     0.3000   -0.0041
##    280        0.3168             nan     0.3000   -0.0037
##    300        0.2971             nan     0.3000   -0.0013
##    320        0.2657             nan     0.3000   -0.0032
##    340        0.2466             nan     0.3000   -0.0008
##    360        0.2370             nan     0.3000   -0.0020
##    380        0.2180             nan     0.3000   -0.0008
##    400        0.2085             nan     0.3000   -0.0012
##    420        0.1968             nan     0.3000   -0.0016
##    440        0.1826             nan     0.3000   -0.0018
##    460        0.1712             nan     0.3000   -0.0015
##    480        0.1627             nan     0.3000   -0.0012
##    500        0.1543             nan     0.3000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1667             nan     0.3000    0.0561
##      2        1.0889             nan     0.3000    0.0379
##      3        1.0377             nan     0.3000    0.0190
##      4        1.0095             nan     0.3000    0.0058
##      5        0.9731             nan     0.3000    0.0144
##      6        0.9468             nan     0.3000    0.0068
##      7        0.9225             nan     0.3000    0.0038
##      8        0.9140             nan     0.3000   -0.0019
##      9        0.8980             nan     0.3000    0.0044
##     10        0.8837             nan     0.3000    0.0019
##     20        0.8092             nan     0.3000   -0.0047
##     40        0.6956             nan     0.3000   -0.0047
##     60        0.6539             nan     0.3000   -0.0039
##     80        0.6066             nan     0.3000   -0.0107
##    100        0.5662             nan     0.3000   -0.0029
##    120        0.5254             nan     0.3000   -0.0077
##    140        0.4948             nan     0.3000   -0.0029
##    160        0.4644             nan     0.3000   -0.0019
##    180        0.4214             nan     0.3000   -0.0049
##    200        0.3946             nan     0.3000   -0.0009
##    220        0.3699             nan     0.3000   -0.0021
##    240        0.3512             nan     0.3000   -0.0039
##    260        0.3255             nan     0.3000   -0.0026
##    280        0.3031             nan     0.3000   -0.0022
##    300        0.2898             nan     0.3000   -0.0008
##    320        0.2714             nan     0.3000   -0.0028
##    340        0.2571             nan     0.3000   -0.0017
##    360        0.2432             nan     0.3000   -0.0040
##    380        0.2281             nan     0.3000   -0.0003
##    400        0.2163             nan     0.3000   -0.0018
##    420        0.2038             nan     0.3000   -0.0018
##    440        0.1934             nan     0.3000   -0.0018
##    460        0.1814             nan     0.3000   -0.0022
##    480        0.1729             nan     0.3000   -0.0006
##    500        0.1654             nan     0.3000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1390             nan     0.3000    0.0671
##      2        1.0581             nan     0.3000    0.0339
##      3        0.9992             nan     0.3000    0.0207
##      4        0.9632             nan     0.3000    0.0085
##      5        0.9283             nan     0.3000    0.0112
##      6        0.9136             nan     0.3000   -0.0066
##      7        0.8982             nan     0.3000   -0.0037
##      8        0.8787             nan     0.3000    0.0019
##      9        0.8664             nan     0.3000   -0.0013
##     10        0.8567             nan     0.3000   -0.0042
##     20        0.7494             nan     0.3000   -0.0096
##     40        0.6322             nan     0.3000   -0.0076
##     60        0.5301             nan     0.3000   -0.0059
##     80        0.4559             nan     0.3000    0.0002
##    100        0.4105             nan     0.3000   -0.0027
##    120        0.3556             nan     0.3000   -0.0019
##    140        0.3095             nan     0.3000   -0.0021
##    160        0.2822             nan     0.3000   -0.0020
##    180        0.2538             nan     0.3000   -0.0020
##    200        0.2305             nan     0.3000   -0.0022
##    220        0.2125             nan     0.3000   -0.0019
##    240        0.1910             nan     0.3000   -0.0029
##    260        0.1697             nan     0.3000   -0.0018
##    280        0.1526             nan     0.3000   -0.0016
##    300        0.1385             nan     0.3000   -0.0016
##    320        0.1258             nan     0.3000   -0.0013
##    340        0.1168             nan     0.3000   -0.0005
##    360        0.1049             nan     0.3000   -0.0008
##    380        0.0968             nan     0.3000   -0.0008
##    400        0.0891             nan     0.3000   -0.0011
##    420        0.0842             nan     0.3000   -0.0003
##    440        0.0772             nan     0.3000   -0.0006
##    460        0.0706             nan     0.3000   -0.0008
##    480        0.0657             nan     0.3000   -0.0013
##    500        0.0600             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1572             nan     0.3000    0.0450
##      2        1.0806             nan     0.3000    0.0327
##      3        1.0234             nan     0.3000    0.0217
##      4        0.9743             nan     0.3000    0.0169
##      5        0.9401             nan     0.3000    0.0098
##      6        0.9066             nan     0.3000    0.0088
##      7        0.8876             nan     0.3000    0.0031
##      8        0.8718             nan     0.3000    0.0000
##      9        0.8566             nan     0.3000   -0.0004
##     10        0.8405             nan     0.3000    0.0017
##     20        0.7421             nan     0.3000   -0.0073
##     40        0.6234             nan     0.3000   -0.0012
##     60        0.5400             nan     0.3000   -0.0032
##     80        0.4757             nan     0.3000   -0.0067
##    100        0.4184             nan     0.3000   -0.0051
##    120        0.3823             nan     0.3000   -0.0043
##    140        0.3308             nan     0.3000   -0.0009
##    160        0.3009             nan     0.3000   -0.0008
##    180        0.2699             nan     0.3000   -0.0048
##    200        0.2345             nan     0.3000   -0.0034
##    220        0.2127             nan     0.3000   -0.0017
##    240        0.1963             nan     0.3000   -0.0011
##    260        0.1785             nan     0.3000   -0.0016
##    280        0.1636             nan     0.3000   -0.0018
##    300        0.1500             nan     0.3000   -0.0012
##    320        0.1371             nan     0.3000   -0.0006
##    340        0.1250             nan     0.3000   -0.0010
##    360        0.1125             nan     0.3000   -0.0013
##    380        0.1036             nan     0.3000   -0.0005
##    400        0.0944             nan     0.3000   -0.0005
##    420        0.0867             nan     0.3000   -0.0008
##    440        0.0798             nan     0.3000   -0.0000
##    460        0.0738             nan     0.3000   -0.0005
##    480        0.0679             nan     0.3000   -0.0006
##    500        0.0627             nan     0.3000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1388             nan     0.3000    0.0552
##      2        1.0591             nan     0.3000    0.0223
##      3        1.0066             nan     0.3000    0.0200
##      4        0.9669             nan     0.3000    0.0149
##      5        0.9235             nan     0.3000    0.0156
##      6        0.8977             nan     0.3000    0.0087
##      7        0.8860             nan     0.3000   -0.0017
##      8        0.8639             nan     0.3000    0.0044
##      9        0.8461             nan     0.3000    0.0023
##     10        0.8290             nan     0.3000   -0.0010
##     20        0.7489             nan     0.3000   -0.0070
##     40        0.6295             nan     0.3000   -0.0066
##     60        0.5440             nan     0.3000   -0.0003
##     80        0.4838             nan     0.3000   -0.0044
##    100        0.4321             nan     0.3000   -0.0054
##    120        0.3886             nan     0.3000   -0.0017
##    140        0.3312             nan     0.3000   -0.0023
##    160        0.2861             nan     0.3000    0.0003
##    180        0.2561             nan     0.3000   -0.0014
##    200        0.2268             nan     0.3000   -0.0011
##    220        0.2024             nan     0.3000   -0.0011
##    240        0.1852             nan     0.3000   -0.0005
##    260        0.1687             nan     0.3000   -0.0009
##    280        0.1550             nan     0.3000   -0.0012
##    300        0.1409             nan     0.3000   -0.0017
##    320        0.1258             nan     0.3000   -0.0016
##    340        0.1141             nan     0.3000   -0.0011
##    360        0.1021             nan     0.3000   -0.0003
##    380        0.0940             nan     0.3000   -0.0008
##    400        0.0875             nan     0.3000   -0.0003
##    420        0.0787             nan     0.3000   -0.0009
##    440        0.0732             nan     0.3000   -0.0011
##    460        0.0673             nan     0.3000   -0.0004
##    480        0.0614             nan     0.3000   -0.0005
##    500        0.0563             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1469             nan     0.5000    0.0618
##      2        1.0905             nan     0.5000    0.0171
##      3        1.0374             nan     0.5000    0.0197
##      4        1.0133             nan     0.5000    0.0012
##      5        0.9682             nan     0.5000    0.0174
##      6        0.9586             nan     0.5000    0.0013
##      7        0.9480             nan     0.5000   -0.0025
##      8        0.9354             nan     0.5000   -0.0004
##      9        0.9139             nan     0.5000    0.0082
##     10        0.9090             nan     0.5000   -0.0019
##     20        0.8518             nan     0.5000   -0.0004
##     40        0.8049             nan     0.5000   -0.0071
##     60        0.7642             nan     0.5000   -0.0040
##     80        0.7356             nan     0.5000   -0.0046
##    100        0.7041             nan     0.5000    0.0001
##    120        0.6807             nan     0.5000   -0.0074
##    140        0.6604             nan     0.5000   -0.0022
##    160        0.6447             nan     0.5000   -0.0028
##    180        0.6284             nan     0.5000   -0.0063
##    200        0.6120             nan     0.5000   -0.0088
##    220        0.6007             nan     0.5000   -0.0041
##    240        0.5850             nan     0.5000    0.0008
##    260        0.5770             nan     0.5000   -0.0054
##    280        0.5656             nan     0.5000    0.0001
##    300        0.5444             nan     0.5000   -0.0009
##    320        0.5319             nan     0.5000   -0.0018
##    340        0.5234             nan     0.5000   -0.0070
##    360        0.5119             nan     0.5000   -0.0053
##    380        0.4957             nan     0.5000   -0.0015
##    400        0.4916             nan     0.5000   -0.0044
##    420        0.4937             nan     0.5000   -0.0042
##    440        0.4819             nan     0.5000   -0.0043
##    460        0.4780             nan     0.5000   -0.0055
##    480        0.4676             nan     0.5000   -0.0031
##    500        0.4678             nan     0.5000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1510             nan     0.5000    0.0669
##      2        1.0830             nan     0.5000    0.0232
##      3        1.0355             nan     0.5000    0.0226
##      4        0.9862             nan     0.5000    0.0198
##      5        0.9712             nan     0.5000    0.0001
##      6        0.9488             nan     0.5000   -0.0003
##      7        0.9224             nan     0.5000    0.0079
##      8        0.9154             nan     0.5000   -0.0004
##      9        0.9009             nan     0.5000    0.0007
##     10        0.8933             nan     0.5000    0.0011
##     20        0.8263             nan     0.5000    0.0018
##     40        0.7903             nan     0.5000   -0.0049
##     60        0.7438             nan     0.5000   -0.0022
##     80        0.7296             nan     0.5000   -0.0045
##    100        0.6921             nan     0.5000   -0.0035
##    120        0.6721             nan     0.5000   -0.0045
##    140        1.1541             nan     0.5000   -0.0104
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1691             nan     0.5000    0.0576
##      2        1.0753             nan     0.5000    0.0309
##      3        1.0469             nan     0.5000    0.0011
##      4        0.9883             nan     0.5000    0.0251
##      5        0.9632             nan     0.5000    0.0104
##      6        0.9374             nan     0.5000    0.0081
##      7        0.9214             nan     0.5000    0.0020
##      8        0.9183             nan     0.5000   -0.0126
##      9        0.9071             nan     0.5000   -0.0078
##     10        0.9018             nan     0.5000   -0.0059
##     20        0.8355             nan     0.5000   -0.0030
##     40        0.7916             nan     0.5000   -0.0110
##     60        0.7508             nan     0.5000   -0.0035
##     80        0.7250             nan     0.5000   -0.0026
##    100        0.6974             nan     0.5000    0.0017
##    120        0.6694             nan     0.5000   -0.0017
##    140        0.6576             nan     0.5000   -0.0039
##    160        0.6463             nan     0.5000   -0.0018
##    180        0.6338             nan     0.5000   -0.0063
##    200        0.6255             nan     0.5000   -0.0056
##    220        0.6073             nan     0.5000   -0.0056
##    240        0.5984             nan     0.5000   -0.0095
##    260        0.5846             nan     0.5000   -0.0097
##    280        0.5633             nan     0.5000   -0.0038
##    300        0.5542             nan     0.5000   -0.0022
##    320        0.5454             nan     0.5000   -0.0020
##    340        0.5438             nan     0.5000   -0.0065
##    360        0.5264             nan     0.5000   -0.0048
##    380        0.5213             nan     0.5000   -0.0006
##    400        0.5112             nan     0.5000   -0.0020
##    420        0.4934             nan     0.5000   -0.0039
##    440        0.4934             nan     0.5000   -0.0021
##    460        0.4891             nan     0.5000   -0.0077
##    480        0.4937             nan     0.5000   -0.0080
##    500        0.4649             nan     0.5000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1118             nan     0.5000    0.0805
##      2        1.0379             nan     0.5000    0.0273
##      3        0.9918             nan     0.5000    0.0128
##      4        0.9523             nan     0.5000    0.0097
##      5        0.9229             nan     0.5000    0.0098
##      6        0.8993             nan     0.5000   -0.0014
##      7        0.8902             nan     0.5000   -0.0065
##      8        0.8756             nan     0.5000   -0.0018
##      9        0.8641             nan     0.5000   -0.0114
##     10        0.8557             nan     0.5000   -0.0033
##     20        0.7781             nan     0.5000   -0.0124
##     40        0.6629             nan     0.5000   -0.0007
##     60        0.6018             nan     0.5000   -0.0017
##     80        0.5201             nan     0.5000   -0.0056
##    100        0.4694             nan     0.5000   -0.0069
##    120        0.4257             nan     0.5000   -0.0026
##    140        0.3877             nan     0.5000   -0.0009
##    160        0.3372             nan     0.5000   -0.0006
##    180        0.2960             nan     0.5000   -0.0024
##    200        0.2663             nan     0.5000   -0.0042
##    220        0.2466             nan     0.5000    0.0008
##    240        0.2201             nan     0.5000   -0.0026
##    260        0.1945             nan     0.5000   -0.0007
##    280        0.1728             nan     0.5000   -0.0017
##    300        0.1602             nan     0.5000   -0.0017
##    320        0.1477             nan     0.5000   -0.0022
##    340        0.1313             nan     0.5000   -0.0003
##    360        0.1231             nan     0.5000   -0.0013
##    380        0.1098             nan     0.5000   -0.0014
##    400        0.1022             nan     0.5000   -0.0013
##    420        0.0958             nan     0.5000   -0.0013
##    440        0.0886             nan     0.5000   -0.0020
##    460        0.0821             nan     0.5000   -0.0013
##    480        0.0767             nan     0.5000   -0.0009
##    500        0.0723             nan     0.5000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1150             nan     0.5000    0.0791
##      2        1.0194             nan     0.5000    0.0430
##      3        0.9747             nan     0.5000    0.0154
##      4        0.9415             nan     0.5000    0.0010
##      5        0.9197             nan     0.5000   -0.0062
##      6        0.8955             nan     0.5000    0.0032
##      7        0.8778             nan     0.5000    0.0032
##      8        0.8628             nan     0.5000    0.0003
##      9        0.8487             nan     0.5000   -0.0037
##     10        0.8361             nan     0.5000   -0.0069
##     20        0.7867             nan     0.5000   -0.0105
##     40        0.6898             nan     0.5000   -0.0127
##     60        0.6093             nan     0.5000   -0.0194
##     80        0.5321             nan     0.5000   -0.0056
##    100        0.4604             nan     0.5000   -0.0012
##    120        0.3973             nan     0.5000   -0.0049
##    140        0.3537             nan     0.5000   -0.0056
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1171             nan     0.5000    0.0835
##      2        1.0376             nan     0.5000    0.0328
##      3        1.0091             nan     0.5000    0.0011
##      4        0.9549             nan     0.5000    0.0131
##      5        0.9291             nan     0.5000   -0.0034
##      6        0.9103             nan     0.5000   -0.0034
##      7        0.8892             nan     0.5000    0.0051
##      8        0.8854             nan     0.5000   -0.0086
##      9        0.8738             nan     0.5000   -0.0035
##     10        0.8613             nan     0.5000   -0.0056
##     20        0.7800             nan     0.5000   -0.0137
##     40        0.6621             nan     0.5000   -0.0087
##     60        0.5910             nan     0.5000   -0.0144
##     80        0.5288             nan     0.5000   -0.0076
##    100        0.4680             nan     0.5000    0.0006
##    120        0.4220             nan     0.5000   -0.0032
##    140        0.4425             nan     0.5000   -0.0710
##    160        0.4065             nan     0.5000   -0.0041
##    180        0.3569             nan     0.5000   -0.0027
##    200        0.3203             nan     0.5000   -0.0057
##    220        0.2842             nan     0.5000   -0.0033
##    240        0.2478             nan     0.5000   -0.0048
##    260        0.2196             nan     0.5000   -0.0066
##    280        0.1995             nan     0.5000   -0.0005
##    300        0.1838             nan     0.5000   -0.0012
##    320        0.1641             nan     0.5000   -0.0026
##    340        0.1484             nan     0.5000   -0.0006
##    360        0.1389             nan     0.5000   -0.0031
##    380        0.1237             nan     0.5000   -0.0021
##    400        0.1132             nan     0.5000   -0.0015
##    420        0.1046             nan     0.5000   -0.0015
##    440        0.0970             nan     0.5000   -0.0020
##    460        0.0908             nan     0.5000   -0.0025
##    480        0.0835             nan     0.5000   -0.0012
##    500        0.0774             nan     0.5000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1060             nan     0.5000    0.0891
##      2        1.0061             nan     0.5000    0.0369
##      3        0.9588             nan     0.5000    0.0058
##      4        0.9234             nan     0.5000    0.0011
##      5        0.8937             nan     0.5000   -0.0042
##      6        0.8825             nan     0.5000   -0.0130
##      7        0.8568             nan     0.5000   -0.0008
##      8        0.8413             nan     0.5000   -0.0121
##      9        0.8257             nan     0.5000   -0.0124
##     10        0.8117             nan     0.5000   -0.0131
##     20        0.7607             nan     0.5000   -0.0173
##     40           inf             nan     0.5000       nan
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0913             nan     0.5000    0.0743
##      2        0.9914             nan     0.5000    0.0410
##      3        0.9623             nan     0.5000   -0.0015
##      4        0.9152             nan     0.5000    0.0007
##      5        0.9008             nan     0.5000   -0.0137
##      6        0.8639             nan     0.5000    0.0120
##      7        0.8419             nan     0.5000    0.0015
##      8        0.8321             nan     0.5000   -0.0168
##      9        0.8178             nan     0.5000   -0.0080
##     10        0.7985             nan     0.5000    0.0057
##     20        0.6865             nan     0.5000   -0.0060
##     40        0.5529             nan     0.5000   -0.0046
##     60        0.4816             nan     0.5000   -0.0041
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0900             nan     0.5000    0.0996
##      2        0.9795             nan     0.5000    0.0486
##      3        0.9387             nan     0.5000    0.0055
##      4        0.9036             nan     0.5000   -0.0093
##      5        0.8663             nan     0.5000    0.0018
##      6        0.8419             nan     0.5000   -0.0017
##      7        0.8191             nan     0.5000    0.0020
##      8        0.8020             nan     0.5000   -0.0021
##      9        0.7910             nan     0.5000   -0.0074
##     10        0.7804             nan     0.5000   -0.0101
##     20        0.6923             nan     0.5000   -0.0018
##     40        0.5592             nan     0.5000   -0.0021
##     60        0.4489             nan     0.5000   -0.0067
##     80        0.3711             nan     0.5000   -0.0034
##    100        0.2980             nan     0.5000   -0.0047
##    120        0.2413             nan     0.5000   -0.0021
##    140        0.1938             nan     0.5000   -0.0041
##    160        0.1626             nan     0.5000   -0.0009
##    180        0.1349             nan     0.5000   -0.0023
##    200        0.1172             nan     0.5000   -0.0018
##    220        0.1018             nan     0.5000   -0.0006
##    240        0.0892             nan     0.5000   -0.0018
##    260        0.0798             nan     0.5000   -0.0022
##    280        0.0675             nan     0.5000   -0.0004
##    300        0.0572             nan     0.5000   -0.0019
##    320        0.0505             nan     0.5000   -0.0005
##    340        0.0436             nan     0.5000   -0.0004
##    360        0.0389             nan     0.5000   -0.0005
##    380        0.0348             nan     0.5000   -0.0002
##    400        0.0311             nan     0.5000   -0.0004
##    420        0.0282             nan     0.5000   -0.0003
##    440        0.0255             nan     0.5000   -0.0002
##    460        0.0216             nan     0.5000   -0.0001
##    480        0.0195             nan     0.5000   -0.0002
##    500        0.0172             nan     0.5000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1158             nan     1.0000    0.0767
##      2        1.0423             nan     1.0000    0.0249
##      3        1.0053             nan     1.0000    0.0057
##      4        0.9769             nan     1.0000   -0.0043
##      5        0.9834             nan     1.0000   -0.0362
##      6        1.0110             nan     1.0000   -0.0540
##      7        0.9709             nan     1.0000    0.0160
##      8        0.9695             nan     1.0000   -0.0196
##      9        0.9623             nan     1.0000   -0.0137
##     10        0.9325             nan     1.0000    0.0095
##     20        0.8886             nan     1.0000   -0.0168
##     40        1.4646             nan     1.0000   -0.0306
##     60        1.3977             nan     1.0000    0.0058
##     80        1.3472             nan     1.0000   -0.0006
##    100        1.3348             nan     1.0000   -0.0125
##    120        1.3152             nan     1.0000    0.0013
##    140        1.3143             nan     1.0000   -0.0140
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1149             nan     1.0000    0.0731
##      2        1.0643             nan     1.0000    0.0125
##      3        1.0184             nan     1.0000    0.0143
##      4        1.0134             nan     1.0000   -0.0300
##      5        0.9858             nan     1.0000   -0.0040
##      6        1.0062             nan     1.0000   -0.0369
##      7        0.9838             nan     1.0000   -0.0014
##      8        0.9755             nan     1.0000   -0.0138
##      9        1.0046             nan     1.0000   -0.0494
##     10        1.2956             nan     1.0000   -0.3158
##     20      543.7399             nan     1.0000    0.0167
##     40      543.6978             nan     1.0000   -0.0065
##     60      548.5043             nan     1.0000   -0.0295
##     80      548.4274             nan     1.0000   -0.0070
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1186             nan     1.0000    0.0696
##      2        1.0604             nan     1.0000    0.0155
##      3        1.0580             nan     1.0000   -0.0166
##      4        1.0232             nan     1.0000    0.0148
##      5        1.0064             nan     1.0000   -0.0226
##      6        0.9997             nan     1.0000   -0.0242
##      7        0.9912             nan     1.0000   -0.0036
##      8        0.9763             nan     1.0000    0.0029
##      9        0.9669             nan     1.0000    0.0016
##     10        0.9627             nan     1.0000   -0.0108
##     20        0.8798             nan     1.0000   -0.0183
##     40        3.8492             nan     1.0000   -0.0173
##     60        7.0538             nan     1.0000   -0.0266
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0913             nan     1.0000    0.0640
##      2        1.0206             nan     1.0000    0.0001
##      3        1.0145             nan     1.0000   -0.0305
##      4        0.9507             nan     1.0000    0.0138
##      5        0.9261             nan     1.0000   -0.0264
##      6        0.8807             nan     1.0000   -0.0165
##      7        0.8575             nan     1.0000   -0.0131
##      8        0.9123             nan     1.0000   -0.0946
##      9        0.9078             nan     1.0000   -0.0160
##     10        0.9074             nan     1.0000   -0.0326
##     20 28774879976.9367             nan     1.0000   -0.1370
##     40 28789022483.0281             nan     1.0000   -0.0132
##     60 28789022483.0081             nan     1.0000   -0.0115
##     80 28789026460.9178             nan     1.0000    0.0000
##    100 28789026460.9149             nan     1.0000   -0.0015
##    120 28789026460.9338             nan     1.0000   -0.0268
##    140 28789026460.8627             nan     1.0000   -0.0320
##    160 28789026461.0191             nan     1.0000   -0.0407
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1027             nan     1.0000    0.0583
##      2        1.0129             nan     1.0000    0.0199
##      3        1.0044             nan     1.0000   -0.0423
##      4        1.0032             nan     1.0000   -0.0294
##      5        0.9675             nan     1.0000   -0.0057
##      6        0.9386             nan     1.0000   -0.0135
##      7        0.9273             nan     1.0000   -0.0046
##      8        0.9563             nan     1.0000   -0.0598
##      9        0.9242             nan     1.0000    0.0043
##     10        0.8994             nan     1.0000    0.0025
##     20        0.8700             nan     1.0000   -0.0379
##     40  2076618.0428             nan     1.0000   -0.0068
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0724             nan     1.0000    0.0825
##      2        0.9845             nan     1.0000    0.0284
##      3        0.9525             nan     1.0000    0.0058
##      4        0.9472             nan     1.0000   -0.0266
##      5        0.9349             nan     1.0000   -0.0226
##      6        0.9324             nan     1.0000   -0.0140
##      7        0.9142             nan     1.0000   -0.0040
##      8        0.8963             nan     1.0000   -0.0037
##      9        0.9036             nan     1.0000   -0.0263
##     10        0.8542             nan     1.0000    0.0050
##     20       32.9208             nan     1.0000  -31.9771
##     40       33.4190             nan     1.0000   -0.0538
##     60    21242.4558             nan     1.0000   -0.0000
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0452             nan     1.0000    0.0899
##      2        1.0353             nan     1.0000   -0.0466
##      3        0.9464             nan     1.0000    0.0336
##      4        0.9157             nan     1.0000   -0.0191
##      5        0.9503             nan     1.0000   -0.1008
##      6        0.9383             nan     1.0000   -0.0165
##      7        0.9199             nan     1.0000   -0.0147
##      8        0.9364             nan     1.0000   -0.0560
##      9        0.9241             nan     1.0000   -0.0143
##     10        1.0724             nan     1.0000   -0.2129
##     20        2.3101             nan     1.0000   -0.6892
##     40   169320.2191             nan     1.0000   -0.0134
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0272             nan     1.0000    0.0975
##      2        0.9336             nan     1.0000    0.0042
##      3        0.9058             nan     1.0000   -0.0146
##      4        0.9381             nan     1.0000   -0.0795
##      5        0.9098             nan     1.0000   -0.0230
##      6        0.9175             nan     1.0000   -0.0425
##      7        0.9140             nan     1.0000   -0.0534
##      8        1.1357             nan     1.0000   -0.3002
##      9           inf             nan     1.0000    0.0071
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        0.9992             nan     1.0000    0.1452
##      2        0.9373             nan     1.0000    0.0194
##      3        0.9077             nan     1.0000   -0.0204
##      4        0.8816             nan     1.0000   -0.0191
##      5        0.8613             nan     1.0000   -0.0087
##      6        0.8440             nan     1.0000   -0.0126
##      7        0.8318             nan     1.0000   -0.0183
##      8        0.8075             nan     1.0000   -0.0160
##      9        0.8235             nan     1.0000   -0.0500
##     10        0.8208             nan     1.0000   -0.0260
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0001
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0001
##     20        1.2864             nan     0.0010    0.0002
##     40        1.2795             nan     0.0010    0.0001
##     60        1.2728             nan     0.0010    0.0001
##     80        1.2665             nan     0.0010    0.0001
##    100        1.2603             nan     0.0010    0.0002
##    120        1.2544             nan     0.0010    0.0001
##    140        1.2486             nan     0.0010    0.0001
##    160        1.2431             nan     0.0010    0.0001
##    180        1.2377             nan     0.0010    0.0001
##    200        1.2324             nan     0.0010    0.0001
##    220        1.2272             nan     0.0010    0.0001
##    240        1.2224             nan     0.0010    0.0001
##    260        1.2175             nan     0.0010    0.0001
##    280        1.2128             nan     0.0010    0.0001
##    300        1.2083             nan     0.0010    0.0001
##    320        1.2040             nan     0.0010    0.0001
##    340        1.1997             nan     0.0010    0.0001
##    360        1.1955             nan     0.0010    0.0001
##    380        1.1913             nan     0.0010    0.0001
##    400        1.1871             nan     0.0010    0.0001
##    420        1.1832             nan     0.0010    0.0001
##    440        1.1793             nan     0.0010    0.0001
##    460        1.1756             nan     0.0010    0.0001
##    480        1.1719             nan     0.0010    0.0001
##    500        1.1683             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2927             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0001
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0001
##     10        1.2899             nan     0.0010    0.0002
##     20        1.2863             nan     0.0010    0.0002
##     40        1.2795             nan     0.0010    0.0001
##     60        1.2727             nan     0.0010    0.0002
##     80        1.2663             nan     0.0010    0.0001
##    100        1.2602             nan     0.0010    0.0001
##    120        1.2543             nan     0.0010    0.0001
##    140        1.2485             nan     0.0010    0.0001
##    160        1.2428             nan     0.0010    0.0001
##    180        1.2373             nan     0.0010    0.0001
##    200        1.2322             nan     0.0010    0.0001
##    220        1.2271             nan     0.0010    0.0001
##    240        1.2222             nan     0.0010    0.0001
##    260        1.2173             nan     0.0010    0.0001
##    280        1.2128             nan     0.0010    0.0001
##    300        1.2081             nan     0.0010    0.0001
##    320        1.2038             nan     0.0010    0.0001
##    340        1.1996             nan     0.0010    0.0001
##    360        1.1952             nan     0.0010    0.0001
##    380        1.1912             nan     0.0010    0.0001
##    400        1.1871             nan     0.0010    0.0001
##    420        1.1832             nan     0.0010    0.0001
##    440        1.1795             nan     0.0010    0.0001
##    460        1.1757             nan     0.0010    0.0001
##    480        1.1720             nan     0.0010    0.0001
##    500        1.1685             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0001
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2899             nan     0.0010    0.0002
##     20        1.2863             nan     0.0010    0.0002
##     40        1.2794             nan     0.0010    0.0002
##     60        1.2728             nan     0.0010    0.0002
##     80        1.2662             nan     0.0010    0.0001
##    100        1.2600             nan     0.0010    0.0001
##    120        1.2541             nan     0.0010    0.0001
##    140        1.2482             nan     0.0010    0.0001
##    160        1.2424             nan     0.0010    0.0001
##    180        1.2369             nan     0.0010    0.0001
##    200        1.2317             nan     0.0010    0.0001
##    220        1.2267             nan     0.0010    0.0001
##    240        1.2216             nan     0.0010    0.0001
##    260        1.2169             nan     0.0010    0.0001
##    280        1.2123             nan     0.0010    0.0001
##    300        1.2077             nan     0.0010    0.0001
##    320        1.2031             nan     0.0010    0.0001
##    340        1.1989             nan     0.0010    0.0001
##    360        1.1947             nan     0.0010    0.0001
##    380        1.1908             nan     0.0010    0.0001
##    400        1.1867             nan     0.0010    0.0001
##    420        1.1827             nan     0.0010    0.0001
##    440        1.1789             nan     0.0010    0.0001
##    460        1.1751             nan     0.0010    0.0001
##    480        1.1713             nan     0.0010    0.0001
##    500        1.1677             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2842             nan     0.0010    0.0002
##     40        1.2753             nan     0.0010    0.0002
##     60        1.2669             nan     0.0010    0.0002
##     80        1.2583             nan     0.0010    0.0002
##    100        1.2503             nan     0.0010    0.0002
##    120        1.2426             nan     0.0010    0.0002
##    140        1.2351             nan     0.0010    0.0002
##    160        1.2276             nan     0.0010    0.0002
##    180        1.2205             nan     0.0010    0.0001
##    200        1.2133             nan     0.0010    0.0002
##    220        1.2066             nan     0.0010    0.0001
##    240        1.1998             nan     0.0010    0.0001
##    260        1.1933             nan     0.0010    0.0001
##    280        1.1870             nan     0.0010    0.0001
##    300        1.1809             nan     0.0010    0.0001
##    320        1.1750             nan     0.0010    0.0001
##    340        1.1693             nan     0.0010    0.0001
##    360        1.1637             nan     0.0010    0.0001
##    380        1.1583             nan     0.0010    0.0001
##    400        1.1526             nan     0.0010    0.0001
##    420        1.1475             nan     0.0010    0.0001
##    440        1.1425             nan     0.0010    0.0001
##    460        1.1373             nan     0.0010    0.0001
##    480        1.1323             nan     0.0010    0.0001
##    500        1.1275             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0003
##     20        1.2841             nan     0.0010    0.0002
##     40        1.2751             nan     0.0010    0.0002
##     60        1.2665             nan     0.0010    0.0002
##     80        1.2580             nan     0.0010    0.0002
##    100        1.2499             nan     0.0010    0.0002
##    120        1.2419             nan     0.0010    0.0002
##    140        1.2342             nan     0.0010    0.0002
##    160        1.2269             nan     0.0010    0.0001
##    180        1.2198             nan     0.0010    0.0002
##    200        1.2129             nan     0.0010    0.0001
##    220        1.2061             nan     0.0010    0.0001
##    240        1.1995             nan     0.0010    0.0002
##    260        1.1930             nan     0.0010    0.0001
##    280        1.1870             nan     0.0010    0.0001
##    300        1.1809             nan     0.0010    0.0001
##    320        1.1750             nan     0.0010    0.0001
##    340        1.1693             nan     0.0010    0.0002
##    360        1.1636             nan     0.0010    0.0001
##    380        1.1583             nan     0.0010    0.0001
##    400        1.1529             nan     0.0010    0.0001
##    420        1.1478             nan     0.0010    0.0001
##    440        1.1426             nan     0.0010    0.0001
##    460        1.1376             nan     0.0010    0.0001
##    480        1.1327             nan     0.0010    0.0001
##    500        1.1279             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2902             nan     0.0010    0.0002
##      8        1.2898             nan     0.0010    0.0002
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2889             nan     0.0010    0.0002
##     20        1.2842             nan     0.0010    0.0002
##     40        1.2753             nan     0.0010    0.0002
##     60        1.2666             nan     0.0010    0.0002
##     80        1.2580             nan     0.0010    0.0002
##    100        1.2500             nan     0.0010    0.0002
##    120        1.2421             nan     0.0010    0.0002
##    140        1.2342             nan     0.0010    0.0002
##    160        1.2266             nan     0.0010    0.0001
##    180        1.2193             nan     0.0010    0.0002
##    200        1.2124             nan     0.0010    0.0002
##    220        1.2055             nan     0.0010    0.0002
##    240        1.1988             nan     0.0010    0.0002
##    260        1.1925             nan     0.0010    0.0001
##    280        1.1861             nan     0.0010    0.0001
##    300        1.1800             nan     0.0010    0.0001
##    320        1.1741             nan     0.0010    0.0001
##    340        1.1684             nan     0.0010    0.0001
##    360        1.1626             nan     0.0010    0.0001
##    380        1.1571             nan     0.0010    0.0001
##    400        1.1517             nan     0.0010    0.0001
##    420        1.1466             nan     0.0010    0.0001
##    440        1.1416             nan     0.0010    0.0001
##    460        1.1368             nan     0.0010    0.0001
##    480        1.1322             nan     0.0010    0.0001
##    500        1.1275             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2912             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0003
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0003
##      8        1.2890             nan     0.0010    0.0003
##      9        1.2885             nan     0.0010    0.0003
##     10        1.2880             nan     0.0010    0.0003
##     20        1.2826             nan     0.0010    0.0002
##     40        1.2725             nan     0.0010    0.0002
##     60        1.2626             nan     0.0010    0.0002
##     80        1.2530             nan     0.0010    0.0002
##    100        1.2438             nan     0.0010    0.0002
##    120        1.2347             nan     0.0010    0.0002
##    140        1.2258             nan     0.0010    0.0002
##    160        1.2174             nan     0.0010    0.0002
##    180        1.2091             nan     0.0010    0.0002
##    200        1.2011             nan     0.0010    0.0002
##    220        1.1933             nan     0.0010    0.0002
##    240        1.1857             nan     0.0010    0.0002
##    260        1.1782             nan     0.0010    0.0001
##    280        1.1709             nan     0.0010    0.0001
##    300        1.1639             nan     0.0010    0.0001
##    320        1.1572             nan     0.0010    0.0002
##    340        1.1507             nan     0.0010    0.0001
##    360        1.1442             nan     0.0010    0.0001
##    380        1.1379             nan     0.0010    0.0001
##    400        1.1318             nan     0.0010    0.0001
##    420        1.1258             nan     0.0010    0.0001
##    440        1.1200             nan     0.0010    0.0001
##    460        1.1145             nan     0.0010    0.0001
##    480        1.1090             nan     0.0010    0.0001
##    500        1.1038             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0003
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0003
##      9        1.2885             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0003
##     20        1.2828             nan     0.0010    0.0002
##     40        1.2723             nan     0.0010    0.0002
##     60        1.2624             nan     0.0010    0.0002
##     80        1.2526             nan     0.0010    0.0002
##    100        1.2432             nan     0.0010    0.0002
##    120        1.2344             nan     0.0010    0.0002
##    140        1.2259             nan     0.0010    0.0002
##    160        1.2173             nan     0.0010    0.0002
##    180        1.2091             nan     0.0010    0.0001
##    200        1.2009             nan     0.0010    0.0001
##    220        1.1931             nan     0.0010    0.0002
##    240        1.1856             nan     0.0010    0.0002
##    260        1.1781             nan     0.0010    0.0001
##    280        1.1710             nan     0.0010    0.0001
##    300        1.1642             nan     0.0010    0.0002
##    320        1.1576             nan     0.0010    0.0002
##    340        1.1507             nan     0.0010    0.0001
##    360        1.1441             nan     0.0010    0.0001
##    380        1.1378             nan     0.0010    0.0001
##    400        1.1319             nan     0.0010    0.0001
##    420        1.1258             nan     0.0010    0.0001
##    440        1.1201             nan     0.0010    0.0001
##    460        1.1144             nan     0.0010    0.0001
##    480        1.1090             nan     0.0010    0.0001
##    500        1.1036             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2895             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0003
##     20        1.2826             nan     0.0010    0.0002
##     40        1.2724             nan     0.0010    0.0002
##     60        1.2623             nan     0.0010    0.0002
##     80        1.2525             nan     0.0010    0.0002
##    100        1.2431             nan     0.0010    0.0002
##    120        1.2340             nan     0.0010    0.0002
##    140        1.2252             nan     0.0010    0.0002
##    160        1.2168             nan     0.0010    0.0002
##    180        1.2086             nan     0.0010    0.0002
##    200        1.2005             nan     0.0010    0.0002
##    220        1.1930             nan     0.0010    0.0002
##    240        1.1854             nan     0.0010    0.0002
##    260        1.1781             nan     0.0010    0.0001
##    280        1.1709             nan     0.0010    0.0002
##    300        1.1642             nan     0.0010    0.0001
##    320        1.1575             nan     0.0010    0.0001
##    340        1.1509             nan     0.0010    0.0001
##    360        1.1446             nan     0.0010    0.0001
##    380        1.1384             nan     0.0010    0.0001
##    400        1.1323             nan     0.0010    0.0002
##    420        1.1264             nan     0.0010    0.0001
##    440        1.1206             nan     0.0010    0.0001
##    460        1.1152             nan     0.0010    0.0001
##    480        1.1097             nan     0.0010    0.0001
##    500        1.1045             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2570             nan     0.1000    0.0164
##      2        1.2316             nan     0.1000    0.0123
##      3        1.2061             nan     0.1000    0.0095
##      4        1.1832             nan     0.1000    0.0101
##      5        1.1640             nan     0.1000    0.0066
##      6        1.1454             nan     0.1000    0.0074
##      7        1.1300             nan     0.1000    0.0071
##      8        1.1149             nan     0.1000    0.0055
##      9        1.1016             nan     0.1000    0.0055
##     10        1.0889             nan     0.1000    0.0051
##     20        1.0062             nan     0.1000    0.0013
##     40        0.9296             nan     0.1000   -0.0002
##     60        0.8844             nan     0.1000   -0.0005
##     80        0.8603             nan     0.1000   -0.0011
##    100        0.8415             nan     0.1000   -0.0003
##    120        0.8274             nan     0.1000    0.0000
##    140        0.8166             nan     0.1000   -0.0020
##    160        0.8073             nan     0.1000   -0.0006
##    180        0.7994             nan     0.1000   -0.0011
##    200        0.7915             nan     0.1000    0.0000
##    220        0.7870             nan     0.1000   -0.0013
##    240        0.7796             nan     0.1000   -0.0008
##    260        0.7721             nan     0.1000   -0.0005
##    280        0.7661             nan     0.1000   -0.0007
##    300        0.7590             nan     0.1000   -0.0010
##    320        0.7532             nan     0.1000   -0.0002
##    340        0.7475             nan     0.1000   -0.0008
##    360        0.7428             nan     0.1000   -0.0010
##    380        0.7384             nan     0.1000   -0.0003
##    400        0.7319             nan     0.1000   -0.0007
##    420        0.7263             nan     0.1000   -0.0006
##    440        0.7213             nan     0.1000   -0.0007
##    460        0.7172             nan     0.1000   -0.0012
##    480        0.7126             nan     0.1000   -0.0007
##    500        0.7087             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2577             nan     0.1000    0.0164
##      2        1.2309             nan     0.1000    0.0123
##      3        1.2084             nan     0.1000    0.0084
##      4        1.1870             nan     0.1000    0.0087
##      5        1.1697             nan     0.1000    0.0070
##      6        1.1544             nan     0.1000    0.0064
##      7        1.1388             nan     0.1000    0.0071
##      8        1.1228             nan     0.1000    0.0071
##      9        1.1098             nan     0.1000    0.0045
##     10        1.0988             nan     0.1000    0.0021
##     20        1.0103             nan     0.1000    0.0009
##     40        0.9300             nan     0.1000    0.0014
##     60        0.8931             nan     0.1000    0.0001
##     80        0.8685             nan     0.1000   -0.0012
##    100        0.8499             nan     0.1000   -0.0008
##    120        0.8353             nan     0.1000   -0.0009
##    140        0.8234             nan     0.1000   -0.0014
##    160        0.8145             nan     0.1000   -0.0017
##    180        0.8058             nan     0.1000   -0.0009
##    200        0.7948             nan     0.1000   -0.0014
##    220        0.7864             nan     0.1000   -0.0004
##    240        0.7789             nan     0.1000   -0.0011
##    260        0.7714             nan     0.1000   -0.0014
##    280        0.7637             nan     0.1000   -0.0010
##    300        0.7553             nan     0.1000   -0.0008
##    320        0.7482             nan     0.1000   -0.0009
##    340        0.7435             nan     0.1000   -0.0001
##    360        0.7389             nan     0.1000   -0.0004
##    380        0.7333             nan     0.1000   -0.0006
##    400        0.7289             nan     0.1000   -0.0017
##    420        0.7238             nan     0.1000   -0.0012
##    440        0.7185             nan     0.1000   -0.0010
##    460        0.7131             nan     0.1000   -0.0008
##    480        0.7086             nan     0.1000   -0.0008
##    500        0.7072             nan     0.1000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2615             nan     0.1000    0.0160
##      2        1.2332             nan     0.1000    0.0120
##      3        1.2142             nan     0.1000    0.0066
##      4        1.1872             nan     0.1000    0.0119
##      5        1.1681             nan     0.1000    0.0088
##      6        1.1522             nan     0.1000    0.0071
##      7        1.1430             nan     0.1000    0.0009
##      8        1.1280             nan     0.1000    0.0056
##      9        1.1179             nan     0.1000    0.0041
##     10        1.1037             nan     0.1000    0.0060
##     20        1.0089             nan     0.1000    0.0032
##     40        0.9346             nan     0.1000   -0.0005
##     60        0.8904             nan     0.1000   -0.0011
##     80        0.8689             nan     0.1000   -0.0006
##    100        0.8489             nan     0.1000   -0.0008
##    120        0.8353             nan     0.1000   -0.0007
##    140        0.8235             nan     0.1000   -0.0005
##    160        0.8144             nan     0.1000   -0.0019
##    180        0.8039             nan     0.1000   -0.0011
##    200        0.7950             nan     0.1000   -0.0014
##    220        0.7890             nan     0.1000   -0.0024
##    240        0.7796             nan     0.1000   -0.0013
##    260        0.7728             nan     0.1000   -0.0012
##    280        0.7670             nan     0.1000   -0.0004
##    300        0.7626             nan     0.1000   -0.0019
##    320        0.7556             nan     0.1000   -0.0006
##    340        0.7491             nan     0.1000   -0.0008
##    360        0.7427             nan     0.1000   -0.0004
##    380        0.7380             nan     0.1000   -0.0007
##    400        0.7327             nan     0.1000   -0.0009
##    420        0.7273             nan     0.1000   -0.0008
##    440        0.7224             nan     0.1000   -0.0008
##    460        0.7174             nan     0.1000   -0.0006
##    480        0.7128             nan     0.1000   -0.0008
##    500        0.7084             nan     0.1000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2502             nan     0.1000    0.0221
##      2        1.2143             nan     0.1000    0.0167
##      3        1.1769             nan     0.1000    0.0158
##      4        1.1466             nan     0.1000    0.0141
##      5        1.1217             nan     0.1000    0.0108
##      6        1.1001             nan     0.1000    0.0082
##      7        1.0779             nan     0.1000    0.0070
##      8        1.0624             nan     0.1000    0.0063
##      9        1.0469             nan     0.1000    0.0046
##     10        1.0325             nan     0.1000    0.0039
##     20        0.9420             nan     0.1000    0.0006
##     40        0.8654             nan     0.1000    0.0003
##     60        0.8067             nan     0.1000   -0.0010
##     80        0.7714             nan     0.1000   -0.0027
##    100        0.7427             nan     0.1000   -0.0011
##    120        0.7184             nan     0.1000   -0.0011
##    140        0.6931             nan     0.1000   -0.0015
##    160        0.6746             nan     0.1000   -0.0009
##    180        0.6557             nan     0.1000   -0.0003
##    200        0.6357             nan     0.1000   -0.0007
##    220        0.6177             nan     0.1000   -0.0012
##    240        0.6038             nan     0.1000   -0.0008
##    260        0.5857             nan     0.1000   -0.0014
##    280        0.5699             nan     0.1000   -0.0027
##    300        0.5528             nan     0.1000   -0.0012
##    320        0.5333             nan     0.1000   -0.0001
##    340        0.5224             nan     0.1000   -0.0015
##    360        0.5091             nan     0.1000   -0.0017
##    380        0.4943             nan     0.1000   -0.0006
##    400        0.4825             nan     0.1000   -0.0011
##    420        0.4699             nan     0.1000   -0.0005
##    440        0.4596             nan     0.1000   -0.0017
##    460        0.4492             nan     0.1000   -0.0019
##    480        0.4391             nan     0.1000   -0.0001
##    500        0.4283             nan     0.1000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2441             nan     0.1000    0.0199
##      2        1.2065             nan     0.1000    0.0166
##      3        1.1725             nan     0.1000    0.0160
##      4        1.1500             nan     0.1000    0.0111
##      5        1.1267             nan     0.1000    0.0084
##      6        1.1040             nan     0.1000    0.0100
##      7        1.0832             nan     0.1000    0.0087
##      8        1.0660             nan     0.1000    0.0062
##      9        1.0502             nan     0.1000    0.0030
##     10        1.0355             nan     0.1000    0.0071
##     20        0.9449             nan     0.1000   -0.0017
##     40        0.8623             nan     0.1000   -0.0019
##     60        0.8136             nan     0.1000   -0.0019
##     80        0.7725             nan     0.1000   -0.0012
##    100        0.7446             nan     0.1000   -0.0012
##    120        0.7254             nan     0.1000   -0.0006
##    140        0.7066             nan     0.1000    0.0001
##    160        0.6835             nan     0.1000   -0.0007
##    180        0.6650             nan     0.1000   -0.0010
##    200        0.6483             nan     0.1000   -0.0010
##    220        0.6305             nan     0.1000   -0.0018
##    240        0.6177             nan     0.1000   -0.0012
##    260        0.5998             nan     0.1000   -0.0013
##    280        0.5790             nan     0.1000   -0.0023
##    300        0.5626             nan     0.1000   -0.0017
##    320        0.5442             nan     0.1000   -0.0006
##    340        0.5292             nan     0.1000   -0.0005
##    360        0.5154             nan     0.1000   -0.0006
##    380        0.5024             nan     0.1000   -0.0012
##    400        0.4917             nan     0.1000   -0.0011
##    420        0.4794             nan     0.1000   -0.0015
##    440        0.4678             nan     0.1000   -0.0013
##    460        0.4584             nan     0.1000   -0.0008
##    480        0.4468             nan     0.1000   -0.0007
##    500        0.4386             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2467             nan     0.1000    0.0206
##      2        1.2121             nan     0.1000    0.0169
##      3        1.1794             nan     0.1000    0.0142
##      4        1.1529             nan     0.1000    0.0111
##      5        1.1292             nan     0.1000    0.0085
##      6        1.1056             nan     0.1000    0.0099
##      7        1.0863             nan     0.1000    0.0067
##      8        1.0673             nan     0.1000    0.0061
##      9        1.0501             nan     0.1000    0.0086
##     10        1.0386             nan     0.1000    0.0036
##     20        0.9391             nan     0.1000    0.0016
##     40        0.8520             nan     0.1000   -0.0009
##     60        0.8091             nan     0.1000   -0.0003
##     80        0.7789             nan     0.1000   -0.0010
##    100        0.7465             nan     0.1000   -0.0012
##    120        0.7216             nan     0.1000   -0.0015
##    140        0.7012             nan     0.1000   -0.0022
##    160        0.6825             nan     0.1000   -0.0008
##    180        0.6577             nan     0.1000   -0.0012
##    200        0.6389             nan     0.1000   -0.0016
##    220        0.6218             nan     0.1000   -0.0011
##    240        0.6069             nan     0.1000   -0.0017
##    260        0.5914             nan     0.1000   -0.0018
##    280        0.5757             nan     0.1000   -0.0023
##    300        0.5574             nan     0.1000   -0.0017
##    320        0.5429             nan     0.1000   -0.0010
##    340        0.5300             nan     0.1000   -0.0005
##    360        0.5143             nan     0.1000   -0.0011
##    380        0.5005             nan     0.1000   -0.0010
##    400        0.4929             nan     0.1000   -0.0011
##    420        0.4822             nan     0.1000   -0.0009
##    440        0.4708             nan     0.1000   -0.0016
##    460        0.4622             nan     0.1000   -0.0005
##    480        0.4507             nan     0.1000   -0.0011
##    500        0.4412             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2434             nan     0.1000    0.0238
##      2        1.2018             nan     0.1000    0.0168
##      3        1.1719             nan     0.1000    0.0132
##      4        1.1384             nan     0.1000    0.0122
##      5        1.1083             nan     0.1000    0.0109
##      6        1.0827             nan     0.1000    0.0088
##      7        1.0605             nan     0.1000    0.0076
##      8        1.0424             nan     0.1000    0.0063
##      9        1.0214             nan     0.1000    0.0074
##     10        1.0076             nan     0.1000    0.0049
##     20        0.9000             nan     0.1000    0.0011
##     40        0.8026             nan     0.1000    0.0017
##     60        0.7448             nan     0.1000   -0.0017
##     80        0.6950             nan     0.1000   -0.0011
##    100        0.6589             nan     0.1000   -0.0012
##    120        0.6292             nan     0.1000   -0.0013
##    140        0.5966             nan     0.1000   -0.0029
##    160        0.5656             nan     0.1000   -0.0005
##    180        0.5409             nan     0.1000   -0.0020
##    200        0.5107             nan     0.1000   -0.0010
##    220        0.4820             nan     0.1000   -0.0010
##    240        0.4610             nan     0.1000   -0.0001
##    260        0.4431             nan     0.1000   -0.0001
##    280        0.4244             nan     0.1000   -0.0005
##    300        0.4064             nan     0.1000   -0.0011
##    320        0.3880             nan     0.1000   -0.0006
##    340        0.3713             nan     0.1000   -0.0009
##    360        0.3548             nan     0.1000   -0.0006
##    380        0.3378             nan     0.1000   -0.0004
##    400        0.3252             nan     0.1000   -0.0007
##    420        0.3154             nan     0.1000   -0.0006
##    440        0.3015             nan     0.1000   -0.0004
##    460        0.2878             nan     0.1000   -0.0005
##    480        0.2786             nan     0.1000   -0.0007
##    500        0.2703             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2430             nan     0.1000    0.0217
##      2        1.1952             nan     0.1000    0.0197
##      3        1.1535             nan     0.1000    0.0163
##      4        1.1187             nan     0.1000    0.0149
##      5        1.0948             nan     0.1000    0.0084
##      6        1.0734             nan     0.1000    0.0088
##      7        1.0546             nan     0.1000    0.0074
##      8        1.0350             nan     0.1000    0.0057
##      9        1.0165             nan     0.1000    0.0046
##     10        0.9985             nan     0.1000    0.0058
##     20        0.9005             nan     0.1000    0.0011
##     40        0.8051             nan     0.1000   -0.0006
##     60        0.7478             nan     0.1000   -0.0012
##     80        0.7047             nan     0.1000   -0.0002
##    100        0.6646             nan     0.1000   -0.0022
##    120        0.6308             nan     0.1000   -0.0008
##    140        0.5970             nan     0.1000   -0.0023
##    160        0.5664             nan     0.1000   -0.0010
##    180        0.5386             nan     0.1000   -0.0019
##    200        0.5117             nan     0.1000   -0.0017
##    220        0.4902             nan     0.1000   -0.0008
##    240        0.4693             nan     0.1000   -0.0001
##    260        0.4475             nan     0.1000   -0.0021
##    280        0.4295             nan     0.1000   -0.0006
##    300        0.4122             nan     0.1000   -0.0003
##    320        0.3950             nan     0.1000   -0.0005
##    340        0.3769             nan     0.1000   -0.0013
##    360        0.3594             nan     0.1000   -0.0012
##    380        0.3455             nan     0.1000   -0.0009
##    400        0.3325             nan     0.1000   -0.0005
##    420        0.3198             nan     0.1000   -0.0012
##    440        0.3074             nan     0.1000   -0.0005
##    460        0.2964             nan     0.1000   -0.0005
##    480        0.2855             nan     0.1000   -0.0005
##    500        0.2759             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2481             nan     0.1000    0.0180
##      2        1.1997             nan     0.1000    0.0209
##      3        1.1604             nan     0.1000    0.0153
##      4        1.1288             nan     0.1000    0.0142
##      5        1.1032             nan     0.1000    0.0102
##      6        1.0784             nan     0.1000    0.0103
##      7        1.0579             nan     0.1000    0.0077
##      8        1.0366             nan     0.1000    0.0094
##      9        1.0217             nan     0.1000    0.0067
##     10        1.0053             nan     0.1000    0.0038
##     20        0.9047             nan     0.1000    0.0019
##     40        0.8106             nan     0.1000   -0.0005
##     60        0.7476             nan     0.1000   -0.0009
##     80        0.7079             nan     0.1000   -0.0011
##    100        0.6651             nan     0.1000   -0.0013
##    120        0.6316             nan     0.1000   -0.0009
##    140        0.5994             nan     0.1000   -0.0009
##    160        0.5688             nan     0.1000   -0.0014
##    180        0.5381             nan     0.1000   -0.0019
##    200        0.5155             nan     0.1000   -0.0008
##    220        0.4888             nan     0.1000   -0.0006
##    240        0.4682             nan     0.1000   -0.0011
##    260        0.4504             nan     0.1000   -0.0005
##    280        0.4338             nan     0.1000   -0.0006
##    300        0.4175             nan     0.1000   -0.0012
##    320        0.3962             nan     0.1000   -0.0012
##    340        0.3829             nan     0.1000   -0.0005
##    360        0.3656             nan     0.1000   -0.0004
##    380        0.3491             nan     0.1000   -0.0003
##    400        0.3371             nan     0.1000   -0.0012
##    420        0.3256             nan     0.1000   -0.0008
##    440        0.3118             nan     0.1000   -0.0005
##    460        0.3010             nan     0.1000   -0.0010
##    480        0.2912             nan     0.1000   -0.0003
##    500        0.2788             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2341             nan     0.2000    0.0215
##      2        1.1765             nan     0.2000    0.0236
##      3        1.1456             nan     0.2000    0.0131
##      4        1.1171             nan     0.2000    0.0104
##      5        1.0916             nan     0.2000    0.0106
##      6        1.0699             nan     0.2000    0.0067
##      7        1.0562             nan     0.2000    0.0055
##      8        1.0348             nan     0.2000    0.0071
##      9        1.0163             nan     0.2000    0.0066
##     10        1.0073             nan     0.2000    0.0014
##     20        0.9271             nan     0.2000    0.0020
##     40        0.8685             nan     0.2000   -0.0024
##     60        0.8405             nan     0.2000   -0.0011
##     80        0.8155             nan     0.2000   -0.0009
##    100        0.7962             nan     0.2000   -0.0023
##    120        0.7785             nan     0.2000   -0.0022
##    140        0.7674             nan     0.2000   -0.0017
##    160        0.7565             nan     0.2000   -0.0020
##    180        0.7445             nan     0.2000   -0.0017
##    200        0.7374             nan     0.2000   -0.0023
##    220        0.7254             nan     0.2000   -0.0015
##    240        0.7135             nan     0.2000   -0.0026
##    260        0.7031             nan     0.2000   -0.0026
##    280        0.6981             nan     0.2000   -0.0006
##    300        0.6894             nan     0.2000   -0.0015
##    320        0.6820             nan     0.2000   -0.0014
##    340        0.6722             nan     0.2000   -0.0020
##    360        0.6668             nan     0.2000   -0.0011
##    380        0.6624             nan     0.2000   -0.0015
##    400        0.6549             nan     0.2000   -0.0021
##    420        0.6485             nan     0.2000   -0.0019
##    440        0.6412             nan     0.2000   -0.0012
##    460        0.6344             nan     0.2000   -0.0011
##    480        0.6293             nan     0.2000   -0.0032
##    500        0.6209             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2374             nan     0.2000    0.0299
##      2        1.1887             nan     0.2000    0.0211
##      3        1.1522             nan     0.2000    0.0141
##      4        1.1226             nan     0.2000    0.0115
##      5        1.1011             nan     0.2000    0.0080
##      6        1.0789             nan     0.2000    0.0091
##      7        1.0604             nan     0.2000    0.0075
##      8        1.0394             nan     0.2000    0.0083
##      9        1.0276             nan     0.2000    0.0043
##     10        1.0160             nan     0.2000    0.0034
##     20        0.9458             nan     0.2000   -0.0010
##     40        0.8745             nan     0.2000   -0.0007
##     60        0.8450             nan     0.2000   -0.0003
##     80        0.8230             nan     0.2000    0.0000
##    100        0.8070             nan     0.2000   -0.0005
##    120        0.7928             nan     0.2000   -0.0001
##    140        0.7803             nan     0.2000   -0.0008
##    160        0.7630             nan     0.2000   -0.0000
##    180        0.7533             nan     0.2000   -0.0022
##    200        0.7428             nan     0.2000   -0.0015
##    220        0.7336             nan     0.2000   -0.0017
##    240        0.7228             nan     0.2000   -0.0018
##    260        0.7140             nan     0.2000   -0.0008
##    280        0.7064             nan     0.2000   -0.0030
##    300        0.6992             nan     0.2000   -0.0011
##    320        0.6921             nan     0.2000    0.0001
##    340        0.6816             nan     0.2000   -0.0022
##    360        0.6784             nan     0.2000   -0.0023
##    380        0.6660             nan     0.2000   -0.0022
##    400        0.6584             nan     0.2000   -0.0015
##    420        0.6505             nan     0.2000   -0.0018
##    440        0.6456             nan     0.2000   -0.0014
##    460        0.6394             nan     0.2000   -0.0026
##    480        0.6334             nan     0.2000   -0.0023
##    500        0.6278             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2306             nan     0.2000    0.0298
##      2        1.1813             nan     0.2000    0.0240
##      3        1.1424             nan     0.2000    0.0127
##      4        1.1078             nan     0.2000    0.0090
##      5        1.0789             nan     0.2000    0.0091
##      6        1.0609             nan     0.2000    0.0069
##      7        1.0436             nan     0.2000    0.0073
##      8        1.0297             nan     0.2000    0.0038
##      9        1.0127             nan     0.2000    0.0069
##     10        1.0017             nan     0.2000    0.0024
##     20        0.9292             nan     0.2000    0.0007
##     40        0.8626             nan     0.2000   -0.0009
##     60        0.8345             nan     0.2000   -0.0019
##     80        0.8084             nan     0.2000   -0.0024
##    100        0.7892             nan     0.2000   -0.0013
##    120        0.7742             nan     0.2000   -0.0021
##    140        0.7634             nan     0.2000   -0.0048
##    160        0.7512             nan     0.2000   -0.0018
##    180        0.7415             nan     0.2000   -0.0022
##    200        0.7365             nan     0.2000   -0.0031
##    220        0.7279             nan     0.2000   -0.0012
##    240        0.7221             nan     0.2000   -0.0018
##    260        0.7097             nan     0.2000   -0.0014
##    280        0.7011             nan     0.2000   -0.0024
##    300        0.6912             nan     0.2000   -0.0021
##    320        0.6824             nan     0.2000   -0.0016
##    340        0.6767             nan     0.2000   -0.0016
##    360        0.6670             nan     0.2000   -0.0021
##    380        0.6596             nan     0.2000   -0.0016
##    400        0.6563             nan     0.2000   -0.0019
##    420        0.6499             nan     0.2000   -0.0026
##    440        0.6463             nan     0.2000   -0.0014
##    460        0.6384             nan     0.2000   -0.0021
##    480        0.6335             nan     0.2000   -0.0017
##    500        0.6297             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2121             nan     0.2000    0.0352
##      2        1.1502             nan     0.2000    0.0261
##      3        1.0973             nan     0.2000    0.0226
##      4        1.0669             nan     0.2000    0.0070
##      5        1.0395             nan     0.2000    0.0096
##      6        1.0107             nan     0.2000    0.0107
##      7        0.9901             nan     0.2000    0.0049
##      8        0.9723             nan     0.2000    0.0027
##      9        0.9542             nan     0.2000    0.0039
##     10        0.9439             nan     0.2000   -0.0020
##     20        0.8579             nan     0.2000    0.0034
##     40        0.7789             nan     0.2000   -0.0035
##     60        0.7223             nan     0.2000   -0.0008
##     80        0.6860             nan     0.2000   -0.0013
##    100        0.6493             nan     0.2000   -0.0006
##    120        0.6184             nan     0.2000   -0.0024
##    140        0.5825             nan     0.2000   -0.0013
##    160        0.5545             nan     0.2000   -0.0013
##    180        0.5304             nan     0.2000   -0.0008
##    200        0.5089             nan     0.2000   -0.0013
##    220        0.4872             nan     0.2000   -0.0017
##    240        0.4657             nan     0.2000   -0.0009
##    260        0.4387             nan     0.2000   -0.0003
##    280        0.4207             nan     0.2000   -0.0010
##    300        0.4031             nan     0.2000   -0.0019
##    320        0.3835             nan     0.2000   -0.0012
##    340        0.3672             nan     0.2000   -0.0006
##    360        0.3525             nan     0.2000   -0.0016
##    380        0.3356             nan     0.2000   -0.0014
##    400        0.3217             nan     0.2000   -0.0020
##    420        0.3095             nan     0.2000   -0.0009
##    440        0.2937             nan     0.2000   -0.0024
##    460        0.2820             nan     0.2000   -0.0013
##    480        0.2714             nan     0.2000   -0.0018
##    500        0.2618             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2064             nan     0.2000    0.0365
##      2        1.1441             nan     0.2000    0.0308
##      3        1.0974             nan     0.2000    0.0206
##      4        1.0578             nan     0.2000    0.0167
##      5        1.0292             nan     0.2000    0.0099
##      6        1.0115             nan     0.2000    0.0060
##      7        0.9936             nan     0.2000    0.0046
##      8        0.9748             nan     0.2000    0.0063
##      9        0.9582             nan     0.2000    0.0006
##     10        0.9401             nan     0.2000    0.0059
##     20        0.8666             nan     0.2000   -0.0033
##     40        0.7790             nan     0.2000   -0.0025
##     60        0.7263             nan     0.2000   -0.0022
##     80        0.6904             nan     0.2000   -0.0044
##    100        0.6434             nan     0.2000   -0.0003
##    120        0.6058             nan     0.2000   -0.0038
##    140        0.5731             nan     0.2000   -0.0007
##    160        0.5513             nan     0.2000   -0.0035
##    180        0.5248             nan     0.2000   -0.0005
##    200        0.5007             nan     0.2000   -0.0022
##    220        0.4792             nan     0.2000    0.0000
##    240        0.4629             nan     0.2000   -0.0005
##    260        0.4379             nan     0.2000   -0.0031
##    280        0.4166             nan     0.2000   -0.0022
##    300        0.3946             nan     0.2000   -0.0021
##    320        0.3798             nan     0.2000   -0.0012
##    340        0.3607             nan     0.2000   -0.0015
##    360        0.3447             nan     0.2000   -0.0005
##    380        0.3333             nan     0.2000   -0.0022
##    400        0.3205             nan     0.2000   -0.0017
##    420        0.3028             nan     0.2000   -0.0025
##    440        0.2922             nan     0.2000   -0.0012
##    460        0.2820             nan     0.2000   -0.0018
##    480        0.2696             nan     0.2000   -0.0002
##    500        0.2596             nan     0.2000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2146             nan     0.2000    0.0386
##      2        1.1569             nan     0.2000    0.0280
##      3        1.1107             nan     0.2000    0.0237
##      4        1.0737             nan     0.2000    0.0118
##      5        1.0405             nan     0.2000    0.0141
##      6        1.0223             nan     0.2000    0.0032
##      7        0.9994             nan     0.2000    0.0090
##      8        0.9818             nan     0.2000    0.0072
##      9        0.9671             nan     0.2000    0.0017
##     10        0.9480             nan     0.2000    0.0040
##     20        0.8579             nan     0.2000   -0.0018
##     40        0.7808             nan     0.2000    0.0007
##     60        0.7315             nan     0.2000   -0.0004
##     80        0.6834             nan     0.2000   -0.0057
##    100        0.6452             nan     0.2000   -0.0022
##    120        0.6041             nan     0.2000   -0.0020
##    140        0.5796             nan     0.2000   -0.0018
##    160        0.5537             nan     0.2000   -0.0024
##    180        0.5295             nan     0.2000   -0.0016
##    200        0.5089             nan     0.2000   -0.0048
##    220        0.4889             nan     0.2000   -0.0025
##    240        0.4728             nan     0.2000   -0.0009
##    260        0.4517             nan     0.2000   -0.0016
##    280        0.4319             nan     0.2000   -0.0013
##    300        0.4117             nan     0.2000   -0.0011
##    320        0.3955             nan     0.2000   -0.0007
##    340        0.3815             nan     0.2000   -0.0015
##    360        0.3688             nan     0.2000   -0.0010
##    380        0.3509             nan     0.2000   -0.0012
##    400        0.3371             nan     0.2000   -0.0018
##    420        0.3227             nan     0.2000   -0.0017
##    440        0.3094             nan     0.2000   -0.0014
##    460        0.2983             nan     0.2000   -0.0026
##    480        0.2895             nan     0.2000   -0.0006
##    500        0.2756             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1956             nan     0.2000    0.0471
##      2        1.1240             nan     0.2000    0.0338
##      3        1.0763             nan     0.2000    0.0149
##      4        1.0343             nan     0.2000    0.0126
##      5        1.0045             nan     0.2000    0.0090
##      6        0.9746             nan     0.2000    0.0080
##      7        0.9549             nan     0.2000    0.0035
##      8        0.9363             nan     0.2000    0.0045
##      9        0.9239             nan     0.2000    0.0032
##     10        0.9061             nan     0.2000    0.0019
##     20        0.8238             nan     0.2000   -0.0019
##     40        0.7162             nan     0.2000   -0.0022
##     60        0.6414             nan     0.2000   -0.0016
##     80        0.5848             nan     0.2000   -0.0023
##    100        0.5377             nan     0.2000   -0.0016
##    120        0.4745             nan     0.2000   -0.0088
##    140        0.4330             nan     0.2000   -0.0027
##    160        0.3994             nan     0.2000   -0.0019
##    180        0.3652             nan     0.2000   -0.0011
##    200        0.3416             nan     0.2000   -0.0022
##    220        0.3179             nan     0.2000   -0.0018
##    240        0.3003             nan     0.2000   -0.0016
##    260        0.2769             nan     0.2000   -0.0017
##    280        0.2573             nan     0.2000   -0.0013
##    300        0.2405             nan     0.2000   -0.0013
##    320        0.2268             nan     0.2000   -0.0017
##    340        0.2123             nan     0.2000   -0.0009
##    360        0.1973             nan     0.2000   -0.0015
##    380        0.1836             nan     0.2000   -0.0010
##    400        0.1714             nan     0.2000   -0.0003
##    420        0.1608             nan     0.2000   -0.0005
##    440        0.1521             nan     0.2000   -0.0012
##    460        0.1410             nan     0.2000   -0.0008
##    480        0.1329             nan     0.2000   -0.0006
##    500        0.1261             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2001             nan     0.2000    0.0316
##      2        1.1301             nan     0.2000    0.0324
##      3        1.0736             nan     0.2000    0.0173
##      4        1.0310             nan     0.2000    0.0172
##      5        0.9996             nan     0.2000    0.0091
##      6        0.9746             nan     0.2000    0.0054
##      7        0.9565             nan     0.2000    0.0033
##      8        0.9375             nan     0.2000    0.0070
##      9        0.9194             nan     0.2000    0.0073
##     10        0.9012             nan     0.2000    0.0021
##     20        0.8291             nan     0.2000   -0.0029
##     40        0.7227             nan     0.2000   -0.0041
##     60        0.6484             nan     0.2000   -0.0020
##     80        0.5830             nan     0.2000   -0.0020
##    100        0.5277             nan     0.2000   -0.0031
##    120        0.4827             nan     0.2000   -0.0049
##    140        0.4472             nan     0.2000   -0.0018
##    160        0.4103             nan     0.2000   -0.0005
##    180        0.3797             nan     0.2000   -0.0014
##    200        0.3488             nan     0.2000   -0.0007
##    220        0.3189             nan     0.2000   -0.0018
##    240        0.2970             nan     0.2000   -0.0024
##    260        0.2743             nan     0.2000   -0.0030
##    280        0.2554             nan     0.2000   -0.0010
##    300        0.2362             nan     0.2000   -0.0007
##    320        0.2206             nan     0.2000   -0.0008
##    340        0.2076             nan     0.2000   -0.0015
##    360        0.1924             nan     0.2000   -0.0010
##    380        0.1796             nan     0.2000   -0.0011
##    400        0.1688             nan     0.2000   -0.0010
##    420        0.1570             nan     0.2000   -0.0006
##    440        0.1479             nan     0.2000   -0.0006
##    460        0.1395             nan     0.2000   -0.0009
##    480        0.1303             nan     0.2000   -0.0011
##    500        0.1228             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2015             nan     0.2000    0.0419
##      2        1.1294             nan     0.2000    0.0305
##      3        1.0692             nan     0.2000    0.0213
##      4        1.0317             nan     0.2000    0.0117
##      5        1.0055             nan     0.2000    0.0062
##      6        0.9787             nan     0.2000    0.0088
##      7        0.9594             nan     0.2000    0.0048
##      8        0.9370             nan     0.2000    0.0072
##      9        0.9215             nan     0.2000    0.0035
##     10        0.9053             nan     0.2000    0.0049
##     20        0.8178             nan     0.2000   -0.0072
##     40        0.7118             nan     0.2000   -0.0030
##     60        0.6367             nan     0.2000   -0.0035
##     80        0.5663             nan     0.2000   -0.0063
##    100        0.5147             nan     0.2000   -0.0014
##    120        0.4766             nan     0.2000   -0.0029
##    140        0.4461             nan     0.2000   -0.0036
##    160        0.4087             nan     0.2000   -0.0019
##    180        0.3771             nan     0.2000   -0.0019
##    200        0.3486             nan     0.2000   -0.0021
##    220        0.3233             nan     0.2000   -0.0025
##    240        0.2991             nan     0.2000   -0.0015
##    260        0.2793             nan     0.2000   -0.0016
##    280        0.2625             nan     0.2000   -0.0014
##    300        0.2436             nan     0.2000   -0.0010
##    320        0.2275             nan     0.2000   -0.0008
##    340        0.2156             nan     0.2000   -0.0012
##    360        0.2007             nan     0.2000   -0.0005
##    380        0.1882             nan     0.2000   -0.0014
##    400        0.1785             nan     0.2000   -0.0015
##    420        0.1690             nan     0.2000   -0.0013
##    440        0.1579             nan     0.2000   -0.0004
##    460        0.1494             nan     0.2000   -0.0014
##    480        0.1401             nan     0.2000   -0.0011
##    500        0.1320             nan     0.2000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2114             nan     0.3000    0.0425
##      2        1.1466             nan     0.3000    0.0276
##      3        1.1099             nan     0.3000    0.0142
##      4        1.0666             nan     0.3000    0.0160
##      5        1.0431             nan     0.3000    0.0085
##      6        1.0234             nan     0.3000    0.0059
##      7        0.9996             nan     0.3000    0.0129
##      8        0.9860             nan     0.3000    0.0030
##      9        0.9764             nan     0.3000    0.0007
##     10        0.9676             nan     0.3000    0.0022
##     20        0.8969             nan     0.3000   -0.0082
##     40        0.8354             nan     0.3000   -0.0031
##     60        0.7987             nan     0.3000   -0.0001
##     80        0.7698             nan     0.3000   -0.0030
##    100        0.7524             nan     0.3000   -0.0069
##    120        0.7283             nan     0.3000   -0.0009
##    140        0.7184             nan     0.3000   -0.0013
##    160        0.7061             nan     0.3000   -0.0046
##    180        0.6918             nan     0.3000   -0.0029
##    200        0.6806             nan     0.3000   -0.0034
##    220        0.6689             nan     0.3000   -0.0057
##    240        0.6627             nan     0.3000   -0.0018
##    260        0.6495             nan     0.3000   -0.0034
##    280        0.6402             nan     0.3000   -0.0034
##    300        0.6322             nan     0.3000   -0.0031
##    320        0.6226             nan     0.3000   -0.0013
##    340        0.6174             nan     0.3000    0.0003
##    360        0.6078             nan     0.3000   -0.0025
##    380        0.6017             nan     0.3000   -0.0019
##    400        0.5911             nan     0.3000   -0.0026
##    420        0.5818             nan     0.3000   -0.0015
##    440        0.5747             nan     0.3000   -0.0033
##    460        0.5650             nan     0.3000   -0.0031
##    480        0.5588             nan     0.3000   -0.0037
##    500        0.5526             nan     0.3000   -0.0028
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2098             nan     0.3000    0.0506
##      2        1.1391             nan     0.3000    0.0308
##      3        1.0993             nan     0.3000    0.0169
##      4        1.0666             nan     0.3000    0.0122
##      5        1.0417             nan     0.3000    0.0098
##      6        1.0143             nan     0.3000    0.0097
##      7        0.9925             nan     0.3000    0.0049
##      8        0.9775             nan     0.3000    0.0047
##      9        0.9661             nan     0.3000    0.0005
##     10        0.9512             nan     0.3000    0.0033
##     20        0.8828             nan     0.3000    0.0014
##     40        0.8326             nan     0.3000   -0.0010
##     60        0.8009             nan     0.3000   -0.0006
##     80        0.7763             nan     0.3000   -0.0007
##    100        0.7554             nan     0.3000   -0.0025
##    120        0.7401             nan     0.3000   -0.0047
##    140        0.7172             nan     0.3000   -0.0019
##    160        0.7007             nan     0.3000   -0.0031
##    180        0.6863             nan     0.3000   -0.0040
##    200        0.6771             nan     0.3000   -0.0044
##    220        0.6632             nan     0.3000   -0.0044
##    240        0.6534             nan     0.3000   -0.0050
##    260        0.6377             nan     0.3000   -0.0015
##    280        0.6273             nan     0.3000    0.0001
##    300        0.6201             nan     0.3000   -0.0012
##    320        0.6141             nan     0.3000   -0.0025
##    340        0.6103             nan     0.3000   -0.0029
##    360        0.6064             nan     0.3000   -0.0074
##    380        0.5954             nan     0.3000   -0.0020
##    400        0.5884             nan     0.3000   -0.0027
##    420        0.5831             nan     0.3000   -0.0040
##    440        0.5735             nan     0.3000   -0.0016
##    460        0.5712             nan     0.3000   -0.0021
##    480        0.5637             nan     0.3000   -0.0009
##    500        0.5607             nan     0.3000   -0.0043
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1992             nan     0.3000    0.0392
##      2        1.1471             nan     0.3000    0.0145
##      3        1.0963             nan     0.3000    0.0227
##      4        1.0640             nan     0.3000    0.0075
##      5        1.0382             nan     0.3000    0.0078
##      6        1.0133             nan     0.3000    0.0121
##      7        0.9924             nan     0.3000    0.0060
##      8        0.9752             nan     0.3000    0.0052
##      9        0.9660             nan     0.3000    0.0026
##     10        0.9522             nan     0.3000    0.0007
##     20        0.8903             nan     0.3000   -0.0012
##     40        0.8432             nan     0.3000   -0.0010
##     60        0.8153             nan     0.3000   -0.0001
##     80        0.7933             nan     0.3000    0.0002
##    100        0.7746             nan     0.3000   -0.0019
##    120        0.7559             nan     0.3000    0.0005
##    140        0.7404             nan     0.3000   -0.0031
##    160        0.7227             nan     0.3000   -0.0031
##    180        0.7096             nan     0.3000   -0.0040
##    200        0.7012             nan     0.3000   -0.0017
##    220        0.6919             nan     0.3000   -0.0020
##    240        0.6839             nan     0.3000   -0.0043
##    260        0.6687             nan     0.3000   -0.0028
##    280        0.6585             nan     0.3000   -0.0034
##    300        0.6495             nan     0.3000   -0.0044
##    320        0.6437             nan     0.3000   -0.0026
##    340        0.6344             nan     0.3000   -0.0033
##    360        0.6236             nan     0.3000   -0.0028
##    380        0.6098             nan     0.3000   -0.0030
##    400        0.6028             nan     0.3000   -0.0041
##    420        0.5977             nan     0.3000   -0.0058
##    440        0.5928             nan     0.3000   -0.0013
##    460        0.5899             nan     0.3000   -0.0067
##    480        0.5837             nan     0.3000   -0.0005
##    500        0.5771             nan     0.3000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1791             nan     0.3000    0.0485
##      2        1.0958             nan     0.3000    0.0383
##      3        1.0528             nan     0.3000    0.0128
##      4        1.0176             nan     0.3000    0.0127
##      5        0.9877             nan     0.3000    0.0061
##      6        0.9656             nan     0.3000    0.0034
##      7        0.9493             nan     0.3000    0.0036
##      8        0.9325             nan     0.3000   -0.0025
##      9        0.9206             nan     0.3000   -0.0003
##     10        0.9064             nan     0.3000   -0.0026
##     20        0.8275             nan     0.3000    0.0017
##     40        0.7253             nan     0.3000   -0.0026
##     60        0.6719             nan     0.3000   -0.0001
##     80        0.6080             nan     0.3000   -0.0008
##    100        0.5747             nan     0.3000   -0.0056
##    120        0.5325             nan     0.3000   -0.0015
##    140        0.4960             nan     0.3000   -0.0016
##    160        0.4564             nan     0.3000   -0.0020
##    180        0.4150             nan     0.3000   -0.0013
##    200        0.3940             nan     0.3000   -0.0030
##    220        0.3669             nan     0.3000   -0.0018
##    240        0.3444             nan     0.3000   -0.0020
##    260        0.3148             nan     0.3000   -0.0017
##    280        0.2967             nan     0.3000   -0.0012
##    300        0.2854             nan     0.3000   -0.0017
##    320        0.2721             nan     0.3000   -0.0028
##    340        0.2569             nan     0.3000   -0.0007
##    360        0.2444             nan     0.3000   -0.0024
##    380        0.2315             nan     0.3000   -0.0026
##    400        0.2189             nan     0.3000   -0.0014
##    420        0.2088             nan     0.3000   -0.0024
##    440        0.1995             nan     0.3000   -0.0019
##    460        0.1894             nan     0.3000   -0.0013
##    480        0.1818             nan     0.3000   -0.0021
##    500        0.1724             nan     0.3000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1738             nan     0.3000    0.0500
##      2        1.1077             nan     0.3000    0.0179
##      3        1.0631             nan     0.3000    0.0207
##      4        1.0171             nan     0.3000    0.0096
##      5        0.9903             nan     0.3000    0.0044
##      6        0.9577             nan     0.3000    0.0107
##      7        0.9394             nan     0.3000    0.0016
##      8        0.9344             nan     0.3000   -0.0052
##      9        0.9101             nan     0.3000    0.0058
##     10        0.9018             nan     0.3000   -0.0033
##     20        0.8240             nan     0.3000   -0.0040
##     40        0.7424             nan     0.3000   -0.0009
##     60        0.6720             nan     0.3000   -0.0041
##     80        0.6140             nan     0.3000   -0.0064
##    100        0.5589             nan     0.3000   -0.0052
##    120        0.5231             nan     0.3000   -0.0043
##    140        0.4879             nan     0.3000   -0.0010
##    160        0.4436             nan     0.3000   -0.0009
##    180        0.4151             nan     0.3000   -0.0040
##    200        0.3867             nan     0.3000   -0.0030
##    220        0.3670             nan     0.3000   -0.0049
##    240        0.3329             nan     0.3000   -0.0019
##    260        0.3139             nan     0.3000   -0.0023
##    280        0.2974             nan     0.3000   -0.0029
##    300        0.2745             nan     0.3000    0.0002
##    320        0.2571             nan     0.3000   -0.0002
##    340        0.2466             nan     0.3000   -0.0022
##    360        0.2320             nan     0.3000   -0.0016
##    380        0.2183             nan     0.3000   -0.0006
##    400        0.2062             nan     0.3000   -0.0006
##    420        0.1926             nan     0.3000   -0.0010
##    440        0.1822             nan     0.3000   -0.0011
##    460        0.1762             nan     0.3000   -0.0016
##    480        0.1658             nan     0.3000   -0.0012
##    500        0.1579             nan     0.3000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1707             nan     0.3000    0.0553
##      2        1.0951             nan     0.3000    0.0290
##      3        1.0460             nan     0.3000    0.0248
##      4        1.0043             nan     0.3000    0.0137
##      5        0.9858             nan     0.3000    0.0037
##      6        0.9662             nan     0.3000    0.0032
##      7        0.9490             nan     0.3000    0.0046
##      8        0.9306             nan     0.3000    0.0049
##      9        0.9146             nan     0.3000    0.0018
##     10        0.8964             nan     0.3000    0.0041
##     20        0.8233             nan     0.3000   -0.0026
##     40        0.7495             nan     0.3000   -0.0037
##     60        0.6973             nan     0.3000   -0.0024
##     80        0.6509             nan     0.3000   -0.0044
##    100        0.6072             nan     0.3000   -0.0076
##    120        0.5659             nan     0.3000   -0.0016
##    140        0.5295             nan     0.3000   -0.0059
##    160        0.5081             nan     0.3000   -0.0040
##    180        0.4623             nan     0.3000   -0.0029
##    200        0.4392             nan     0.3000   -0.0049
##    220        0.4084             nan     0.3000   -0.0052
##    240        0.3833             nan     0.3000   -0.0053
##    260        0.3615             nan     0.3000   -0.0021
##    280        0.3449             nan     0.3000   -0.0022
##    300        0.3188             nan     0.3000   -0.0010
##    320        0.2931             nan     0.3000   -0.0003
##    340        0.2740             nan     0.3000   -0.0024
##    360        0.2639             nan     0.3000   -0.0009
##    380        0.2537             nan     0.3000   -0.0017
##    400        0.2452             nan     0.3000   -0.0027
##    420        0.2289             nan     0.3000   -0.0011
##    440        0.2172             nan     0.3000   -0.0024
##    460        0.2065             nan     0.3000   -0.0005
##    480        0.1954             nan     0.3000   -0.0012
##    500        0.1870             nan     0.3000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1639             nan     0.3000    0.0544
##      2        1.0883             nan     0.3000    0.0278
##      3        1.0066             nan     0.3000    0.0360
##      4        0.9634             nan     0.3000    0.0149
##      5        0.9373             nan     0.3000    0.0055
##      6        0.9196             nan     0.3000   -0.0002
##      7        0.8948             nan     0.3000    0.0015
##      8        0.8781             nan     0.3000   -0.0004
##      9        0.8629             nan     0.3000   -0.0003
##     10        0.8537             nan     0.3000   -0.0066
##     20        0.7532             nan     0.3000   -0.0043
##     40        0.6191             nan     0.3000   -0.0076
##     60        0.5561             nan     0.3000   -0.0073
##     80        0.4870             nan     0.3000   -0.0020
##    100        0.4680             nan     0.3000   -0.0015
##    120           inf             nan     0.3000       nan
##    140           inf             nan     0.3000       nan
##    160           inf             nan     0.3000       nan
##    180           inf             nan     0.3000   -0.0005
##    200           inf             nan     0.3000   -0.0016
##    220           inf             nan     0.3000   -0.0014
##    240           inf             nan     0.3000       nan
##    260           inf             nan     0.3000   -0.0018
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000   -0.0012
##    340           inf             nan     0.3000   -0.0014
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000   -0.0007
##    440           inf             nan     0.3000   -0.0006
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1614             nan     0.3000    0.0617
##      2        1.0670             nan     0.3000    0.0443
##      3        1.0176             nan     0.3000    0.0107
##      4        0.9741             nan     0.3000    0.0140
##      5        0.9426             nan     0.3000    0.0073
##      6        0.9241             nan     0.3000   -0.0019
##      7        0.8996             nan     0.3000    0.0043
##      8        0.8844             nan     0.3000    0.0012
##      9        0.8780             nan     0.3000   -0.0050
##     10        0.8620             nan     0.3000   -0.0021
##     20        0.7731             nan     0.3000   -0.0052
##     40        0.6527             nan     0.3000   -0.0028
##     60        0.5643             nan     0.3000   -0.0105
##     80        0.4992             nan     0.3000   -0.0006
##    100        0.4467             nan     0.3000   -0.0013
##    120        0.4049             nan     0.3000   -0.0038
##    140        0.3657             nan     0.3000   -0.0007
##    160        0.3143             nan     0.3000   -0.0037
##    180        0.2826             nan     0.3000   -0.0055
##    200        0.2513             nan     0.3000   -0.0016
##    220        0.2280             nan     0.3000   -0.0038
##    240        0.2022             nan     0.3000   -0.0001
##    260        0.1791             nan     0.3000   -0.0029
##    280        0.1627             nan     0.3000   -0.0025
##    300        0.1504             nan     0.3000   -0.0015
##    320        0.1398             nan     0.3000   -0.0016
##    340        0.1243             nan     0.3000   -0.0012
##    360        0.1139             nan     0.3000   -0.0010
##    380        0.1056             nan     0.3000   -0.0005
##    400        0.0956             nan     0.3000   -0.0006
##    420        0.0893             nan     0.3000   -0.0008
##    440        0.0803             nan     0.3000   -0.0005
##    460        0.0729             nan     0.3000   -0.0004
##    480        0.0676             nan     0.3000   -0.0008
##    500        0.0617             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1515             nan     0.3000    0.0611
##      2        1.0820             nan     0.3000    0.0237
##      3        1.0420             nan     0.3000    0.0074
##      4        0.9868             nan     0.3000    0.0208
##      5        0.9442             nan     0.3000    0.0107
##      6        0.9179             nan     0.3000    0.0086
##      7        0.8927             nan     0.3000    0.0038
##      8        0.8781             nan     0.3000   -0.0079
##      9        0.8612             nan     0.3000    0.0041
##     10        0.8495             nan     0.3000   -0.0030
##     20        0.7710             nan     0.3000    0.0013
##     40        0.6611             nan     0.3000   -0.0005
##     60        0.5995             nan     0.3000   -0.0023
##     80        0.5275             nan     0.3000   -0.0047
##    100        0.4720             nan     0.3000   -0.0059
##    120        0.4161             nan     0.3000   -0.0032
##    140        0.3703             nan     0.3000   -0.0036
##    160        0.3274             nan     0.3000   -0.0013
##    180        0.2838             nan     0.3000   -0.0041
##    200        0.2490             nan     0.3000   -0.0015
##    220        0.2273             nan     0.3000   -0.0030
##    240        0.2010             nan     0.3000   -0.0018
##    260        0.1852             nan     0.3000   -0.0037
##    280        0.1662             nan     0.3000    0.0003
##    300        0.1480             nan     0.3000   -0.0008
##    320        0.1340             nan     0.3000   -0.0011
##    340        0.1203             nan     0.3000   -0.0013
##    360        0.1098             nan     0.3000   -0.0011
##    380        0.1018             nan     0.3000   -0.0009
##    400        0.0922             nan     0.3000   -0.0009
##    420        0.0837             nan     0.3000   -0.0014
##    440        0.0765             nan     0.3000   -0.0006
##    460        0.0695             nan     0.3000   -0.0004
##    480        0.0649             nan     0.3000   -0.0003
##    500        0.0597             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1648             nan     0.5000    0.0690
##      2        1.0886             nan     0.5000    0.0309
##      3        1.0341             nan     0.5000    0.0186
##      4        0.9935             nan     0.5000    0.0137
##      5        0.9771             nan     0.5000    0.0031
##      6        0.9553             nan     0.5000    0.0009
##      7        0.9339             nan     0.5000    0.0089
##      8        0.9233             nan     0.5000    0.0004
##      9        0.9219             nan     0.5000   -0.0086
##     10        0.9158             nan     0.5000   -0.0016
##     20        0.8739             nan     0.5000   -0.0069
##     40        0.8249             nan     0.5000   -0.0027
##     60        0.7890             nan     0.5000   -0.0095
##     80        0.7614             nan     0.5000   -0.0038
##    100        0.7335             nan     0.5000   -0.0040
##    120        0.7141             nan     0.5000   -0.0017
##    140        0.6959             nan     0.5000   -0.0075
##    160        0.6694             nan     0.5000   -0.0032
##    180        0.6569             nan     0.5000   -0.0076
##    200        0.6431             nan     0.5000   -0.0048
##    220        0.6313             nan     0.5000   -0.0066
##    240        0.6192             nan     0.5000   -0.0066
##    260        0.6054             nan     0.5000   -0.0027
##    280        0.5932             nan     0.5000   -0.0046
##    300        0.5840             nan     0.5000   -0.0037
##    320        0.5795             nan     0.5000   -0.0038
##    340        0.5641             nan     0.5000   -0.0053
##    360        0.5498             nan     0.5000   -0.0039
##    380        0.5444             nan     0.5000   -0.0025
##    400        0.5322             nan     0.5000   -0.0011
##    420        0.5246             nan     0.5000   -0.0041
##    440        0.5195             nan     0.5000   -0.0012
##    460        0.5064             nan     0.5000   -0.0011
##    480        0.4963             nan     0.5000   -0.0016
##    500        0.4941             nan     0.5000   -0.0041
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1728             nan     0.5000    0.0552
##      2        1.0987             nan     0.5000    0.0299
##      3        1.0566             nan     0.5000    0.0139
##      4        1.0181             nan     0.5000    0.0162
##      5        0.9764             nan     0.5000    0.0208
##      6        0.9687             nan     0.5000   -0.0070
##      7        0.9574             nan     0.5000   -0.0001
##      8        0.9396             nan     0.5000    0.0005
##      9        0.9388             nan     0.5000   -0.0094
##     10        0.9277             nan     0.5000   -0.0003
##     20        0.8559             nan     0.5000   -0.0005
##     40        0.7974             nan     0.5000   -0.0080
##     60        0.7704             nan     0.5000   -0.0027
##     80        0.7497             nan     0.5000   -0.0034
##    100        0.7326             nan     0.5000   -0.0090
##    120        0.7105             nan     0.5000   -0.0036
##    140        0.6897             nan     0.5000   -0.0061
##    160        0.6711             nan     0.5000   -0.0074
##    180        0.6521             nan     0.5000   -0.0087
##    200        0.6330             nan     0.5000   -0.0034
##    220        0.6283             nan     0.5000   -0.0139
##    240        0.6177             nan     0.5000   -0.0012
##    260        0.6094             nan     0.5000   -0.0074
##    280        0.5974             nan     0.5000   -0.0045
##    300        0.5793             nan     0.5000   -0.0005
##    320        0.5662             nan     0.5000   -0.0047
##    340        0.5578             nan     0.5000   -0.0046
##    360        0.5554             nan     0.5000   -0.0042
##    380        0.5399             nan     0.5000   -0.0028
##    400        0.5382             nan     0.5000   -0.0021
##    420        0.5333             nan     0.5000   -0.0055
##    440        0.5254             nan     0.5000   -0.0032
##    460        0.5183             nan     0.5000   -0.0029
##    480        0.5140             nan     0.5000   -0.0065
##    500        0.5058             nan     0.5000   -0.0041
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1503             nan     0.5000    0.0619
##      2        1.0854             nan     0.5000    0.0199
##      3        1.0342             nan     0.5000    0.0206
##      4        0.9927             nan     0.5000    0.0185
##      5        0.9737             nan     0.5000    0.0028
##      6        0.9515             nan     0.5000    0.0074
##      7        0.9360             nan     0.5000    0.0064
##      8        0.9241             nan     0.5000   -0.0019
##      9        0.9173             nan     0.5000   -0.0011
##     10        0.9115             nan     0.5000   -0.0009
##     20        0.8666             nan     0.5000   -0.0004
##     40        0.8163             nan     0.5000   -0.0027
##     60        0.7797             nan     0.5000   -0.0049
##     80        0.7494             nan     0.5000   -0.0007
##    100        0.7223             nan     0.5000   -0.0027
##    120        0.7075             nan     0.5000   -0.0053
##    140        0.6866             nan     0.5000   -0.0015
##    160        0.6845             nan     0.5000   -0.0127
##    180        0.6620             nan     0.5000   -0.0050
##    200        0.6413             nan     0.5000   -0.0045
##    220        0.6289             nan     0.5000   -0.0076
##    240        0.6097             nan     0.5000   -0.0052
##    260        0.5952             nan     0.5000   -0.0032
##    280        0.5857             nan     0.5000   -0.0072
##    300        0.5758             nan     0.5000   -0.0019
##    320        0.5627             nan     0.5000   -0.0072
##    340        0.5549             nan     0.5000   -0.0036
##    360        0.5430             nan     0.5000   -0.0033
##    380        0.5278             nan     0.5000   -0.0039
##    400        0.5235             nan     0.5000   -0.0034
##    420        0.5078             nan     0.5000   -0.0003
##    440        0.5022             nan     0.5000   -0.0015
##    460        0.5017             nan     0.5000   -0.0027
##    480        0.5020             nan     0.5000   -0.0070
##    500        0.4885             nan     0.5000   -0.0050
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1316             nan     0.5000    0.0713
##      2        1.0409             nan     0.5000    0.0305
##      3        0.9845             nan     0.5000    0.0118
##      4        0.9595             nan     0.5000    0.0011
##      5        0.9307             nan     0.5000    0.0028
##      6        0.9090             nan     0.5000    0.0030
##      7        0.8933             nan     0.5000   -0.0057
##      8        0.8791             nan     0.5000   -0.0005
##      9        0.8957             nan     0.5000   -0.0388
##     10        0.8775             nan     0.5000   -0.0023
##     20        0.7895             nan     0.5000   -0.0140
##     40        0.6676             nan     0.5000   -0.0034
##     60        0.6065             nan     0.5000    0.0007
##     80        0.5824             nan     0.5000   -0.0131
##    100        0.5366             nan     0.5000   -0.0012
##    120        1.1926             nan     0.5000   -0.0061
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1130             nan     0.5000    0.0831
##      2        1.0147             nan     0.5000    0.0293
##      3        0.9714             nan     0.5000    0.0033
##      4        0.9476             nan     0.5000   -0.0075
##      5        0.9162             nan     0.5000    0.0124
##      6        0.9095             nan     0.5000   -0.0045
##      7        0.8901             nan     0.5000    0.0046
##      8        0.8789             nan     0.5000   -0.0090
##      9        0.8623             nan     0.5000   -0.0121
##     10        0.8463             nan     0.5000   -0.0021
##     20        0.7806             nan     0.5000   -0.0114
##     40        0.6689             nan     0.5000   -0.0085
##     60        0.6116             nan     0.5000   -0.0037
##     80        0.5608             nan     0.5000   -0.0003
##    100        0.5148             nan     0.5000   -0.0094
##    120        0.4610             nan     0.5000   -0.0099
##    140        0.4216             nan     0.5000   -0.0052
##    160        0.3755             nan     0.5000   -0.0088
##    180        0.3274             nan     0.5000   -0.0067
##    200        0.3040             nan     0.5000   -0.0054
##    220        0.2841             nan     0.5000   -0.0061
##    240           inf             nan     0.5000      -inf
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1194             nan     0.5000    0.0885
##      2        1.0369             nan     0.5000    0.0326
##      3        0.9862             nan     0.5000    0.0185
##      4        0.9434             nan     0.5000    0.0152
##      5        0.9194             nan     0.5000    0.0009
##      6        0.9081             nan     0.5000   -0.0073
##      7        0.8908             nan     0.5000   -0.0086
##      8        0.8791             nan     0.5000   -0.0084
##      9        0.8657             nan     0.5000    0.0048
##     10        0.8622             nan     0.5000   -0.0138
##     20        0.7710             nan     0.5000    0.0001
##     40        0.6658             nan     0.5000   -0.0060
##     60        0.5841             nan     0.5000   -0.0056
##     80        0.5166             nan     0.5000   -0.0056
##    100        0.4580             nan     0.5000   -0.0115
##    120        0.4134             nan     0.5000   -0.0047
##    140        0.3576             nan     0.5000   -0.0041
##    160        0.3279             nan     0.5000   -0.0075
##    180        0.2979             nan     0.5000   -0.0048
##    200        0.2720             nan     0.5000   -0.0035
##    220        0.2439             nan     0.5000   -0.0017
##    240        0.2272             nan     0.5000   -0.0042
##    260        0.2097             nan     0.5000   -0.0046
##    280        0.1890             nan     0.5000   -0.0067
##    300        0.1663             nan     0.5000   -0.0013
##    320        0.1573             nan     0.5000   -0.0016
##    340        0.1460             nan     0.5000   -0.0015
##    360        0.1319             nan     0.5000   -0.0008
##    380        0.1181             nan     0.5000   -0.0008
##    400        0.1062             nan     0.5000   -0.0019
##    420        0.0971             nan     0.5000   -0.0010
##    440        0.0899             nan     0.5000   -0.0004
##    460        0.0834             nan     0.5000   -0.0009
##    480        0.0762             nan     0.5000   -0.0004
##    500        0.0724             nan     0.5000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1228             nan     0.5000    0.0686
##      2        1.0247             nan     0.5000    0.0362
##      3        0.9717             nan     0.5000    0.0051
##      4        0.9261             nan     0.5000    0.0078
##      5        0.8972             nan     0.5000   -0.0120
##      6        0.8749             nan     0.5000   -0.0007
##      7        0.8527             nan     0.5000   -0.0067
##      8        0.8479             nan     0.5000   -0.0120
##      9        0.8425             nan     0.5000   -0.0133
##     10        0.8210             nan     0.5000   -0.0005
##     20        0.7136             nan     0.5000   -0.0008
##     40       23.1725             nan     0.5000   -0.2983
##     60       26.6824             nan     0.5000   -0.0134
##     80       26.6617             nan     0.5000   -0.0335
##    100 1173151856648.1650             nan     0.5000   -0.0034
##    120 1173151856648.1345             nan     0.5000   -0.0001
##    140 1173151856648.1089             nan     0.5000   -0.0048
##    160 1173151856648.1216             nan     0.5000    0.0001
##    180 1173151855354.3801             nan     0.5000   -0.0025
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1007             nan     0.5000    0.0833
##      2        1.0010             nan     0.5000    0.0415
##      3        0.9526             nan     0.5000    0.0091
##      4        0.9248             nan     0.5000   -0.0005
##      5        0.9131             nan     0.5000   -0.0085
##      6        0.8876             nan     0.5000   -0.0042
##      7        0.8693             nan     0.5000   -0.0011
##      8        0.8573             nan     0.5000   -0.0083
##      9        0.8307             nan     0.5000   -0.0039
##     10        0.8237             nan     0.5000   -0.0122
##     20        0.7262             nan     0.5000   -0.0199
##     40        0.5879             nan     0.5000   -0.0075
##     60        0.4756             nan     0.5000   -0.0135
##     80        0.3895             nan     0.5000   -0.0014
##    100        0.3259             nan     0.5000   -0.0037
##    120        0.2773             nan     0.5000   -0.0089
##    140        0.2167             nan     0.5000   -0.0045
##    160        0.1641             nan     0.5000   -0.0005
##    180        0.1365             nan     0.5000   -0.0011
##    200        0.1183             nan     0.5000   -0.0008
##    220        0.1002             nan     0.5000   -0.0012
##    240        0.0868             nan     0.5000   -0.0005
##    260        0.0749             nan     0.5000    0.0002
##    280        0.0680             nan     0.5000   -0.0023
##    300        0.0585             nan     0.5000   -0.0009
##    320        0.0514             nan     0.5000   -0.0008
##    340        0.0450             nan     0.5000   -0.0011
##    360        0.0406             nan     0.5000   -0.0002
##    380        0.0356             nan     0.5000   -0.0003
##    400        0.0316             nan     0.5000   -0.0003
##    420        0.0279             nan     0.5000   -0.0003
##    440        0.0245             nan     0.5000   -0.0001
##    460        0.0219             nan     0.5000   -0.0004
##    480        0.0186             nan     0.5000   -0.0001
##    500        0.0172             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0730             nan     0.5000    0.0869
##      2        0.9772             nan     0.5000    0.0382
##      3        0.9294             nan     0.5000    0.0124
##      4        0.9052             nan     0.5000   -0.0048
##      5        0.8758             nan     0.5000   -0.0001
##      6        0.8567             nan     0.5000   -0.0036
##      7        0.8476             nan     0.5000   -0.0087
##      8        0.8413             nan     0.5000   -0.0102
##      9        0.8334             nan     0.5000   -0.0049
##     10        0.8093             nan     0.5000    0.0002
##     20        0.7016             nan     0.5000   -0.0048
##     40        0.5885             nan     0.5000   -0.0047
##     60        0.4818             nan     0.5000   -0.0106
##     80        0.4040             nan     0.5000   -0.0084
##    100        0.3307             nan     0.5000   -0.0059
##    120        0.2730             nan     0.5000   -0.0019
##    140        0.2214             nan     0.5000   -0.0029
##    160        0.2164             nan     0.5000   -0.0171
##    180        0.1609             nan     0.5000   -0.0028
##    200        0.1317             nan     0.5000   -0.0006
##    220        0.1134             nan     0.5000   -0.0007
##    240        0.0948             nan     0.5000   -0.0013
##    260        0.0825             nan     0.5000   -0.0003
##    280        0.0711             nan     0.5000   -0.0018
##    300        0.0623             nan     0.5000   -0.0004
##    320        0.0532             nan     0.5000   -0.0009
##    340        0.0460             nan     0.5000   -0.0002
##    360        0.0402             nan     0.5000   -0.0005
##    380        0.0349             nan     0.5000   -0.0004
##    400        0.0303             nan     0.5000    0.0001
##    420        0.0274             nan     0.5000   -0.0004
##    440        0.0242             nan     0.5000   -0.0002
##    460        0.0214             nan     0.5000   -0.0002
##    480        0.0194             nan     0.5000   -0.0002
##    500        0.0174             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1308             nan     1.0000    0.0460
##      2        1.0528             nan     1.0000    0.0306
##      3        1.0352             nan     1.0000   -0.0052
##      4        1.0055             nan     1.0000    0.0070
##      5        1.0128             nan     1.0000   -0.0317
##      6        1.0014             nan     1.0000   -0.0222
##      7        0.9820             nan     1.0000   -0.0127
##      8        0.9478             nan     1.0000    0.0144
##      9        0.9304             nan     1.0000    0.0049
##     10        0.9149             nan     1.0000    0.0033
##     20        0.9750             nan     1.0000   -0.0007
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1480             nan     1.0000    0.0590
##      2        1.0832             nan     1.0000    0.0324
##      3        1.0217             nan     1.0000    0.0234
##      4        0.9924             nan     1.0000    0.0070
##      5        0.9909             nan     1.0000   -0.0211
##      6        0.9804             nan     1.0000   -0.0097
##      7        0.9958             nan     1.0000   -0.0322
##      8        1.0032             nan     1.0000   -0.0259
##      9        1.0006             nan     1.0000   -0.0205
##     10        0.9985             nan     1.0000   -0.0162
##     20        0.9159             nan     1.0000    0.0116
##     40        3.8254             nan     1.0000   -0.0164
##     60        3.8086             nan     1.0000    0.0001
##     80  8628202.9085             nan     1.0000 -5743820.4699
##    100  8628202.8606             nan     1.0000    0.0038
##    120  8628202.8273             nan     1.0000   -0.0302
##    140  8628202.8023             nan     1.0000    0.0037
##    160  8628202.7985             nan     1.0000    0.0018
##    180  8628202.7487             nan     1.0000   -0.0010
##    200  8628202.7405             nan     1.0000   -0.0361
##    220  8628202.7393             nan     1.0000   -0.0014
##    240  8628208.4030             nan     1.0000   -0.0057
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1185             nan     1.0000    0.0740
##      2        1.0691             nan     1.0000    0.0094
##      3        1.0174             nan     1.0000    0.0024
##      4        0.9964             nan     1.0000    0.0026
##      5        0.9874             nan     1.0000   -0.0144
##      6        0.9835             nan     1.0000   -0.0212
##      7        0.9737             nan     1.0000   -0.0085
##      8        0.9580             nan     1.0000    0.0066
##      9        0.9603             nan     1.0000   -0.0135
##     10        0.9696             nan     1.0000   -0.0335
##     20        0.9359             nan     1.0000   -0.0512
##     40        0.8632             nan     1.0000    0.0008
##     60        0.8041             nan     1.0000   -0.0208
##     80        0.7984             nan     1.0000   -0.0229
##    100        0.7312             nan     1.0000   -0.0100
##    120        0.7273             nan     1.0000   -0.0338
##    140        0.6956             nan     1.0000   -0.0175
##    160        0.6773             nan     1.0000    0.0017
##    180        0.7876             nan     1.0000   -0.0153
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0590             nan     1.0000    0.0811
##      2        0.9905             nan     1.0000    0.0013
##      3        0.9500             nan     1.0000    0.0075
##      4        0.9618             nan     1.0000   -0.0267
##      5        0.9271             nan     1.0000   -0.0067
##      6        0.9003             nan     1.0000    0.0010
##      7        0.9030             nan     1.0000   -0.0248
##      8        0.9098             nan     1.0000   -0.0331
##      9        0.8959             nan     1.0000   -0.0089
##     10        0.8639             nan     1.0000    0.0045
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000   -0.0043
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0747             nan     1.0000    0.0649
##      2        1.0041             nan     1.0000    0.0088
##      3        0.9694             nan     1.0000   -0.0011
##      4        0.9480             nan     1.0000    0.0034
##      5        0.9505             nan     1.0000   -0.0319
##      6        0.9316             nan     1.0000   -0.0122
##      7        0.9370             nan     1.0000   -0.0368
##      8        0.9171             nan     1.0000   -0.0143
##      9        0.9164             nan     1.0000   -0.0175
##     10        0.9224             nan     1.0000   -0.0354
##     20        0.8828             nan     1.0000   -0.0172
##     40           inf             nan     1.0000      -inf
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000    0.1627
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0751             nan     1.0000    0.0781
##      2        0.9972             nan     1.0000   -0.0068
##      3        0.9766             nan     1.0000   -0.0152
##      4        0.9896             nan     1.0000   -0.0352
##      5        0.9874             nan     1.0000   -0.0319
##      6        0.9426             nan     1.0000    0.0105
##      7        0.9559             nan     1.0000   -0.0396
##      8        0.9524             nan     1.0000   -0.0305
##      9        1.0052             nan     1.0000   -0.0502
##     10        0.9876             nan     1.0000   -0.0194
##     20           inf             nan     1.0000      -inf
##     40 592575273.2485             nan     1.0000   -0.0322
##     60 592575273.1291             nan     1.0000   -0.0193
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000   -0.0252
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0755             nan     1.0000    0.0674
##      2        1.0177             nan     1.0000   -0.0010
##      3        0.9631             nan     1.0000   -0.0023
##      4        0.9460             nan     1.0000   -0.0140
##      5        0.9124             nan     1.0000   -0.0022
##      6        1.1324             nan     1.0000   -0.2553
##      7        1.1068             nan     1.0000   -0.0580
##      8        1.0576             nan     1.0000   -0.0067
##      9           inf             nan     1.0000      -inf
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000    0.0016
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000   -0.0102
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           nan             nan     1.0000       nan
##    200           nan             nan     1.0000       nan
##    220           nan             nan     1.0000       nan
##    240           nan             nan     1.0000       nan
##    260           nan             nan     1.0000       nan
##    280           nan             nan     1.0000       nan
##    300           nan             nan     1.0000       nan
##    320           nan             nan     1.0000       nan
##    340           nan             nan     1.0000       nan
##    360           nan             nan     1.0000       nan
##    380           nan             nan     1.0000       nan
##    400           nan             nan     1.0000       nan
##    420           nan             nan     1.0000       nan
##    440           nan             nan     1.0000       nan
##    460           nan             nan     1.0000       nan
##    480           nan             nan     1.0000       nan
##    500           nan             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0672             nan     1.0000    0.0637
##      2        1.0092             nan     1.0000    0.0037
##      3        0.9480             nan     1.0000    0.0060
##      4        0.9404             nan     1.0000   -0.0323
##      5        0.9308             nan     1.0000   -0.0343
##      6        0.9191             nan     1.0000   -0.0155
##      7        0.8707             nan     1.0000    0.0026
##      8        0.8586             nan     1.0000   -0.0198
##      9        0.8727             nan     1.0000   -0.0610
##     10        0.8743             nan     1.0000   -0.0521
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0541             nan     1.0000    0.1122
##      2        0.9829             nan     1.0000    0.0015
##      3        0.9586             nan     1.0000   -0.0171
##      4        0.9455             nan     1.0000   -0.0308
##      5        0.9682             nan     1.0000   -0.0760
##      6        0.9772             nan     1.0000   -0.0608
##      7        0.9780             nan     1.0000   -0.0443
##      8        0.9533             nan     1.0000   -0.0555
##      9        0.9298             nan     1.0000   -0.0224
##     10        0.9070             nan     1.0000   -0.0305
##     20        9.5833             nan     1.0000   -0.0307
##     40 1585163930813153811042442464088684406686.0000             nan     1.0000   -0.0396
##     60 1585163930813153811042442464088684406686.0000             nan     1.0000   -0.0157
##     80 1585163930813153811042442464088684406686.0000             nan     1.0000   -0.0139
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2945             nan     0.0000    0.0000
##      2        1.2945             nan     0.0000    0.0000
##      3        1.2945             nan     0.0000    0.0000
##      4        1.2945             nan     0.0000    0.0000
##      5        1.2945             nan     0.0000    0.0000
##      6        1.2945             nan     0.0000    0.0000
##      7        1.2945             nan     0.0000    0.0000
##      8        1.2945             nan     0.0000    0.0000
##      9        1.2945             nan     0.0000    0.0000
##     10        1.2945             nan     0.0000    0.0000
##     20        1.2945             nan     0.0000    0.0000
##     40        1.2945             nan     0.0000    0.0000
##     60        1.2945             nan     0.0000    0.0000
##     80        1.2945             nan     0.0000    0.0000
##    100        1.2945             nan     0.0000    0.0000
##    120        1.2945             nan     0.0000    0.0000
##    140        1.2945             nan     0.0000    0.0000
##    160        1.2945             nan     0.0000    0.0000
##    180        1.2945             nan     0.0000    0.0000
##    200        1.2945             nan     0.0000    0.0000
##    220        1.2945             nan     0.0000    0.0000
##    240        1.2945             nan     0.0000    0.0000
##    260        1.2945             nan     0.0000    0.0000
##    280        1.2945             nan     0.0000    0.0000
##    300        1.2945             nan     0.0000    0.0000
##    320        1.2945             nan     0.0000    0.0000
##    340        1.2945             nan     0.0000    0.0000
##    360        1.2945             nan     0.0000    0.0000
##    380        1.2945             nan     0.0000    0.0000
##    400        1.2945             nan     0.0000    0.0000
##    420        1.2945             nan     0.0000    0.0000
##    440        1.2945             nan     0.0000    0.0000
##    460        1.2945             nan     0.0000    0.0000
##    480        1.2945             nan     0.0000    0.0000
##    500        1.2945             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2942             nan     0.0010    0.0002
##      2        1.2937             nan     0.0010    0.0002
##      3        1.2933             nan     0.0010    0.0002
##      4        1.2929             nan     0.0010    0.0002
##      5        1.2926             nan     0.0010    0.0002
##      6        1.2921             nan     0.0010    0.0002
##      7        1.2918             nan     0.0010    0.0002
##      8        1.2914             nan     0.0010    0.0002
##      9        1.2910             nan     0.0010    0.0002
##     10        1.2906             nan     0.0010    0.0002
##     20        1.2867             nan     0.0010    0.0001
##     40        1.2794             nan     0.0010    0.0002
##     60        1.2722             nan     0.0010    0.0002
##     80        1.2656             nan     0.0010    0.0001
##    100        1.2590             nan     0.0010    0.0001
##    120        1.2525             nan     0.0010    0.0001
##    140        1.2462             nan     0.0010    0.0001
##    160        1.2402             nan     0.0010    0.0001
##    180        1.2344             nan     0.0010    0.0001
##    200        1.2288             nan     0.0010    0.0001
##    220        1.2234             nan     0.0010    0.0001
##    240        1.2181             nan     0.0010    0.0001
##    260        1.2130             nan     0.0010    0.0001
##    280        1.2081             nan     0.0010    0.0001
##    300        1.2033             nan     0.0010    0.0001
##    320        1.1985             nan     0.0010    0.0001
##    340        1.1940             nan     0.0010    0.0001
##    360        1.1894             nan     0.0010    0.0001
##    380        1.1850             nan     0.0010    0.0001
##    400        1.1807             nan     0.0010    0.0001
##    420        1.1767             nan     0.0010    0.0001
##    440        1.1728             nan     0.0010    0.0001
##    460        1.1689             nan     0.0010    0.0001
##    480        1.1651             nan     0.0010    0.0001
##    500        1.1613             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2937             nan     0.0010    0.0002
##      3        1.2933             nan     0.0010    0.0002
##      4        1.2930             nan     0.0010    0.0002
##      5        1.2926             nan     0.0010    0.0002
##      6        1.2922             nan     0.0010    0.0002
##      7        1.2919             nan     0.0010    0.0002
##      8        1.2915             nan     0.0010    0.0002
##      9        1.2911             nan     0.0010    0.0002
##     10        1.2908             nan     0.0010    0.0002
##     20        1.2870             nan     0.0010    0.0002
##     40        1.2795             nan     0.0010    0.0002
##     60        1.2724             nan     0.0010    0.0002
##     80        1.2654             nan     0.0010    0.0002
##    100        1.2588             nan     0.0010    0.0002
##    120        1.2523             nan     0.0010    0.0001
##    140        1.2461             nan     0.0010    0.0001
##    160        1.2401             nan     0.0010    0.0001
##    180        1.2341             nan     0.0010    0.0001
##    200        1.2286             nan     0.0010    0.0001
##    220        1.2231             nan     0.0010    0.0001
##    240        1.2176             nan     0.0010    0.0001
##    260        1.2123             nan     0.0010    0.0001
##    280        1.2072             nan     0.0010    0.0001
##    300        1.2025             nan     0.0010    0.0001
##    320        1.1979             nan     0.0010    0.0001
##    340        1.1933             nan     0.0010    0.0001
##    360        1.1888             nan     0.0010    0.0001
##    380        1.1845             nan     0.0010    0.0001
##    400        1.1805             nan     0.0010    0.0001
##    420        1.1764             nan     0.0010    0.0001
##    440        1.1723             nan     0.0010    0.0001
##    460        1.1684             nan     0.0010    0.0000
##    480        1.1647             nan     0.0010    0.0001
##    500        1.1611             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2937             nan     0.0010    0.0002
##      3        1.2933             nan     0.0010    0.0002
##      4        1.2929             nan     0.0010    0.0002
##      5        1.2925             nan     0.0010    0.0002
##      6        1.2921             nan     0.0010    0.0002
##      7        1.2917             nan     0.0010    0.0002
##      8        1.2914             nan     0.0010    0.0002
##      9        1.2909             nan     0.0010    0.0002
##     10        1.2905             nan     0.0010    0.0002
##     20        1.2869             nan     0.0010    0.0002
##     40        1.2795             nan     0.0010    0.0002
##     60        1.2723             nan     0.0010    0.0002
##     80        1.2654             nan     0.0010    0.0002
##    100        1.2585             nan     0.0010    0.0001
##    120        1.2521             nan     0.0010    0.0002
##    140        1.2460             nan     0.0010    0.0001
##    160        1.2402             nan     0.0010    0.0001
##    180        1.2343             nan     0.0010    0.0001
##    200        1.2287             nan     0.0010    0.0001
##    220        1.2232             nan     0.0010    0.0001
##    240        1.2179             nan     0.0010    0.0001
##    260        1.2128             nan     0.0010    0.0001
##    280        1.2079             nan     0.0010    0.0001
##    300        1.2031             nan     0.0010    0.0001
##    320        1.1983             nan     0.0010    0.0001
##    340        1.1940             nan     0.0010    0.0001
##    360        1.1893             nan     0.0010    0.0001
##    380        1.1849             nan     0.0010    0.0001
##    400        1.1807             nan     0.0010    0.0001
##    420        1.1766             nan     0.0010    0.0001
##    440        1.1727             nan     0.0010    0.0001
##    460        1.1687             nan     0.0010    0.0001
##    480        1.1649             nan     0.0010    0.0001
##    500        1.1612             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2921             nan     0.0010    0.0002
##      6        1.2916             nan     0.0010    0.0002
##      7        1.2911             nan     0.0010    0.0002
##      8        1.2907             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2851             nan     0.0010    0.0002
##     40        1.2757             nan     0.0010    0.0002
##     60        1.2666             nan     0.0010    0.0002
##     80        1.2577             nan     0.0010    0.0002
##    100        1.2493             nan     0.0010    0.0001
##    120        1.2408             nan     0.0010    0.0002
##    140        1.2329             nan     0.0010    0.0002
##    160        1.2253             nan     0.0010    0.0002
##    180        1.2178             nan     0.0010    0.0002
##    200        1.2106             nan     0.0010    0.0002
##    220        1.2034             nan     0.0010    0.0002
##    240        1.1964             nan     0.0010    0.0002
##    260        1.1898             nan     0.0010    0.0001
##    280        1.1832             nan     0.0010    0.0001
##    300        1.1769             nan     0.0010    0.0001
##    320        1.1707             nan     0.0010    0.0001
##    340        1.1647             nan     0.0010    0.0001
##    360        1.1588             nan     0.0010    0.0001
##    380        1.1531             nan     0.0010    0.0001
##    400        1.1475             nan     0.0010    0.0001
##    420        1.1420             nan     0.0010    0.0001
##    440        1.1366             nan     0.0010    0.0001
##    460        1.1312             nan     0.0010    0.0001
##    480        1.1261             nan     0.0010    0.0001
##    500        1.1210             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2941             nan     0.0010    0.0002
##      2        1.2936             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2927             nan     0.0010    0.0002
##      5        1.2922             nan     0.0010    0.0002
##      6        1.2917             nan     0.0010    0.0002
##      7        1.2912             nan     0.0010    0.0002
##      8        1.2908             nan     0.0010    0.0002
##      9        1.2904             nan     0.0010    0.0002
##     10        1.2900             nan     0.0010    0.0002
##     20        1.2852             nan     0.0010    0.0002
##     40        1.2756             nan     0.0010    0.0002
##     60        1.2665             nan     0.0010    0.0002
##     80        1.2578             nan     0.0010    0.0002
##    100        1.2492             nan     0.0010    0.0002
##    120        1.2409             nan     0.0010    0.0002
##    140        1.2329             nan     0.0010    0.0002
##    160        1.2248             nan     0.0010    0.0002
##    180        1.2172             nan     0.0010    0.0002
##    200        1.2098             nan     0.0010    0.0002
##    220        1.2027             nan     0.0010    0.0002
##    240        1.1958             nan     0.0010    0.0002
##    260        1.1891             nan     0.0010    0.0001
##    280        1.1826             nan     0.0010    0.0002
##    300        1.1763             nan     0.0010    0.0001
##    320        1.1703             nan     0.0010    0.0001
##    340        1.1643             nan     0.0010    0.0001
##    360        1.1586             nan     0.0010    0.0001
##    380        1.1527             nan     0.0010    0.0001
##    400        1.1474             nan     0.0010    0.0001
##    420        1.1419             nan     0.0010    0.0001
##    440        1.1365             nan     0.0010    0.0001
##    460        1.1315             nan     0.0010    0.0001
##    480        1.1265             nan     0.0010    0.0001
##    500        1.1215             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2940             nan     0.0010    0.0002
##      2        1.2935             nan     0.0010    0.0002
##      3        1.2931             nan     0.0010    0.0002
##      4        1.2926             nan     0.0010    0.0002
##      5        1.2921             nan     0.0010    0.0002
##      6        1.2915             nan     0.0010    0.0002
##      7        1.2911             nan     0.0010    0.0002
##      8        1.2906             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2848             nan     0.0010    0.0002
##     40        1.2754             nan     0.0010    0.0002
##     60        1.2661             nan     0.0010    0.0002
##     80        1.2572             nan     0.0010    0.0002
##    100        1.2487             nan     0.0010    0.0002
##    120        1.2407             nan     0.0010    0.0002
##    140        1.2325             nan     0.0010    0.0001
##    160        1.2247             nan     0.0010    0.0001
##    180        1.2173             nan     0.0010    0.0002
##    200        1.2100             nan     0.0010    0.0002
##    220        1.2028             nan     0.0010    0.0002
##    240        1.1958             nan     0.0010    0.0001
##    260        1.1892             nan     0.0010    0.0002
##    280        1.1826             nan     0.0010    0.0001
##    300        1.1763             nan     0.0010    0.0001
##    320        1.1703             nan     0.0010    0.0001
##    340        1.1642             nan     0.0010    0.0001
##    360        1.1585             nan     0.0010    0.0001
##    380        1.1527             nan     0.0010    0.0001
##    400        1.1473             nan     0.0010    0.0001
##    420        1.1418             nan     0.0010    0.0001
##    440        1.1364             nan     0.0010    0.0001
##    460        1.1314             nan     0.0010    0.0001
##    480        1.1265             nan     0.0010    0.0001
##    500        1.1216             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2939             nan     0.0010    0.0003
##      2        1.2933             nan     0.0010    0.0003
##      3        1.2927             nan     0.0010    0.0002
##      4        1.2922             nan     0.0010    0.0003
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2904             nan     0.0010    0.0003
##      8        1.2899             nan     0.0010    0.0002
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0003
##     20        1.2833             nan     0.0010    0.0002
##     40        1.2723             nan     0.0010    0.0002
##     60        1.2619             nan     0.0010    0.0002
##     80        1.2519             nan     0.0010    0.0002
##    100        1.2424             nan     0.0010    0.0002
##    120        1.2329             nan     0.0010    0.0002
##    140        1.2236             nan     0.0010    0.0002
##    160        1.2148             nan     0.0010    0.0002
##    180        1.2061             nan     0.0010    0.0002
##    200        1.1977             nan     0.0010    0.0002
##    220        1.1898             nan     0.0010    0.0001
##    240        1.1820             nan     0.0010    0.0002
##    260        1.1744             nan     0.0010    0.0002
##    280        1.1670             nan     0.0010    0.0002
##    300        1.1599             nan     0.0010    0.0002
##    320        1.1528             nan     0.0010    0.0001
##    340        1.1461             nan     0.0010    0.0001
##    360        1.1395             nan     0.0010    0.0001
##    380        1.1329             nan     0.0010    0.0002
##    400        1.1267             nan     0.0010    0.0001
##    420        1.1206             nan     0.0010    0.0001
##    440        1.1145             nan     0.0010    0.0001
##    460        1.1086             nan     0.0010    0.0001
##    480        1.1029             nan     0.0010    0.0001
##    500        1.0974             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2939             nan     0.0010    0.0002
##      2        1.2933             nan     0.0010    0.0002
##      3        1.2927             nan     0.0010    0.0003
##      4        1.2922             nan     0.0010    0.0003
##      5        1.2916             nan     0.0010    0.0003
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2905             nan     0.0010    0.0002
##      8        1.2899             nan     0.0010    0.0003
##      9        1.2893             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2834             nan     0.0010    0.0002
##     40        1.2725             nan     0.0010    0.0002
##     60        1.2623             nan     0.0010    0.0002
##     80        1.2522             nan     0.0010    0.0002
##    100        1.2421             nan     0.0010    0.0002
##    120        1.2326             nan     0.0010    0.0002
##    140        1.2235             nan     0.0010    0.0002
##    160        1.2145             nan     0.0010    0.0002
##    180        1.2059             nan     0.0010    0.0002
##    200        1.1977             nan     0.0010    0.0001
##    220        1.1900             nan     0.0010    0.0002
##    240        1.1820             nan     0.0010    0.0002
##    260        1.1743             nan     0.0010    0.0002
##    280        1.1670             nan     0.0010    0.0002
##    300        1.1599             nan     0.0010    0.0001
##    320        1.1530             nan     0.0010    0.0001
##    340        1.1463             nan     0.0010    0.0001
##    360        1.1398             nan     0.0010    0.0001
##    380        1.1336             nan     0.0010    0.0002
##    400        1.1274             nan     0.0010    0.0001
##    420        1.1213             nan     0.0010    0.0001
##    440        1.1152             nan     0.0010    0.0001
##    460        1.1095             nan     0.0010    0.0001
##    480        1.1039             nan     0.0010    0.0001
##    500        1.0985             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2939             nan     0.0010    0.0003
##      2        1.2933             nan     0.0010    0.0002
##      3        1.2927             nan     0.0010    0.0003
##      4        1.2922             nan     0.0010    0.0003
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2905             nan     0.0010    0.0002
##      8        1.2899             nan     0.0010    0.0003
##      9        1.2894             nan     0.0010    0.0003
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2834             nan     0.0010    0.0002
##     40        1.2726             nan     0.0010    0.0002
##     60        1.2621             nan     0.0010    0.0002
##     80        1.2522             nan     0.0010    0.0002
##    100        1.2425             nan     0.0010    0.0002
##    120        1.2330             nan     0.0010    0.0002
##    140        1.2239             nan     0.0010    0.0002
##    160        1.2152             nan     0.0010    0.0002
##    180        1.2066             nan     0.0010    0.0002
##    200        1.1984             nan     0.0010    0.0001
##    220        1.1904             nan     0.0010    0.0002
##    240        1.1824             nan     0.0010    0.0002
##    260        1.1748             nan     0.0010    0.0002
##    280        1.1673             nan     0.0010    0.0001
##    300        1.1601             nan     0.0010    0.0002
##    320        1.1530             nan     0.0010    0.0001
##    340        1.1460             nan     0.0010    0.0001
##    360        1.1391             nan     0.0010    0.0001
##    380        1.1327             nan     0.0010    0.0001
##    400        1.1265             nan     0.0010    0.0001
##    420        1.1203             nan     0.0010    0.0001
##    440        1.1142             nan     0.0010    0.0001
##    460        1.1084             nan     0.0010    0.0001
##    480        1.1029             nan     0.0010    0.0001
##    500        1.0971             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2557             nan     0.1000    0.0177
##      2        1.2248             nan     0.1000    0.0138
##      3        1.1999             nan     0.1000    0.0113
##      4        1.1795             nan     0.1000    0.0092
##      5        1.1571             nan     0.1000    0.0093
##      6        1.1413             nan     0.1000    0.0064
##      7        1.1239             nan     0.1000    0.0067
##      8        1.1075             nan     0.1000    0.0070
##      9        1.0950             nan     0.1000    0.0048
##     10        1.0806             nan     0.1000    0.0046
##     20        0.9947             nan     0.1000   -0.0005
##     40        0.9169             nan     0.1000   -0.0003
##     60        0.8759             nan     0.1000   -0.0007
##     80        0.8504             nan     0.1000   -0.0002
##    100        0.8307             nan     0.1000   -0.0004
##    120        0.8164             nan     0.1000   -0.0020
##    140        0.8056             nan     0.1000   -0.0006
##    160        0.7954             nan     0.1000   -0.0006
##    180        0.7880             nan     0.1000   -0.0007
##    200        0.7773             nan     0.1000   -0.0013
##    220        0.7687             nan     0.1000   -0.0022
##    240        0.7607             nan     0.1000   -0.0015
##    260        0.7540             nan     0.1000   -0.0003
##    280        0.7457             nan     0.1000   -0.0009
##    300        0.7388             nan     0.1000   -0.0019
##    320        0.7329             nan     0.1000   -0.0011
##    340        0.7279             nan     0.1000   -0.0011
##    360        0.7222             nan     0.1000   -0.0003
##    380        0.7172             nan     0.1000   -0.0008
##    400        0.7127             nan     0.1000   -0.0010
##    420        0.7088             nan     0.1000   -0.0010
##    440        0.7034             nan     0.1000   -0.0011
##    460        0.6984             nan     0.1000   -0.0006
##    480        0.6956             nan     0.1000   -0.0014
##    500        0.6915             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2549             nan     0.1000    0.0166
##      2        1.2268             nan     0.1000    0.0129
##      3        1.2021             nan     0.1000    0.0126
##      4        1.1817             nan     0.1000    0.0082
##      5        1.1645             nan     0.1000    0.0075
##      6        1.1473             nan     0.1000    0.0085
##      7        1.1306             nan     0.1000    0.0066
##      8        1.1148             nan     0.1000    0.0060
##      9        1.0986             nan     0.1000    0.0058
##     10        1.0862             nan     0.1000    0.0047
##     20        0.9975             nan     0.1000    0.0018
##     40        0.9141             nan     0.1000   -0.0006
##     60        0.8767             nan     0.1000   -0.0013
##     80        0.8535             nan     0.1000   -0.0012
##    100        0.8370             nan     0.1000   -0.0010
##    120        0.8246             nan     0.1000   -0.0005
##    140        0.8104             nan     0.1000   -0.0004
##    160        0.8009             nan     0.1000    0.0006
##    180        0.7914             nan     0.1000   -0.0006
##    200        0.7824             nan     0.1000   -0.0007
##    220        0.7740             nan     0.1000   -0.0009
##    240        0.7659             nan     0.1000   -0.0006
##    260        0.7595             nan     0.1000   -0.0009
##    280        0.7543             nan     0.1000   -0.0018
##    300        0.7502             nan     0.1000   -0.0019
##    320        0.7449             nan     0.1000   -0.0011
##    340        0.7400             nan     0.1000   -0.0022
##    360        0.7353             nan     0.1000   -0.0003
##    380        0.7304             nan     0.1000   -0.0009
##    400        0.7260             nan     0.1000   -0.0014
##    420        0.7197             nan     0.1000   -0.0004
##    440        0.7170             nan     0.1000   -0.0009
##    460        0.7144             nan     0.1000   -0.0010
##    480        0.7083             nan     0.1000   -0.0012
##    500        0.7052             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2554             nan     0.1000    0.0180
##      2        1.2282             nan     0.1000    0.0138
##      3        1.1991             nan     0.1000    0.0129
##      4        1.1757             nan     0.1000    0.0094
##      5        1.1576             nan     0.1000    0.0062
##      6        1.1385             nan     0.1000    0.0078
##      7        1.1219             nan     0.1000    0.0070
##      8        1.1083             nan     0.1000    0.0060
##      9        1.0919             nan     0.1000    0.0067
##     10        1.0811             nan     0.1000    0.0053
##     20        0.9943             nan     0.1000    0.0003
##     40        0.9107             nan     0.1000   -0.0007
##     60        0.8697             nan     0.1000   -0.0003
##     80        0.8457             nan     0.1000   -0.0010
##    100        0.8264             nan     0.1000   -0.0006
##    120        0.8147             nan     0.1000   -0.0014
##    140        0.8038             nan     0.1000   -0.0017
##    160        0.7933             nan     0.1000   -0.0005
##    180        0.7857             nan     0.1000   -0.0011
##    200        0.7775             nan     0.1000   -0.0005
##    220        0.7711             nan     0.1000   -0.0008
##    240        0.7639             nan     0.1000   -0.0011
##    260        0.7599             nan     0.1000   -0.0004
##    280        0.7562             nan     0.1000   -0.0011
##    300        0.7489             nan     0.1000   -0.0011
##    320        0.7442             nan     0.1000   -0.0007
##    340        0.7397             nan     0.1000   -0.0006
##    360        0.7344             nan     0.1000   -0.0010
##    380        0.7289             nan     0.1000   -0.0007
##    400        0.7258             nan     0.1000   -0.0010
##    420        0.7219             nan     0.1000   -0.0015
##    440        0.7146             nan     0.1000   -0.0006
##    460        0.7113             nan     0.1000   -0.0003
##    480        0.7070             nan     0.1000   -0.0010
##    500        0.7031             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2535             nan     0.1000    0.0153
##      2        1.2178             nan     0.1000    0.0191
##      3        1.1809             nan     0.1000    0.0158
##      4        1.1492             nan     0.1000    0.0134
##      5        1.1191             nan     0.1000    0.0125
##      6        1.0947             nan     0.1000    0.0088
##      7        1.0748             nan     0.1000    0.0081
##      8        1.0538             nan     0.1000    0.0084
##      9        1.0384             nan     0.1000    0.0049
##     10        1.0220             nan     0.1000    0.0064
##     20        0.9348             nan     0.1000    0.0001
##     40        0.8399             nan     0.1000   -0.0004
##     60        0.7908             nan     0.1000   -0.0009
##     80        0.7556             nan     0.1000   -0.0010
##    100        0.7289             nan     0.1000   -0.0015
##    120        0.7048             nan     0.1000    0.0001
##    140        0.6776             nan     0.1000   -0.0022
##    160        0.6612             nan     0.1000   -0.0005
##    180        0.6432             nan     0.1000   -0.0003
##    200        0.6243             nan     0.1000   -0.0009
##    220        0.6079             nan     0.1000   -0.0002
##    240        0.5922             nan     0.1000   -0.0001
##    260        0.5787             nan     0.1000   -0.0005
##    280        0.5607             nan     0.1000   -0.0011
##    300        0.5427             nan     0.1000   -0.0021
##    320        0.5259             nan     0.1000    0.0001
##    340        0.5155             nan     0.1000   -0.0009
##    360        0.5022             nan     0.1000   -0.0005
##    380        0.4903             nan     0.1000   -0.0003
##    400        0.4782             nan     0.1000   -0.0012
##    420        0.4674             nan     0.1000   -0.0009
##    440        0.4548             nan     0.1000   -0.0008
##    460        0.4438             nan     0.1000   -0.0015
##    480        0.4334             nan     0.1000   -0.0012
##    500        0.4243             nan     0.1000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2514             nan     0.1000    0.0221
##      2        1.2136             nan     0.1000    0.0179
##      3        1.1804             nan     0.1000    0.0157
##      4        1.1510             nan     0.1000    0.0142
##      5        1.1251             nan     0.1000    0.0095
##      6        1.1027             nan     0.1000    0.0096
##      7        1.0791             nan     0.1000    0.0077
##      8        1.0592             nan     0.1000    0.0055
##      9        1.0417             nan     0.1000    0.0059
##     10        1.0270             nan     0.1000    0.0069
##     20        0.9318             nan     0.1000    0.0000
##     40        0.8485             nan     0.1000    0.0003
##     60        0.8062             nan     0.1000   -0.0010
##     80        0.7762             nan     0.1000   -0.0017
##    100        0.7473             nan     0.1000   -0.0008
##    120        0.7255             nan     0.1000   -0.0013
##    140        0.6988             nan     0.1000   -0.0005
##    160        0.6790             nan     0.1000   -0.0009
##    180        0.6553             nan     0.1000   -0.0013
##    200        0.6365             nan     0.1000   -0.0015
##    220        0.6139             nan     0.1000   -0.0014
##    240        0.5939             nan     0.1000   -0.0009
##    260        0.5784             nan     0.1000   -0.0011
##    280        0.5621             nan     0.1000   -0.0022
##    300        0.5478             nan     0.1000   -0.0008
##    320        0.5330             nan     0.1000   -0.0004
##    340        0.5174             nan     0.1000   -0.0014
##    360        0.5084             nan     0.1000   -0.0013
##    380        0.4978             nan     0.1000   -0.0013
##    400        0.4864             nan     0.1000   -0.0015
##    420        0.4755             nan     0.1000   -0.0013
##    440        0.4637             nan     0.1000   -0.0009
##    460        0.4511             nan     0.1000   -0.0014
##    480        0.4454             nan     0.1000   -0.0005
##    500        0.4353             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2493             nan     0.1000    0.0220
##      2        1.2075             nan     0.1000    0.0150
##      3        1.1751             nan     0.1000    0.0131
##      4        1.1454             nan     0.1000    0.0133
##      5        1.1168             nan     0.1000    0.0114
##      6        1.0967             nan     0.1000    0.0068
##      7        1.0782             nan     0.1000    0.0072
##      8        1.0606             nan     0.1000    0.0052
##      9        1.0410             nan     0.1000    0.0079
##     10        1.0245             nan     0.1000    0.0050
##     20        0.9254             nan     0.1000    0.0007
##     40        0.8427             nan     0.1000   -0.0013
##     60        0.7994             nan     0.1000   -0.0026
##     80        0.7670             nan     0.1000   -0.0019
##    100        0.7412             nan     0.1000   -0.0007
##    120        0.7194             nan     0.1000   -0.0019
##    140        0.6901             nan     0.1000   -0.0021
##    160        0.6712             nan     0.1000   -0.0023
##    180        0.6507             nan     0.1000   -0.0009
##    200        0.6369             nan     0.1000   -0.0014
##    220        0.6190             nan     0.1000   -0.0011
##    240        0.5995             nan     0.1000   -0.0000
##    260        0.5823             nan     0.1000   -0.0014
##    280        0.5665             nan     0.1000   -0.0018
##    300        0.5514             nan     0.1000   -0.0009
##    320        0.5389             nan     0.1000   -0.0013
##    340        0.5212             nan     0.1000   -0.0005
##    360        0.5076             nan     0.1000   -0.0010
##    380        0.4950             nan     0.1000   -0.0003
##    400        0.4834             nan     0.1000   -0.0010
##    420        0.4756             nan     0.1000   -0.0011
##    440        0.4629             nan     0.1000   -0.0012
##    460        0.4530             nan     0.1000   -0.0012
##    480        0.4471             nan     0.1000   -0.0017
##    500        0.4358             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2439             nan     0.1000    0.0247
##      2        1.1943             nan     0.1000    0.0221
##      3        1.1600             nan     0.1000    0.0141
##      4        1.1220             nan     0.1000    0.0151
##      5        1.0920             nan     0.1000    0.0118
##      6        1.0666             nan     0.1000    0.0103
##      7        1.0482             nan     0.1000    0.0083
##      8        1.0311             nan     0.1000    0.0062
##      9        1.0099             nan     0.1000    0.0068
##     10        0.9947             nan     0.1000    0.0044
##     20        0.8922             nan     0.1000    0.0019
##     40        0.7982             nan     0.1000   -0.0003
##     60        0.7406             nan     0.1000    0.0000
##     80        0.6954             nan     0.1000   -0.0019
##    100        0.6574             nan     0.1000   -0.0003
##    120        0.6281             nan     0.1000   -0.0017
##    140        0.5967             nan     0.1000   -0.0008
##    160        0.5735             nan     0.1000   -0.0020
##    180        0.5385             nan     0.1000   -0.0005
##    200        0.5100             nan     0.1000   -0.0010
##    220        0.4867             nan     0.1000   -0.0005
##    240        0.4644             nan     0.1000   -0.0011
##    260        0.4450             nan     0.1000   -0.0017
##    280        0.4271             nan     0.1000   -0.0009
##    300        0.4096             nan     0.1000   -0.0008
##    320        0.3919             nan     0.1000   -0.0009
##    340        0.3746             nan     0.1000   -0.0010
##    360        0.3556             nan     0.1000   -0.0003
##    380        0.3387             nan     0.1000   -0.0004
##    400        0.3278             nan     0.1000   -0.0008
##    420        0.3150             nan     0.1000   -0.0007
##    440        0.3039             nan     0.1000   -0.0005
##    460        0.2934             nan     0.1000   -0.0009
##    480        0.2832             nan     0.1000   -0.0004
##    500        0.2717             nan     0.1000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2416             nan     0.1000    0.0255
##      2        1.1992             nan     0.1000    0.0192
##      3        1.1538             nan     0.1000    0.0194
##      4        1.1223             nan     0.1000    0.0119
##      5        1.0938             nan     0.1000    0.0112
##      6        1.0657             nan     0.1000    0.0108
##      7        1.0467             nan     0.1000    0.0057
##      8        1.0280             nan     0.1000    0.0054
##      9        1.0099             nan     0.1000    0.0068
##     10        0.9936             nan     0.1000    0.0061
##     20        0.8886             nan     0.1000    0.0016
##     40        0.7960             nan     0.1000   -0.0012
##     60        0.7384             nan     0.1000   -0.0004
##     80        0.6980             nan     0.1000   -0.0016
##    100        0.6526             nan     0.1000   -0.0014
##    120        0.6190             nan     0.1000   -0.0009
##    140        0.5877             nan     0.1000   -0.0012
##    160        0.5580             nan     0.1000   -0.0010
##    180        0.5276             nan     0.1000   -0.0003
##    200        0.5003             nan     0.1000   -0.0004
##    220        0.4779             nan     0.1000   -0.0004
##    240        0.4551             nan     0.1000   -0.0006
##    260        0.4423             nan     0.1000   -0.0010
##    280        0.4218             nan     0.1000   -0.0008
##    300        0.4043             nan     0.1000   -0.0012
##    320        0.3867             nan     0.1000   -0.0010
##    340        0.3726             nan     0.1000   -0.0007
##    360        0.3588             nan     0.1000   -0.0006
##    380        0.3464             nan     0.1000   -0.0012
##    400        0.3323             nan     0.1000   -0.0006
##    420        0.3190             nan     0.1000   -0.0001
##    440        0.3072             nan     0.1000   -0.0002
##    460        0.2968             nan     0.1000   -0.0003
##    480        0.2868             nan     0.1000   -0.0005
##    500        0.2777             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2376             nan     0.1000    0.0241
##      2        1.1904             nan     0.1000    0.0165
##      3        1.1548             nan     0.1000    0.0191
##      4        1.1268             nan     0.1000    0.0104
##      5        1.0984             nan     0.1000    0.0100
##      6        1.0760             nan     0.1000    0.0062
##      7        1.0526             nan     0.1000    0.0084
##      8        1.0267             nan     0.1000    0.0100
##      9        1.0110             nan     0.1000    0.0025
##     10        0.9957             nan     0.1000    0.0042
##     20        0.8915             nan     0.1000    0.0009
##     40        0.7966             nan     0.1000   -0.0012
##     60        0.7375             nan     0.1000   -0.0001
##     80        0.6907             nan     0.1000   -0.0022
##    100        0.6478             nan     0.1000   -0.0012
##    120        0.6136             nan     0.1000   -0.0012
##    140        0.5784             nan     0.1000   -0.0023
##    160        0.5502             nan     0.1000   -0.0007
##    180        0.5284             nan     0.1000   -0.0031
##    200        0.4969             nan     0.1000   -0.0008
##    220        0.4729             nan     0.1000   -0.0007
##    240        0.4519             nan     0.1000   -0.0006
##    260        0.4301             nan     0.1000   -0.0012
##    280        0.4119             nan     0.1000   -0.0010
##    300        0.3934             nan     0.1000   -0.0009
##    320        0.3781             nan     0.1000   -0.0012
##    340        0.3613             nan     0.1000   -0.0004
##    360        0.3459             nan     0.1000   -0.0007
##    380        0.3332             nan     0.1000   -0.0010
##    400        0.3201             nan     0.1000   -0.0007
##    420        0.3055             nan     0.1000   -0.0013
##    440        0.2915             nan     0.1000   -0.0009
##    460        0.2801             nan     0.1000   -0.0011
##    480        0.2726             nan     0.1000   -0.0006
##    500        0.2611             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2290             nan     0.2000    0.0355
##      2        1.1778             nan     0.2000    0.0198
##      3        1.1427             nan     0.2000    0.0136
##      4        1.1136             nan     0.2000    0.0104
##      5        1.0850             nan     0.2000    0.0093
##      6        1.0579             nan     0.2000    0.0126
##      7        1.0400             nan     0.2000    0.0076
##      8        1.0263             nan     0.2000    0.0026
##      9        1.0079             nan     0.2000    0.0076
##     10        0.9903             nan     0.2000    0.0052
##     20        0.9101             nan     0.2000    0.0004
##     40        0.8459             nan     0.2000   -0.0026
##     60        0.8192             nan     0.2000   -0.0011
##     80        0.7977             nan     0.2000   -0.0018
##    100        0.7817             nan     0.2000   -0.0019
##    120        0.7663             nan     0.2000   -0.0020
##    140        0.7538             nan     0.2000   -0.0017
##    160        0.7422             nan     0.2000   -0.0020
##    180        0.7315             nan     0.2000   -0.0022
##    200        0.7224             nan     0.2000   -0.0002
##    220        0.7157             nan     0.2000   -0.0023
##    240        0.7050             nan     0.2000   -0.0021
##    260        0.6965             nan     0.2000   -0.0032
##    280        0.6896             nan     0.2000   -0.0026
##    300        0.6792             nan     0.2000   -0.0017
##    320        0.6764             nan     0.2000   -0.0013
##    340        0.6642             nan     0.2000   -0.0004
##    360        0.6576             nan     0.2000   -0.0017
##    380        0.6554             nan     0.2000   -0.0023
##    400        0.6471             nan     0.2000   -0.0017
##    420        0.6429             nan     0.2000   -0.0010
##    440        0.6370             nan     0.2000   -0.0012
##    460        0.6309             nan     0.2000   -0.0022
##    480        0.6261             nan     0.2000   -0.0036
##    500        0.6218             nan     0.2000   -0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2119             nan     0.2000    0.0309
##      2        1.1666             nan     0.2000    0.0214
##      3        1.1332             nan     0.2000    0.0163
##      4        1.1045             nan     0.2000    0.0102
##      5        1.0789             nan     0.2000    0.0110
##      6        1.0598             nan     0.2000    0.0047
##      7        1.0347             nan     0.2000    0.0113
##      8        1.0132             nan     0.2000    0.0071
##      9        0.9983             nan     0.2000    0.0060
##     10        0.9884             nan     0.2000    0.0014
##     20        0.9090             nan     0.2000   -0.0002
##     40        0.8425             nan     0.2000   -0.0013
##     60        0.8146             nan     0.2000   -0.0046
##     80        0.7939             nan     0.2000   -0.0020
##    100        0.7800             nan     0.2000   -0.0002
##    120        0.7677             nan     0.2000   -0.0016
##    140        0.7558             nan     0.2000   -0.0014
##    160        0.7498             nan     0.2000   -0.0036
##    180        0.7383             nan     0.2000   -0.0025
##    200        0.7310             nan     0.2000   -0.0022
##    220        0.7199             nan     0.2000   -0.0024
##    240        0.7116             nan     0.2000   -0.0036
##    260        0.7011             nan     0.2000   -0.0012
##    280        0.6961             nan     0.2000   -0.0026
##    300        0.6901             nan     0.2000   -0.0017
##    320        0.6835             nan     0.2000   -0.0010
##    340        0.6759             nan     0.2000   -0.0018
##    360        0.6710             nan     0.2000   -0.0030
##    380        0.6615             nan     0.2000   -0.0019
##    400        0.6583             nan     0.2000   -0.0024
##    420        0.6518             nan     0.2000   -0.0013
##    440        0.6464             nan     0.2000   -0.0034
##    460        0.6401             nan     0.2000   -0.0019
##    480        0.6383             nan     0.2000   -0.0020
##    500        0.6317             nan     0.2000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2249             nan     0.2000    0.0337
##      2        1.1686             nan     0.2000    0.0226
##      3        1.1348             nan     0.2000    0.0134
##      4        1.1076             nan     0.2000    0.0135
##      5        1.0890             nan     0.2000    0.0061
##      6        1.0631             nan     0.2000    0.0107
##      7        1.0392             nan     0.2000    0.0087
##      8        1.0211             nan     0.2000    0.0080
##      9        1.0063             nan     0.2000    0.0073
##     10        0.9943             nan     0.2000    0.0040
##     20        0.9139             nan     0.2000   -0.0003
##     40        0.8475             nan     0.2000   -0.0006
##     60        0.8165             nan     0.2000   -0.0014
##     80        0.7992             nan     0.2000   -0.0043
##    100        0.7781             nan     0.2000   -0.0012
##    120        0.7691             nan     0.2000   -0.0012
##    140        0.7561             nan     0.2000   -0.0004
##    160        0.7455             nan     0.2000   -0.0030
##    180        0.7317             nan     0.2000   -0.0028
##    200        0.7204             nan     0.2000   -0.0025
##    220        0.7104             nan     0.2000   -0.0038
##    240        0.7070             nan     0.2000   -0.0013
##    260        0.6982             nan     0.2000   -0.0014
##    280        0.6918             nan     0.2000   -0.0020
##    300        0.6847             nan     0.2000   -0.0009
##    320        0.6799             nan     0.2000   -0.0034
##    340        0.6733             nan     0.2000   -0.0039
##    360        0.6638             nan     0.2000   -0.0015
##    380        0.6572             nan     0.2000   -0.0013
##    400        0.6514             nan     0.2000   -0.0025
##    420        0.6450             nan     0.2000   -0.0015
##    440        0.6417             nan     0.2000   -0.0009
##    460        0.6340             nan     0.2000   -0.0040
##    480        0.6306             nan     0.2000   -0.0025
##    500        0.6266             nan     0.2000   -0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2002             nan     0.2000    0.0372
##      2        1.1511             nan     0.2000    0.0206
##      3        1.1018             nan     0.2000    0.0250
##      4        1.0608             nan     0.2000    0.0137
##      5        1.0258             nan     0.2000    0.0164
##      6        1.0025             nan     0.2000    0.0063
##      7        0.9867             nan     0.2000    0.0011
##      8        0.9662             nan     0.2000    0.0037
##      9        0.9506             nan     0.2000    0.0019
##     10        0.9353             nan     0.2000    0.0051
##     20        0.8587             nan     0.2000   -0.0016
##     40        0.7827             nan     0.2000   -0.0029
##     60        0.7332             nan     0.2000    0.0000
##     80        0.6825             nan     0.2000   -0.0040
##    100        0.6458             nan     0.2000   -0.0022
##    120        0.6075             nan     0.2000   -0.0008
##    140        0.5646             nan     0.2000   -0.0026
##    160        0.5462             nan     0.2000   -0.0013
##    180        0.5207             nan     0.2000   -0.0002
##    200        0.4962             nan     0.2000   -0.0001
##    220        0.4775             nan     0.2000   -0.0013
##    240        0.4502             nan     0.2000   -0.0026
##    260        0.4283             nan     0.2000   -0.0009
##    280        0.4123             nan     0.2000   -0.0011
##    300        0.3961             nan     0.2000   -0.0017
##    320        0.3799             nan     0.2000   -0.0004
##    340        0.3629             nan     0.2000   -0.0010
##    360        0.3475             nan     0.2000   -0.0027
##    380        0.3353             nan     0.2000   -0.0013
##    400        0.3206             nan     0.2000   -0.0010
##    420        0.3109             nan     0.2000   -0.0022
##    440        0.2969             nan     0.2000   -0.0008
##    460        0.2852             nan     0.2000   -0.0014
##    480        0.2728             nan     0.2000   -0.0009
##    500        0.2645             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1960             nan     0.2000    0.0441
##      2        1.1392             nan     0.2000    0.0274
##      3        1.1004             nan     0.2000    0.0175
##      4        1.0623             nan     0.2000    0.0127
##      5        1.0224             nan     0.2000    0.0174
##      6        0.9959             nan     0.2000    0.0078
##      7        0.9744             nan     0.2000    0.0068
##      8        0.9513             nan     0.2000    0.0077
##      9        0.9423             nan     0.2000   -0.0004
##     10        0.9302             nan     0.2000    0.0011
##     20        0.8436             nan     0.2000   -0.0026
##     40        0.7740             nan     0.2000   -0.0036
##     60        0.7156             nan     0.2000   -0.0008
##     80        0.6696             nan     0.2000   -0.0008
##    100        0.6328             nan     0.2000   -0.0013
##    120        0.6007             nan     0.2000   -0.0037
##    140        0.5747             nan     0.2000   -0.0027
##    160        0.5466             nan     0.2000   -0.0010
##    180        0.5235             nan     0.2000   -0.0022
##    200        0.5004             nan     0.2000   -0.0036
##    220        0.4792             nan     0.2000   -0.0024
##    240        0.4534             nan     0.2000   -0.0004
##    260        0.4326             nan     0.2000   -0.0038
##    280        0.4178             nan     0.2000   -0.0028
##    300        0.3969             nan     0.2000   -0.0025
##    320        0.3752             nan     0.2000   -0.0020
##    340        0.3564             nan     0.2000   -0.0016
##    360        0.3416             nan     0.2000   -0.0015
##    380        0.3268             nan     0.2000   -0.0025
##    400        0.3118             nan     0.2000   -0.0022
##    420        0.2963             nan     0.2000   -0.0001
##    440        0.2852             nan     0.2000   -0.0021
##    460        0.2784             nan     0.2000   -0.0013
##    480        0.2647             nan     0.2000   -0.0009
##    500        0.2562             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2043             nan     0.2000    0.0419
##      2        1.1423             nan     0.2000    0.0294
##      3        1.0912             nan     0.2000    0.0203
##      4        1.0573             nan     0.2000    0.0123
##      5        1.0314             nan     0.2000    0.0088
##      6        1.0065             nan     0.2000    0.0084
##      7        0.9847             nan     0.2000    0.0056
##      8        0.9559             nan     0.2000    0.0117
##      9        0.9406             nan     0.2000    0.0042
##     10        0.9239             nan     0.2000    0.0037
##     20        0.8341             nan     0.2000    0.0001
##     40        0.7675             nan     0.2000   -0.0037
##     60        0.7238             nan     0.2000   -0.0014
##     80        0.6674             nan     0.2000   -0.0040
##    100        0.6294             nan     0.2000   -0.0013
##    120        0.5911             nan     0.2000   -0.0038
##    140        0.5596             nan     0.2000   -0.0013
##    160        0.5356             nan     0.2000   -0.0012
##    180        0.5048             nan     0.2000   -0.0017
##    200        0.4805             nan     0.2000   -0.0037
##    220        0.4592             nan     0.2000   -0.0024
##    240        0.4385             nan     0.2000   -0.0019
##    260        0.4201             nan     0.2000   -0.0023
##    280        0.4059             nan     0.2000   -0.0020
##    300        0.3873             nan     0.2000   -0.0002
##    320        0.3701             nan     0.2000   -0.0014
##    340        0.3555             nan     0.2000   -0.0022
##    360        0.3398             nan     0.2000   -0.0014
##    380        0.3265             nan     0.2000   -0.0025
##    400        0.3110             nan     0.2000   -0.0014
##    420        0.2996             nan     0.2000   -0.0007
##    440        0.2885             nan     0.2000   -0.0008
##    460        0.2778             nan     0.2000   -0.0007
##    480        0.2655             nan     0.2000   -0.0010
##    500        0.2545             nan     0.2000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1923             nan     0.2000    0.0440
##      2        1.1062             nan     0.2000    0.0312
##      3        1.0538             nan     0.2000    0.0184
##      4        1.0149             nan     0.2000    0.0128
##      5        0.9848             nan     0.2000    0.0104
##      6        0.9611             nan     0.2000    0.0061
##      7        0.9422             nan     0.2000    0.0063
##      8        0.9198             nan     0.2000    0.0051
##      9        0.9016             nan     0.2000    0.0038
##     10        0.8832             nan     0.2000    0.0037
##     20        0.8018             nan     0.2000    0.0001
##     40        0.7043             nan     0.2000    0.0011
##     60        0.6246             nan     0.2000   -0.0034
##     80        0.5596             nan     0.2000   -0.0011
##    100        0.5017             nan     0.2000   -0.0017
##    120        0.4570             nan     0.2000   -0.0009
##    140        0.4243             nan     0.2000   -0.0019
##    160        0.3922             nan     0.2000   -0.0012
##    180        0.3648             nan     0.2000   -0.0005
##    200        0.3373             nan     0.2000   -0.0016
##    220        0.3125             nan     0.2000   -0.0011
##    240        0.2922             nan     0.2000   -0.0003
##    260        0.2692             nan     0.2000   -0.0013
##    280        0.2500             nan     0.2000   -0.0012
##    300        0.2349             nan     0.2000   -0.0004
##    320        0.2155             nan     0.2000   -0.0002
##    340        0.2010             nan     0.2000   -0.0017
##    360        0.1882             nan     0.2000   -0.0007
##    380        0.1743             nan     0.2000   -0.0008
##    400        0.1639             nan     0.2000   -0.0009
##    420        0.1526             nan     0.2000   -0.0007
##    440        0.1456             nan     0.2000   -0.0004
##    460        0.1362             nan     0.2000   -0.0004
##    480        0.1280             nan     0.2000   -0.0006
##    500        0.1197             nan     0.2000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1941             nan     0.2000    0.0464
##      2        1.1227             nan     0.2000    0.0289
##      3        1.0700             nan     0.2000    0.0217
##      4        1.0320             nan     0.2000    0.0144
##      5        0.9913             nan     0.2000    0.0124
##      6        0.9703             nan     0.2000    0.0055
##      7        0.9462             nan     0.2000    0.0080
##      8        0.9222             nan     0.2000    0.0079
##      9        0.9033             nan     0.2000    0.0036
##     10        0.8791             nan     0.2000    0.0032
##     20        0.8036             nan     0.2000   -0.0036
##     40        0.7034             nan     0.2000   -0.0014
##     60        0.6374             nan     0.2000   -0.0044
##     80        0.5821             nan     0.2000   -0.0036
##    100        0.5230             nan     0.2000   -0.0027
##    120        0.4803             nan     0.2000   -0.0009
##    140        0.4378             nan     0.2000   -0.0009
##    160        0.4002             nan     0.2000   -0.0029
##    180        0.3686             nan     0.2000   -0.0027
##    200        0.3396             nan     0.2000   -0.0018
##    220        0.3154             nan     0.2000   -0.0013
##    240        0.2935             nan     0.2000   -0.0017
##    260        0.2727             nan     0.2000   -0.0020
##    280        0.2552             nan     0.2000   -0.0015
##    300        0.2354             nan     0.2000   -0.0010
##    320        0.2222             nan     0.2000   -0.0016
##    340        0.2061             nan     0.2000   -0.0004
##    360        0.1949             nan     0.2000   -0.0015
##    380        0.1806             nan     0.2000   -0.0009
##    400        0.1688             nan     0.2000   -0.0014
##    420        0.1575             nan     0.2000   -0.0006
##    440        0.1493             nan     0.2000   -0.0009
##    460        0.1384             nan     0.2000   -0.0008
##    480        0.1317             nan     0.2000   -0.0006
##    500        0.1249             nan     0.2000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1891             nan     0.2000    0.0475
##      2        1.1444             nan     0.2000    0.0131
##      3        1.0873             nan     0.2000    0.0220
##      4        1.0437             nan     0.2000    0.0167
##      5        1.0138             nan     0.2000    0.0097
##      6        0.9806             nan     0.2000    0.0104
##      7        0.9577             nan     0.2000    0.0042
##      8        0.9391             nan     0.2000   -0.0009
##      9        0.9211             nan     0.2000    0.0040
##     10        0.9128             nan     0.2000   -0.0037
##     20        0.8104             nan     0.2000   -0.0006
##     40        0.7139             nan     0.2000   -0.0038
##     60        0.6309             nan     0.2000   -0.0037
##     80        0.5671             nan     0.2000   -0.0053
##    100        0.5198             nan     0.2000   -0.0027
##    120        0.4734             nan     0.2000   -0.0014
##    140        0.4384             nan     0.2000   -0.0016
##    160        0.4013             nan     0.2000   -0.0014
##    180        0.3761             nan     0.2000   -0.0014
##    200        0.3527             nan     0.2000   -0.0030
##    220        0.3243             nan     0.2000   -0.0032
##    240        0.3003             nan     0.2000   -0.0017
##    260        0.2756             nan     0.2000   -0.0012
##    280        0.2561             nan     0.2000   -0.0010
##    300        0.2395             nan     0.2000   -0.0015
##    320        0.2235             nan     0.2000   -0.0014
##    340        0.2088             nan     0.2000   -0.0024
##    360        0.1950             nan     0.2000   -0.0009
##    380        0.1826             nan     0.2000   -0.0004
##    400        0.1704             nan     0.2000   -0.0013
##    420        0.1576             nan     0.2000   -0.0006
##    440        0.1478             nan     0.2000   -0.0007
##    460        0.1395             nan     0.2000   -0.0006
##    480        0.1322             nan     0.2000   -0.0005
##    500        0.1239             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1956             nan     0.3000    0.0457
##      2        1.1417             nan     0.3000    0.0290
##      3        1.0982             nan     0.3000    0.0165
##      4        1.0636             nan     0.3000    0.0147
##      5        1.0308             nan     0.3000    0.0129
##      6        1.0120             nan     0.3000    0.0078
##      7        0.9962             nan     0.3000    0.0014
##      8        0.9712             nan     0.3000    0.0061
##      9        0.9654             nan     0.3000   -0.0010
##     10        0.9477             nan     0.3000   -0.0004
##     20        0.8889             nan     0.3000   -0.0041
##     40        0.8250             nan     0.3000   -0.0010
##     60        0.7980             nan     0.3000   -0.0033
##     80        0.7732             nan     0.3000   -0.0055
##    100        0.7548             nan     0.3000   -0.0042
##    120        0.7411             nan     0.3000   -0.0022
##    140        0.7299             nan     0.3000   -0.0013
##    160        0.7170             nan     0.3000   -0.0054
##    180        0.7037             nan     0.3000   -0.0045
##    200        0.6892             nan     0.3000   -0.0028
##    220        0.6810             nan     0.3000   -0.0063
##    240        0.6747             nan     0.3000   -0.0020
##    260        0.6592             nan     0.3000   -0.0037
##    280        0.6485             nan     0.3000   -0.0017
##    300        0.6448             nan     0.3000   -0.0036
##    320        0.6414             nan     0.3000   -0.0034
##    340        0.6341             nan     0.3000   -0.0037
##    360        0.6283             nan     0.3000   -0.0036
##    380        0.6183             nan     0.3000   -0.0029
##    400        0.6077             nan     0.3000   -0.0036
##    420        0.5988             nan     0.3000   -0.0031
##    440        0.5891             nan     0.3000   -0.0034
##    460        0.5814             nan     0.3000   -0.0043
##    480        0.5789             nan     0.3000   -0.0043
##    500        0.5708             nan     0.3000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1940             nan     0.3000    0.0450
##      2        1.1411             nan     0.3000    0.0119
##      3        1.0882             nan     0.3000    0.0217
##      4        1.0543             nan     0.3000    0.0131
##      5        1.0329             nan     0.3000    0.0080
##      6        1.0028             nan     0.3000    0.0079
##      7        0.9951             nan     0.3000   -0.0015
##      8        0.9667             nan     0.3000    0.0129
##      9        0.9533             nan     0.3000    0.0009
##     10        0.9413             nan     0.3000    0.0058
##     20        0.8740             nan     0.3000    0.0016
##     40        0.8267             nan     0.3000   -0.0009
##     60        0.7927             nan     0.3000    0.0006
##     80        0.7740             nan     0.3000   -0.0019
##    100        0.7518             nan     0.3000   -0.0008
##    120        0.7429             nan     0.3000   -0.0047
##    140        0.7293             nan     0.3000   -0.0043
##    160        0.7151             nan     0.3000   -0.0019
##    180        0.7114             nan     0.3000   -0.0042
##    200        0.7019             nan     0.3000   -0.0047
##    220        0.6842             nan     0.3000   -0.0037
##    240        0.6744             nan     0.3000   -0.0059
##    260        0.6639             nan     0.3000   -0.0033
##    280        0.6577             nan     0.3000   -0.0021
##    300        0.6464             nan     0.3000   -0.0029
##    320        0.6325             nan     0.3000   -0.0004
##    340        0.6276             nan     0.3000   -0.0026
##    360        0.6214             nan     0.3000   -0.0023
##    380        0.6137             nan     0.3000   -0.0012
##    400        0.6085             nan     0.3000   -0.0041
##    420        0.5996             nan     0.3000   -0.0020
##    440        0.5984             nan     0.3000   -0.0033
##    460        0.5909             nan     0.3000   -0.0033
##    480        0.5867             nan     0.3000   -0.0030
##    500        0.5776             nan     0.3000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2035             nan     0.3000    0.0357
##      2        1.1386             nan     0.3000    0.0280
##      3        1.0942             nan     0.3000    0.0164
##      4        1.0582             nan     0.3000    0.0181
##      5        1.0275             nan     0.3000    0.0145
##      6        1.0036             nan     0.3000    0.0079
##      7        0.9801             nan     0.3000    0.0066
##      8        0.9692             nan     0.3000    0.0049
##      9        0.9559             nan     0.3000    0.0006
##     10        0.9422             nan     0.3000    0.0061
##     20        0.8704             nan     0.3000   -0.0040
##     40        0.8051             nan     0.3000   -0.0017
##     60        0.7884             nan     0.3000   -0.0006
##     80        0.7689             nan     0.3000   -0.0019
##    100        0.7458             nan     0.3000   -0.0027
##    120        0.7195             nan     0.3000   -0.0020
##    140        0.7094             nan     0.3000   -0.0044
##    160        0.6999             nan     0.3000   -0.0043
##    180        0.6878             nan     0.3000   -0.0025
##    200        0.6812             nan     0.3000   -0.0030
##    220        0.6718             nan     0.3000   -0.0005
##    240        0.6622             nan     0.3000   -0.0032
##    260        0.6578             nan     0.3000   -0.0028
##    280        0.6543             nan     0.3000   -0.0065
##    300        0.6445             nan     0.3000   -0.0023
##    320        0.6355             nan     0.3000   -0.0017
##    340        0.6293             nan     0.3000   -0.0029
##    360        0.6228             nan     0.3000   -0.0033
##    380        0.6184             nan     0.3000   -0.0025
##    400        0.6078             nan     0.3000   -0.0030
##    420        0.6041             nan     0.3000   -0.0013
##    440        0.5957             nan     0.3000   -0.0012
##    460        0.5826             nan     0.3000   -0.0016
##    480        0.5770             nan     0.3000   -0.0038
##    500        0.5741             nan     0.3000   -0.0023
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1686             nan     0.3000    0.0552
##      2        1.0876             nan     0.3000    0.0355
##      3        1.0338             nan     0.3000    0.0192
##      4        0.9924             nan     0.3000    0.0116
##      5        0.9585             nan     0.3000    0.0119
##      6        0.9421             nan     0.3000   -0.0014
##      7        0.9259             nan     0.3000   -0.0013
##      8        0.9059             nan     0.3000    0.0054
##      9        0.8893             nan     0.3000    0.0046
##     10        0.8776             nan     0.3000   -0.0005
##     20        0.8069             nan     0.3000   -0.0055
##     40        0.7342             nan     0.3000   -0.0115
##     60        0.6738             nan     0.3000   -0.0054
##     80        0.6167             nan     0.3000   -0.0073
##    100        0.5617             nan     0.3000   -0.0002
##    120        0.5292             nan     0.3000   -0.0028
##    140        0.4883             nan     0.3000   -0.0040
##    160        0.4478             nan     0.3000   -0.0004
##    180        0.4130             nan     0.3000   -0.0012
##    200        0.3798             nan     0.3000   -0.0010
##    220        0.3591             nan     0.3000    0.0003
##    240        0.3333             nan     0.3000   -0.0014
##    260        0.3175             nan     0.3000   -0.0019
##    280        0.2968             nan     0.3000   -0.0016
##    300        0.2804             nan     0.3000   -0.0016
##    320        0.2649             nan     0.3000   -0.0017
##    340        0.2469             nan     0.3000   -0.0011
##    360        0.2319             nan     0.3000   -0.0025
##    380        0.2174             nan     0.3000   -0.0006
##    400        0.2037             nan     0.3000   -0.0009
##    420        0.1901             nan     0.3000   -0.0010
##    440        0.1820             nan     0.3000   -0.0013
##    460        0.1734             nan     0.3000   -0.0010
##    480        0.1652             nan     0.3000   -0.0007
##    500        0.1560             nan     0.3000   -0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1648             nan     0.3000    0.0583
##      2        1.0964             nan     0.3000    0.0262
##      3        1.0409             nan     0.3000    0.0206
##      4        0.9892             nan     0.3000    0.0236
##      5        0.9622             nan     0.3000    0.0081
##      6        0.9466             nan     0.3000   -0.0021
##      7        0.9273             nan     0.3000    0.0025
##      8        0.9029             nan     0.3000    0.0068
##      9        0.8954             nan     0.3000   -0.0058
##     10        0.8852             nan     0.3000   -0.0027
##     20        0.8084             nan     0.3000   -0.0030
##     40        0.7376             nan     0.3000   -0.0042
##     60        0.6659             nan     0.3000   -0.0032
##     80        0.6169             nan     0.3000   -0.0038
##    100        0.5787             nan     0.3000   -0.0004
##    120        0.5381             nan     0.3000    0.0001
##    140        0.5039             nan     0.3000   -0.0017
##    160        0.4729             nan     0.3000   -0.0013
##    180        0.4436             nan     0.3000   -0.0006
##    200        0.4083             nan     0.3000   -0.0025
##    220        0.3824             nan     0.3000   -0.0019
##    240        0.3522             nan     0.3000   -0.0035
##    260        0.3364             nan     0.3000   -0.0011
##    280        0.3185             nan     0.3000   -0.0026
##    300        0.2981             nan     0.3000   -0.0021
##    320        0.2745             nan     0.3000   -0.0010
##    340        0.2595             nan     0.3000   -0.0009
##    360        0.2463             nan     0.3000   -0.0005
##    380        0.2351             nan     0.3000   -0.0024
##    400        0.2167             nan     0.3000   -0.0013
##    420        0.2047             nan     0.3000   -0.0019
##    440        0.1955             nan     0.3000   -0.0016
##    460        0.1839             nan     0.3000   -0.0013
##    480        0.1758             nan     0.3000   -0.0011
##    500        0.1656             nan     0.3000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1819             nan     0.3000    0.0587
##      2        1.1070             nan     0.3000    0.0319
##      3        1.0498             nan     0.3000    0.0207
##      4        1.0039             nan     0.3000    0.0141
##      5        0.9761             nan     0.3000    0.0033
##      6        0.9501             nan     0.3000    0.0086
##      7        0.9301             nan     0.3000    0.0065
##      8        0.9124             nan     0.3000    0.0026
##      9        0.8952             nan     0.3000    0.0052
##     10        0.8824             nan     0.3000   -0.0003
##     20        0.8159             nan     0.3000   -0.0013
##     40        0.7452             nan     0.3000   -0.0036
##     60        0.6688             nan     0.3000   -0.0003
##     80        0.6123             nan     0.3000   -0.0015
##    100        0.5634             nan     0.3000   -0.0069
##    120        0.5250             nan     0.3000   -0.0043
##    140        0.4898             nan     0.3000   -0.0039
##    160        0.4485             nan     0.3000   -0.0018
##    180        0.4295             nan     0.3000   -0.0050
##    200        0.3993             nan     0.3000   -0.0067
##    220        0.3705             nan     0.3000   -0.0007
##    240        0.3543             nan     0.3000   -0.0021
##    260        0.3408             nan     0.3000   -0.0018
##    280        0.3102             nan     0.3000   -0.0009
##    300        0.2854             nan     0.3000   -0.0024
##    320        0.2755             nan     0.3000   -0.0031
##    340        0.2613             nan     0.3000   -0.0019
##    360        0.2458             nan     0.3000   -0.0016
##    380        0.2342             nan     0.3000   -0.0011
##    400        0.2220             nan     0.3000   -0.0015
##    420        0.2121             nan     0.3000   -0.0024
##    440        0.1985             nan     0.3000   -0.0021
##    460        0.1882             nan     0.3000   -0.0021
##    480        0.1767             nan     0.3000   -0.0025
##    500        0.1660             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1449             nan     0.3000    0.0600
##      2        1.0439             nan     0.3000    0.0438
##      3        0.9956             nan     0.3000    0.0111
##      4        0.9574             nan     0.3000    0.0044
##      5        0.9313             nan     0.3000    0.0060
##      6        0.8996             nan     0.3000    0.0112
##      7        0.8854             nan     0.3000   -0.0019
##      8        0.8695             nan     0.3000   -0.0015
##      9        0.8571             nan     0.3000   -0.0022
##     10        0.8475             nan     0.3000   -0.0045
##     20        0.7469             nan     0.3000   -0.0061
##     40        0.6476             nan     0.3000   -0.0059
##     60        0.5655             nan     0.3000   -0.0051
##     80        0.4909             nan     0.3000   -0.0019
##    100        0.4275             nan     0.3000   -0.0011
##    120        0.3757             nan     0.3000   -0.0024
##    140        0.3412             nan     0.3000   -0.0032
##    160        0.2970             nan     0.3000    0.0003
##    180        0.2626             nan     0.3000   -0.0008
##    200        0.2390             nan     0.3000   -0.0016
##    220        0.2154             nan     0.3000   -0.0008
##    240        0.1922             nan     0.3000   -0.0024
##    260        0.1768             nan     0.3000   -0.0017
##    280        0.1663             nan     0.3000   -0.0012
##    300        0.1529             nan     0.3000   -0.0012
##    320        0.1386             nan     0.3000   -0.0014
##    340        0.1258             nan     0.3000   -0.0006
##    360        0.1150             nan     0.3000   -0.0012
##    380        0.1025             nan     0.3000   -0.0007
##    400        0.0960             nan     0.3000   -0.0005
##    420        0.0890             nan     0.3000   -0.0004
##    440        0.0832             nan     0.3000   -0.0013
##    460        0.0753             nan     0.3000   -0.0007
##    480        0.0688             nan     0.3000   -0.0003
##    500        0.0637             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1566             nan     0.3000    0.0445
##      2        1.0613             nan     0.3000    0.0356
##      3        1.0112             nan     0.3000    0.0174
##      4        0.9795             nan     0.3000    0.0054
##      5        0.9588             nan     0.3000   -0.0005
##      6        0.9350             nan     0.3000    0.0055
##      7        0.9103             nan     0.3000   -0.0016
##      8        0.8904             nan     0.3000    0.0055
##      9        0.8734             nan     0.3000   -0.0024
##     10        0.8641             nan     0.3000   -0.0068
##     20        0.7751             nan     0.3000   -0.0077
##     40        0.6394             nan     0.3000   -0.0023
##     60        0.5555             nan     0.3000   -0.0023
##     80        0.4899             nan     0.3000   -0.0066
##    100        0.4433             nan     0.3000   -0.0050
##    120        0.3858             nan     0.3000   -0.0031
##    140        0.3397             nan     0.3000   -0.0029
##    160        0.3013             nan     0.3000   -0.0033
##    180        0.2711             nan     0.3000   -0.0029
##    200        0.2398             nan     0.3000   -0.0045
##    220        0.2127             nan     0.3000   -0.0011
##    240        0.1927             nan     0.3000   -0.0012
##    260        0.1713             nan     0.3000   -0.0016
##    280        0.1544             nan     0.3000   -0.0013
##    300        0.1417             nan     0.3000   -0.0026
##    320        0.1292             nan     0.3000   -0.0012
##    340        0.1190             nan     0.3000   -0.0010
##    360        0.1081             nan     0.3000   -0.0011
##    380        0.0994             nan     0.3000   -0.0005
##    400        0.0928             nan     0.3000   -0.0006
##    420        0.0840             nan     0.3000   -0.0005
##    440        0.0773             nan     0.3000   -0.0006
##    460        0.0701             nan     0.3000   -0.0003
##    480        0.0650             nan     0.3000   -0.0006
##    500        0.0590             nan     0.3000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1565             nan     0.3000    0.0633
##      2        1.0805             nan     0.3000    0.0303
##      3        1.0195             nan     0.3000    0.0246
##      4        0.9706             nan     0.3000    0.0144
##      5        0.9352             nan     0.3000    0.0109
##      6        0.9048             nan     0.3000    0.0089
##      7        0.8751             nan     0.3000    0.0079
##      8        0.8614             nan     0.3000   -0.0048
##      9        0.8483             nan     0.3000   -0.0008
##     10        0.8455             nan     0.3000   -0.0086
##     20        0.7611             nan     0.3000   -0.0113
##     40        0.6389             nan     0.3000   -0.0070
##     60        0.5471             nan     0.3000   -0.0116
##     80        0.4790             nan     0.3000   -0.0042
##    100        0.4320             nan     0.3000   -0.0053
##    120        0.3837             nan     0.3000   -0.0041
##    140        0.3424             nan     0.3000   -0.0019
##    160        0.3070             nan     0.3000   -0.0023
##    180        0.2661             nan     0.3000   -0.0005
##    200        0.2488             nan     0.3000   -0.0035
##    220        0.2255             nan     0.3000   -0.0024
##    240        0.2039             nan     0.3000   -0.0013
##    260        0.1815             nan     0.3000   -0.0001
##    280        0.1660             nan     0.3000   -0.0001
##    300        0.1511             nan     0.3000   -0.0015
##    320        0.1377             nan     0.3000   -0.0017
##    340        0.1265             nan     0.3000   -0.0012
##    360        0.1153             nan     0.3000   -0.0008
##    380        0.1053             nan     0.3000   -0.0012
##    400        0.0938             nan     0.3000   -0.0010
##    420        0.0860             nan     0.3000   -0.0010
##    440        0.0797             nan     0.3000   -0.0009
##    460        0.0727             nan     0.3000   -0.0004
##    480        0.0679             nan     0.3000   -0.0009
##    500        0.0618             nan     0.3000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1484             nan     0.5000    0.0674
##      2        1.0791             nan     0.5000    0.0235
##      3        1.0318             nan     0.5000    0.0266
##      4        1.0034             nan     0.5000    0.0085
##      5        0.9644             nan     0.5000    0.0166
##      6        0.9481             nan     0.5000   -0.0004
##      7        0.9348             nan     0.5000    0.0014
##      8        0.9216             nan     0.5000    0.0010
##      9        0.9063             nan     0.5000    0.0054
##     10        0.8988             nan     0.5000   -0.0013
##     20        0.8450             nan     0.5000    0.0007
##     40        0.8026             nan     0.5000   -0.0094
##     60        0.9953             nan     0.5000    0.0002
##     80        0.9754             nan     0.5000   -0.0006
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1416             nan     0.5000    0.0478
##      2        1.0705             nan     0.5000    0.0196
##      3        1.0139             nan     0.5000    0.0216
##      4        0.9922             nan     0.5000    0.0063
##      5        0.9791             nan     0.5000    0.0012
##      6        0.9486             nan     0.5000    0.0136
##      7        0.9282             nan     0.5000    0.0025
##      8        0.9142             nan     0.5000   -0.0037
##      9        0.9073             nan     0.5000   -0.0022
##     10        0.9013             nan     0.5000   -0.0020
##     20        0.8485             nan     0.5000    0.0011
##     40        0.7967             nan     0.5000    0.0016
##     60        0.7667             nan     0.5000   -0.0026
##     80        0.7593             nan     0.5000    0.0116
##    100        0.7341             nan     0.5000   -0.0081
##    120        0.7241             nan     0.5000   -0.0052
##    140        0.7053             nan     0.5000   -0.0103
##    160        0.6907             nan     0.5000   -0.0025
##    180        0.6807             nan     0.5000   -0.0044
##    200        0.6499             nan     0.5000   -0.0024
##    220        0.6388             nan     0.5000   -0.0056
##    240        0.6128             nan     0.5000    0.0001
##    260        0.6088             nan     0.5000   -0.0079
##    280        0.5954             nan     0.5000   -0.0038
##    300        0.5842             nan     0.5000   -0.0038
##    320        0.7547             nan     0.5000   -0.0010
##    340        0.7404             nan     0.5000   -0.0014
##    360        0.7314             nan     0.5000   -0.0039
##    380    24784.4697             nan     0.5000   -0.0020
##    400    24784.4694             nan     0.5000   -0.0006
##    420    24784.3571             nan     0.5000   -0.0005
##    440    24784.3590             nan     0.5000   -0.0006
##    460    24784.3534             nan     0.5000    0.0001
##    480    24808.2080             nan     0.5000   -0.0007
##    500    24808.2077             nan     0.5000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1522             nan     0.5000    0.0615
##      2        1.1036             nan     0.5000    0.0080
##      3        1.0449             nan     0.5000    0.0240
##      4        1.0003             nan     0.5000    0.0171
##      5        0.9810             nan     0.5000    0.0022
##      6        0.9665             nan     0.5000    0.0016
##      7        0.9550             nan     0.5000   -0.0052
##      8        0.9440             nan     0.5000    0.0007
##      9        0.9316             nan     0.5000   -0.0035
##     10        0.9254             nan     0.5000   -0.0063
##     20        0.8494             nan     0.5000   -0.0039
##     40        0.8078             nan     0.5000   -0.0096
##     60        0.7684             nan     0.5000   -0.0048
##     80        0.7449             nan     0.5000   -0.0003
##    100        0.7323             nan     0.5000   -0.0060
##    120        0.7050             nan     0.5000   -0.0052
##    140        0.6832             nan     0.5000   -0.0021
##    160        0.6754             nan     0.5000   -0.0062
##    180        0.6564             nan     0.5000   -0.0029
##    200        0.6392             nan     0.5000   -0.0030
##    220        0.6308             nan     0.5000   -0.0051
##    240        0.6159             nan     0.5000   -0.0052
##    260        0.6013             nan     0.5000   -0.0079
##    280        0.5917             nan     0.5000   -0.0057
##    300        0.5701             nan     0.5000   -0.0056
##    320        0.5668             nan     0.5000   -0.0045
##    340        0.5518             nan     0.5000   -0.0034
##    360        0.5463             nan     0.5000   -0.0029
##    380        0.5386             nan     0.5000   -0.0076
##    400        0.5333             nan     0.5000   -0.0040
##    420        0.5180             nan     0.5000   -0.0022
##    440        0.5202             nan     0.5000   -0.0058
##    460        0.5139             nan     0.5000   -0.0047
##    480        0.4970             nan     0.5000   -0.0064
##    500        0.4904             nan     0.5000   -0.0040
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1218             nan     0.5000    0.0749
##      2        1.0150             nan     0.5000    0.0414
##      3        0.9762             nan     0.5000    0.0028
##      4        0.9417             nan     0.5000    0.0095
##      5        0.9245             nan     0.5000   -0.0013
##      6        0.9079             nan     0.5000   -0.0058
##      7        0.8961             nan     0.5000   -0.0027
##      8        0.8801             nan     0.5000   -0.0013
##      9        0.8766             nan     0.5000   -0.0120
##     10        0.8618             nan     0.5000   -0.0028
##     20        0.7967             nan     0.5000   -0.0073
##     40        0.6882             nan     0.5000   -0.0018
##     60        0.8248             nan     0.5000   -0.0028
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1191             nan     0.5000    0.0897
##      2        1.0111             nan     0.5000    0.0454
##      3        0.9741             nan     0.5000    0.0142
##      4        0.9459             nan     0.5000    0.0056
##      5        0.9273             nan     0.5000   -0.0024
##      6        0.8952             nan     0.5000    0.0089
##      7        0.8799             nan     0.5000    0.0027
##      8        0.8649             nan     0.5000    0.0015
##      9        0.8484             nan     0.5000   -0.0053
##     10        0.8432             nan     0.5000   -0.0017
##     20        0.7792             nan     0.5000   -0.0051
##     40        0.6641             nan     0.5000    0.0060
##     60        0.5976             nan     0.5000   -0.0118
##     80        0.5528             nan     0.5000   -0.0044
##    100        0.5084             nan     0.5000   -0.0090
##    120        0.4530             nan     0.5000   -0.0035
##    140        0.4077             nan     0.5000   -0.0072
##    160        0.3722             nan     0.5000   -0.0077
##    180        0.3431             nan     0.5000   -0.0032
##    200        0.2993             nan     0.5000   -0.0045
##    220        0.2696             nan     0.5000    0.0001
##    240        0.2424             nan     0.5000   -0.0032
##    260        0.2168             nan     0.5000   -0.0028
##    280        0.2012             nan     0.5000   -0.0034
##    300        0.1812             nan     0.5000   -0.0028
##    320        0.1695             nan     0.5000   -0.0022
##    340        0.1525             nan     0.5000   -0.0016
##    360        0.1374             nan     0.5000   -0.0012
##    380        0.1275             nan     0.5000   -0.0018
##    400        0.1208             nan     0.5000   -0.0029
##    420        0.1087             nan     0.5000   -0.0023
##    440        0.1024             nan     0.5000   -0.0011
##    460        0.0935             nan     0.5000   -0.0007
##    480        0.0839             nan     0.5000   -0.0005
##    500        0.0781             nan     0.5000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1248             nan     0.5000    0.0749
##      2        1.0407             nan     0.5000    0.0376
##      3        0.9916             nan     0.5000    0.0047
##      4        0.9460             nan     0.5000    0.0088
##      5        0.9329             nan     0.5000   -0.0065
##      6        0.9112             nan     0.5000    0.0003
##      7        0.8868             nan     0.5000    0.0008
##      8        0.8718             nan     0.5000   -0.0045
##      9        0.8528             nan     0.5000    0.0042
##     10        0.8549             nan     0.5000   -0.0174
##     20        0.8045             nan     0.5000   -0.0047
##     40        0.7341             nan     0.5000   -0.0107
##     60        0.6571             nan     0.5000   -0.0130
##     80        0.6261             nan     0.5000   -0.0063
##    100        0.5196             nan     0.5000   -0.0003
##    120        0.4836             nan     0.5000   -0.0136
##    140        0.4405             nan     0.5000   -0.0102
##    160        0.3863             nan     0.5000   -0.0101
##    180        0.3429             nan     0.5000   -0.0025
##    200        0.3039             nan     0.5000   -0.0020
##    220        0.2693             nan     0.5000   -0.0029
##    240        0.2504             nan     0.5000   -0.0032
##    260        0.2286             nan     0.5000   -0.0036
##    280        0.2049             nan     0.5000   -0.0020
##    300        0.1850             nan     0.5000   -0.0034
##    320        0.1734             nan     0.5000   -0.0027
##    340        0.1581             nan     0.5000   -0.0010
##    360        0.1496             nan     0.5000   -0.0033
##    380        0.1341             nan     0.5000   -0.0022
##    400        0.1211             nan     0.5000   -0.0001
##    420        0.1125             nan     0.5000   -0.0023
##    440        0.1013             nan     0.5000   -0.0010
##    460        0.0924             nan     0.5000   -0.0012
##    480        0.0856             nan     0.5000   -0.0023
##    500        0.0811             nan     0.5000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1040             nan     0.5000    0.0865
##      2        1.0286             nan     0.5000    0.0200
##      3        0.9603             nan     0.5000    0.0239
##      4        0.9229             nan     0.5000    0.0035
##      5        0.8947             nan     0.5000   -0.0004
##      6        0.8661             nan     0.5000    0.0050
##      7        0.8539             nan     0.5000   -0.0047
##      8        0.8402             nan     0.5000   -0.0146
##      9        0.8139             nan     0.5000    0.0057
##     10        0.8008             nan     0.5000   -0.0054
##     20        3.5636             nan     0.5000   -0.0095
##     40        3.5028             nan     0.5000   -0.0218
##     60        3.3449             nan     0.5000   -0.0116
##     80        3.2498             nan     0.5000   -0.0083
##    100           inf             nan     0.5000   -0.0107
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0856             nan     0.5000    0.0887
##      2        0.9858             nan     0.5000    0.0423
##      3        0.9317             nan     0.5000    0.0137
##      4        0.8921             nan     0.5000    0.0036
##      5        0.8630             nan     0.5000   -0.0060
##      6        0.8511             nan     0.5000   -0.0154
##      7        0.8307             nan     0.5000   -0.0062
##      8        0.8155             nan     0.5000   -0.0210
##      9        0.7990             nan     0.5000   -0.0049
##     10        0.7847             nan     0.5000   -0.0042
##     20        0.6700             nan     0.5000   -0.0017
##     40        0.5282             nan     0.5000   -0.0110
##     60        0.4378             nan     0.5000   -0.0117
##     80        0.3578             nan     0.5000   -0.0011
##    100        0.2911             nan     0.5000   -0.0052
##    120        0.2487             nan     0.5000   -0.0076
##    140        0.2195             nan     0.5000   -0.0030
##    160        0.1897             nan     0.5000   -0.0051
##    180        0.1539             nan     0.5000   -0.0008
##    200        0.1365             nan     0.5000   -0.0033
##    220        0.1116             nan     0.5000   -0.0016
##    240        0.0979             nan     0.5000    0.0000
##    260        0.0877             nan     0.5000   -0.0004
##    280        0.0787             nan     0.5000   -0.0013
##    300        0.0689             nan     0.5000   -0.0013
##    320        0.0617             nan     0.5000   -0.0005
##    340        0.0538             nan     0.5000   -0.0010
##    360        0.0466             nan     0.5000   -0.0012
##    380        0.0399             nan     0.5000   -0.0002
##    400        0.0361             nan     0.5000   -0.0004
##    420        0.0318             nan     0.5000   -0.0002
##    440        0.0276             nan     0.5000   -0.0003
##    460        0.0253             nan     0.5000   -0.0003
##    480        0.0221             nan     0.5000   -0.0000
##    500        0.0203             nan     0.5000   -0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1113             nan     0.5000    0.0792
##      2        1.0204             nan     0.5000    0.0229
##      3        0.9576             nan     0.5000    0.0216
##      4        0.9142             nan     0.5000    0.0069
##      5        0.9111             nan     0.5000   -0.0295
##      6        0.8803             nan     0.5000    0.0024
##      7        0.8542             nan     0.5000   -0.0017
##      8        0.8400             nan     0.5000    0.0000
##      9        0.8214             nan     0.5000   -0.0013
##     10        0.8028             nan     0.5000   -0.0080
##     20        0.6789             nan     0.5000   -0.0080
##     40        0.5761             nan     0.5000   -0.0100
##     60        0.4873             nan     0.5000   -0.0062
##     80        0.4004             nan     0.5000   -0.0089
##    100        0.3202             nan     0.5000   -0.0029
##    120        0.2660             nan     0.5000   -0.0036
##    140        0.2199             nan     0.5000   -0.0034
##    160        0.1719             nan     0.5000   -0.0034
##    180        0.1460             nan     0.5000   -0.0045
##    200        0.1275             nan     0.5000   -0.0041
##    220        0.1108             nan     0.5000   -0.0027
##    240        0.0937             nan     0.5000   -0.0007
##    260        0.0806             nan     0.5000   -0.0006
##    280        0.0717             nan     0.5000   -0.0010
##    300        0.0642             nan     0.5000   -0.0014
##    320        0.0570             nan     0.5000   -0.0007
##    340        0.0486             nan     0.5000   -0.0002
##    360        0.0429             nan     0.5000   -0.0000
##    380        0.0406             nan     0.5000   -0.0007
##    400        0.0357             nan     0.5000   -0.0002
##    420        0.0310             nan     0.5000   -0.0006
##    440        0.0277             nan     0.5000   -0.0007
##    460        0.0242             nan     0.5000    0.0000
##    480        0.0219             nan     0.5000   -0.0004
##    500        0.0197             nan     0.5000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1246             nan     1.0000    0.0498
##      2        1.0445             nan     1.0000    0.0330
##      3        0.9964             nan     1.0000    0.0085
##      4        0.9675             nan     1.0000    0.0056
##      5        0.9583             nan     1.0000   -0.0063
##      6        0.9483             nan     1.0000   -0.0099
##      7        0.9494             nan     1.0000   -0.0170
##      8        0.9466             nan     1.0000   -0.0094
##      9        0.9369             nan     1.0000   -0.0007
##     10        0.9638             nan     1.0000   -0.0673
##     20        0.9451             nan     1.0000   -0.0087
##     40           inf             nan     1.0000   -0.0291
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000    0.0007
##    300 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000   -0.0219
##    320 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000    0.0027
##    340 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000   -0.0053
##    360 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000   -0.0016
##    380 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000   -0.0010
##    400 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000   -0.0008
##    420 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000    0.0047
##    440 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000   -0.0009
##    460 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000   -0.0043
##    480 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000    0.0000
##    500 206657770593747471408486666042040824248444080600868484686646004804428680042804066444402464226804846244262088846268826206.0000             nan     1.0000   -0.0034
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1114             nan     1.0000    0.1011
##      2        1.0635             nan     1.0000   -0.0015
##      3        1.0018             nan     1.0000    0.0213
##      4        0.9738             nan     1.0000   -0.0069
##      5        0.9725             nan     1.0000   -0.0342
##      6        0.9527             nan     1.0000   -0.0042
##      7        0.9346             nan     1.0000    0.0042
##      8        0.9437             nan     1.0000   -0.0181
##      9        0.9378             nan     1.0000   -0.0056
##     10        0.9154             nan     1.0000    0.0076
##     20        0.9271             nan     1.0000   -0.0164
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1100             nan     1.0000    0.1054
##      2        1.0353             nan     1.0000    0.0213
##      3        0.9730             nan     1.0000    0.0168
##      4        0.9677             nan     1.0000   -0.0135
##      5        0.9538             nan     1.0000   -0.0067
##      6        0.9256             nan     1.0000    0.0129
##      7        0.9413             nan     1.0000   -0.0265
##      8        0.9236             nan     1.0000    0.0003
##      9        0.9085             nan     1.0000   -0.0166
##     10        0.8965             nan     1.0000   -0.0042
##     20        0.8550             nan     1.0000   -0.0197
##     40        0.7972             nan     1.0000   -0.0012
##     60        0.8010             nan     1.0000   -0.0172
##     80        1.4753             nan     1.0000   -0.3277
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0488             nan     1.0000    0.1211
##      2        0.9782             nan     1.0000    0.0133
##      3        0.9646             nan     1.0000   -0.0197
##      4        0.9465             nan     1.0000   -0.0222
##      5        0.9373             nan     1.0000   -0.0243
##      6        0.9464             nan     1.0000   -0.0263
##      7        0.9561             nan     1.0000   -0.0342
##      8        0.9550             nan     1.0000   -0.0271
##      9        0.9465             nan     1.0000   -0.0194
##     10        0.9582             nan     1.0000   -0.0366
##     20        0.9325             nan     1.0000   -0.0403
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0597             nan     1.0000    0.0818
##      2        0.9786             nan     1.0000    0.0153
##      3        0.9384             nan     1.0000    0.0061
##      4        0.9098             nan     1.0000   -0.0001
##      5        0.9203             nan     1.0000   -0.0400
##      6        0.9205             nan     1.0000   -0.0223
##      7        0.9156             nan     1.0000   -0.0326
##      8        0.9154             nan     1.0000   -0.0172
##      9        0.8951             nan     1.0000   -0.0097
##     10        0.8921             nan     1.0000   -0.0202
##     20        0.9368             nan     1.0000   -0.0281
##     40        0.9183             nan     1.0000   -0.0776
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0463             nan     1.0000    0.1023
##      2        0.9348             nan     1.0000    0.0428
##      3        0.9226             nan     1.0000   -0.0191
##      4        0.8837             nan     1.0000    0.0043
##      5        0.8839             nan     1.0000   -0.0286
##      6        0.8885             nan     1.0000   -0.0331
##      7        0.9106             nan     1.0000   -0.0520
##      8        0.8884             nan     1.0000   -0.0188
##      9        0.8873             nan     1.0000   -0.0357
##     10        0.8731             nan     1.0000   -0.0099
##     20        0.8340             nan     1.0000   -0.0307
##     40        0.6796             nan     1.0000   -0.0032
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0415             nan     1.0000    0.0998
##      2        0.9470             nan     1.0000    0.0302
##      3        0.9102             nan     1.0000   -0.0232
##      4        0.9289             nan     1.0000   -0.0445
##      5        0.9483             nan     1.0000   -0.0768
##      6        0.9242             nan     1.0000   -0.0167
##      7        1.3181             nan     1.0000   -0.0221
##      8        1.3492             nan     1.0000   -0.0839
##      9        1.3115             nan     1.0000   -0.0237
##     10        1.2841             nan     1.0000   -0.0093
##     20        1.2950             nan     1.0000   -0.0306
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0181             nan     1.0000    0.1361
##      2        0.9541             nan     1.0000   -0.0060
##      3        0.9239             nan     1.0000   -0.0132
##      4        0.9510             nan     1.0000   -0.0724
##      5        0.9344             nan     1.0000   -0.0270
##      6        0.9829             nan     1.0000   -0.0932
##      7        0.9221             nan     1.0000   -0.0016
##      8        0.9085             nan     1.0000   -0.0385
##      9        0.8486             nan     1.0000    0.0052
##     10        0.8475             nan     1.0000   -0.0407
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0534             nan     1.0000    0.0981
##      2        0.9539             nan     1.0000    0.0390
##      3        0.9923             nan     1.0000   -0.0641
##      4        1.2407             nan     1.0000   -0.3070
##      5        1.2011             nan     1.0000   -0.0022
##      6        1.2235             nan     1.0000   -0.0637
##      7        1.2521             nan     1.0000   -0.0779
##      8        1.2221             nan     1.0000   -0.0164
##      9        1.2440             nan     1.0000   -0.0566
##     10        1.2352             nan     1.0000   -0.0352
##     20        1.1388             nan     1.0000   -0.0346
##     40 208258942306149.3750             nan     1.0000   -0.0615
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0001
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2859             nan     0.0010    0.0002
##     40        1.2790             nan     0.0010    0.0001
##     60        1.2722             nan     0.0010    0.0002
##     80        1.2659             nan     0.0010    0.0002
##    100        1.2597             nan     0.0010    0.0001
##    120        1.2534             nan     0.0010    0.0001
##    140        1.2474             nan     0.0010    0.0001
##    160        1.2415             nan     0.0010    0.0001
##    180        1.2358             nan     0.0010    0.0001
##    200        1.2305             nan     0.0010    0.0001
##    220        1.2252             nan     0.0010    0.0001
##    240        1.2202             nan     0.0010    0.0001
##    260        1.2153             nan     0.0010    0.0001
##    280        1.2106             nan     0.0010    0.0001
##    300        1.2059             nan     0.0010    0.0001
##    320        1.2015             nan     0.0010    0.0001
##    340        1.1973             nan     0.0010    0.0001
##    360        1.1931             nan     0.0010    0.0001
##    380        1.1891             nan     0.0010    0.0001
##    400        1.1851             nan     0.0010    0.0001
##    420        1.1813             nan     0.0010    0.0001
##    440        1.1775             nan     0.0010    0.0001
##    460        1.1736             nan     0.0010    0.0001
##    480        1.1700             nan     0.0010    0.0001
##    500        1.1665             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0001
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0001
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2860             nan     0.0010    0.0002
##     40        1.2789             nan     0.0010    0.0002
##     60        1.2721             nan     0.0010    0.0001
##     80        1.2655             nan     0.0010    0.0002
##    100        1.2593             nan     0.0010    0.0001
##    120        1.2530             nan     0.0010    0.0002
##    140        1.2473             nan     0.0010    0.0001
##    160        1.2415             nan     0.0010    0.0001
##    180        1.2360             nan     0.0010    0.0001
##    200        1.2307             nan     0.0010    0.0001
##    220        1.2255             nan     0.0010    0.0001
##    240        1.2202             nan     0.0010    0.0001
##    260        1.2155             nan     0.0010    0.0001
##    280        1.2108             nan     0.0010    0.0001
##    300        1.2061             nan     0.0010    0.0001
##    320        1.2017             nan     0.0010    0.0001
##    340        1.1974             nan     0.0010    0.0001
##    360        1.1930             nan     0.0010    0.0001
##    380        1.1889             nan     0.0010    0.0001
##    400        1.1849             nan     0.0010    0.0001
##    420        1.1811             nan     0.0010    0.0001
##    440        1.1773             nan     0.0010    0.0001
##    460        1.1736             nan     0.0010    0.0001
##    480        1.1700             nan     0.0010    0.0001
##    500        1.1664             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2914             nan     0.0010    0.0002
##      6        1.2910             nan     0.0010    0.0002
##      7        1.2906             nan     0.0010    0.0002
##      8        1.2902             nan     0.0010    0.0002
##      9        1.2898             nan     0.0010    0.0002
##     10        1.2895             nan     0.0010    0.0002
##     20        1.2860             nan     0.0010    0.0002
##     40        1.2789             nan     0.0010    0.0002
##     60        1.2720             nan     0.0010    0.0002
##     80        1.2654             nan     0.0010    0.0001
##    100        1.2591             nan     0.0010    0.0002
##    120        1.2530             nan     0.0010    0.0001
##    140        1.2470             nan     0.0010    0.0001
##    160        1.2415             nan     0.0010    0.0001
##    180        1.2360             nan     0.0010    0.0001
##    200        1.2308             nan     0.0010    0.0001
##    220        1.2258             nan     0.0010    0.0001
##    240        1.2209             nan     0.0010    0.0001
##    260        1.2159             nan     0.0010    0.0001
##    280        1.2113             nan     0.0010    0.0001
##    300        1.2068             nan     0.0010    0.0001
##    320        1.2023             nan     0.0010    0.0001
##    340        1.1980             nan     0.0010    0.0001
##    360        1.1939             nan     0.0010    0.0001
##    380        1.1897             nan     0.0010    0.0001
##    400        1.1857             nan     0.0010    0.0001
##    420        1.1818             nan     0.0010    0.0001
##    440        1.1779             nan     0.0010    0.0001
##    460        1.1744             nan     0.0010    0.0001
##    480        1.1706             nan     0.0010    0.0001
##    500        1.1671             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2843             nan     0.0010    0.0002
##     40        1.2755             nan     0.0010    0.0002
##     60        1.2668             nan     0.0010    0.0002
##     80        1.2582             nan     0.0010    0.0002
##    100        1.2503             nan     0.0010    0.0002
##    120        1.2424             nan     0.0010    0.0002
##    140        1.2348             nan     0.0010    0.0002
##    160        1.2274             nan     0.0010    0.0002
##    180        1.2204             nan     0.0010    0.0001
##    200        1.2135             nan     0.0010    0.0001
##    220        1.2067             nan     0.0010    0.0002
##    240        1.2001             nan     0.0010    0.0001
##    260        1.1939             nan     0.0010    0.0001
##    280        1.1877             nan     0.0010    0.0001
##    300        1.1817             nan     0.0010    0.0001
##    320        1.1758             nan     0.0010    0.0001
##    340        1.1701             nan     0.0010    0.0001
##    360        1.1645             nan     0.0010    0.0001
##    380        1.1590             nan     0.0010    0.0001
##    400        1.1538             nan     0.0010    0.0001
##    420        1.1486             nan     0.0010    0.0001
##    440        1.1437             nan     0.0010    0.0001
##    460        1.1388             nan     0.0010    0.0001
##    480        1.1340             nan     0.0010    0.0001
##    500        1.1293             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2842             nan     0.0010    0.0002
##     40        1.2754             nan     0.0010    0.0002
##     60        1.2666             nan     0.0010    0.0002
##     80        1.2583             nan     0.0010    0.0002
##    100        1.2503             nan     0.0010    0.0002
##    120        1.2425             nan     0.0010    0.0002
##    140        1.2347             nan     0.0010    0.0002
##    160        1.2273             nan     0.0010    0.0002
##    180        1.2201             nan     0.0010    0.0002
##    200        1.2134             nan     0.0010    0.0002
##    220        1.2068             nan     0.0010    0.0001
##    240        1.2004             nan     0.0010    0.0001
##    260        1.1940             nan     0.0010    0.0001
##    280        1.1876             nan     0.0010    0.0001
##    300        1.1816             nan     0.0010    0.0001
##    320        1.1760             nan     0.0010    0.0001
##    340        1.1703             nan     0.0010    0.0001
##    360        1.1649             nan     0.0010    0.0001
##    380        1.1593             nan     0.0010    0.0001
##    400        1.1541             nan     0.0010    0.0001
##    420        1.1489             nan     0.0010    0.0001
##    440        1.1438             nan     0.0010    0.0001
##    460        1.1389             nan     0.0010    0.0001
##    480        1.1340             nan     0.0010    0.0001
##    500        1.1293             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2911             nan     0.0010    0.0002
##      6        1.2906             nan     0.0010    0.0002
##      7        1.2902             nan     0.0010    0.0002
##      8        1.2897             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2843             nan     0.0010    0.0002
##     40        1.2754             nan     0.0010    0.0002
##     60        1.2669             nan     0.0010    0.0002
##     80        1.2585             nan     0.0010    0.0002
##    100        1.2500             nan     0.0010    0.0002
##    120        1.2422             nan     0.0010    0.0002
##    140        1.2345             nan     0.0010    0.0002
##    160        1.2270             nan     0.0010    0.0002
##    180        1.2198             nan     0.0010    0.0002
##    200        1.2127             nan     0.0010    0.0002
##    220        1.2062             nan     0.0010    0.0001
##    240        1.1997             nan     0.0010    0.0001
##    260        1.1934             nan     0.0010    0.0001
##    280        1.1872             nan     0.0010    0.0001
##    300        1.1811             nan     0.0010    0.0001
##    320        1.1751             nan     0.0010    0.0001
##    340        1.1694             nan     0.0010    0.0001
##    360        1.1641             nan     0.0010    0.0001
##    380        1.1585             nan     0.0010    0.0001
##    400        1.1531             nan     0.0010    0.0001
##    420        1.1479             nan     0.0010    0.0001
##    440        1.1429             nan     0.0010    0.0001
##    460        1.1379             nan     0.0010    0.0001
##    480        1.1332             nan     0.0010    0.0001
##    500        1.1285             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0002
##      7        1.2895             nan     0.0010    0.0002
##      8        1.2890             nan     0.0010    0.0002
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2879             nan     0.0010    0.0003
##     20        1.2828             nan     0.0010    0.0002
##     40        1.2725             nan     0.0010    0.0002
##     60        1.2627             nan     0.0010    0.0002
##     80        1.2530             nan     0.0010    0.0002
##    100        1.2439             nan     0.0010    0.0002
##    120        1.2349             nan     0.0010    0.0002
##    140        1.2261             nan     0.0010    0.0002
##    160        1.2178             nan     0.0010    0.0002
##    180        1.2095             nan     0.0010    0.0002
##    200        1.2015             nan     0.0010    0.0002
##    220        1.1936             nan     0.0010    0.0002
##    240        1.1862             nan     0.0010    0.0001
##    260        1.1789             nan     0.0010    0.0002
##    280        1.1720             nan     0.0010    0.0002
##    300        1.1651             nan     0.0010    0.0001
##    320        1.1585             nan     0.0010    0.0001
##    340        1.1519             nan     0.0010    0.0001
##    360        1.1455             nan     0.0010    0.0001
##    380        1.1392             nan     0.0010    0.0002
##    400        1.1334             nan     0.0010    0.0001
##    420        1.1275             nan     0.0010    0.0001
##    440        1.1218             nan     0.0010    0.0001
##    460        1.1161             nan     0.0010    0.0001
##    480        1.1108             nan     0.0010    0.0001
##    500        1.1053             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0003
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2908             nan     0.0010    0.0002
##      6        1.2903             nan     0.0010    0.0002
##      7        1.2898             nan     0.0010    0.0002
##      8        1.2893             nan     0.0010    0.0002
##      9        1.2887             nan     0.0010    0.0003
##     10        1.2882             nan     0.0010    0.0002
##     20        1.2830             nan     0.0010    0.0002
##     40        1.2726             nan     0.0010    0.0002
##     60        1.2628             nan     0.0010    0.0002
##     80        1.2532             nan     0.0010    0.0002
##    100        1.2440             nan     0.0010    0.0002
##    120        1.2349             nan     0.0010    0.0002
##    140        1.2262             nan     0.0010    0.0002
##    160        1.2177             nan     0.0010    0.0002
##    180        1.2098             nan     0.0010    0.0001
##    200        1.2020             nan     0.0010    0.0002
##    220        1.1941             nan     0.0010    0.0002
##    240        1.1867             nan     0.0010    0.0002
##    260        1.1795             nan     0.0010    0.0002
##    280        1.1723             nan     0.0010    0.0001
##    300        1.1654             nan     0.0010    0.0001
##    320        1.1587             nan     0.0010    0.0001
##    340        1.1524             nan     0.0010    0.0002
##    360        1.1461             nan     0.0010    0.0001
##    380        1.1399             nan     0.0010    0.0001
##    400        1.1343             nan     0.0010    0.0001
##    420        1.1281             nan     0.0010    0.0001
##    440        1.1223             nan     0.0010    0.0001
##    460        1.1168             nan     0.0010    0.0001
##    480        1.1112             nan     0.0010    0.0001
##    500        1.1058             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0003
##      2        1.2922             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2886             nan     0.0010    0.0002
##     10        1.2880             nan     0.0010    0.0002
##     20        1.2829             nan     0.0010    0.0002
##     40        1.2727             nan     0.0010    0.0002
##     60        1.2626             nan     0.0010    0.0002
##     80        1.2532             nan     0.0010    0.0002
##    100        1.2439             nan     0.0010    0.0002
##    120        1.2353             nan     0.0010    0.0002
##    140        1.2267             nan     0.0010    0.0002
##    160        1.2183             nan     0.0010    0.0002
##    180        1.2101             nan     0.0010    0.0002
##    200        1.2021             nan     0.0010    0.0002
##    220        1.1944             nan     0.0010    0.0002
##    240        1.1869             nan     0.0010    0.0002
##    260        1.1796             nan     0.0010    0.0001
##    280        1.1725             nan     0.0010    0.0001
##    300        1.1657             nan     0.0010    0.0001
##    320        1.1590             nan     0.0010    0.0001
##    340        1.1525             nan     0.0010    0.0001
##    360        1.1463             nan     0.0010    0.0001
##    380        1.1402             nan     0.0010    0.0001
##    400        1.1342             nan     0.0010    0.0001
##    420        1.1284             nan     0.0010    0.0001
##    440        1.1227             nan     0.0010    0.0001
##    460        1.1172             nan     0.0010    0.0001
##    480        1.1119             nan     0.0010    0.0001
##    500        1.1065             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2590             nan     0.1000    0.0167
##      2        1.2300             nan     0.1000    0.0115
##      3        1.2042             nan     0.1000    0.0116
##      4        1.1814             nan     0.1000    0.0090
##      5        1.1644             nan     0.1000    0.0065
##      6        1.1487             nan     0.1000    0.0074
##      7        1.1336             nan     0.1000    0.0066
##      8        1.1177             nan     0.1000    0.0063
##      9        1.1047             nan     0.1000    0.0042
##     10        1.0932             nan     0.1000    0.0042
##     20        1.0085             nan     0.1000    0.0005
##     40        0.9299             nan     0.1000   -0.0004
##     60        0.8884             nan     0.1000    0.0000
##     80        0.8588             nan     0.1000    0.0002
##    100        0.8413             nan     0.1000   -0.0008
##    120        0.8261             nan     0.1000   -0.0003
##    140        0.8170             nan     0.1000   -0.0014
##    160        0.8084             nan     0.1000   -0.0001
##    180        0.7998             nan     0.1000    0.0000
##    200        0.7922             nan     0.1000   -0.0016
##    220        0.7848             nan     0.1000   -0.0006
##    240        0.7771             nan     0.1000   -0.0011
##    260        0.7712             nan     0.1000   -0.0006
##    280        0.7664             nan     0.1000   -0.0007
##    300        0.7576             nan     0.1000   -0.0010
##    320        0.7535             nan     0.1000   -0.0016
##    340        0.7476             nan     0.1000   -0.0001
##    360        0.7405             nan     0.1000   -0.0005
##    380        0.7350             nan     0.1000   -0.0005
##    400        0.7320             nan     0.1000   -0.0007
##    420        0.7268             nan     0.1000   -0.0016
##    440        0.7215             nan     0.1000   -0.0017
##    460        0.7175             nan     0.1000   -0.0014
##    480        0.7120             nan     0.1000   -0.0008
##    500        0.7083             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2532             nan     0.1000    0.0159
##      2        1.2215             nan     0.1000    0.0122
##      3        1.1970             nan     0.1000    0.0092
##      4        1.1764             nan     0.1000    0.0063
##      5        1.1577             nan     0.1000    0.0075
##      6        1.1401             nan     0.1000    0.0067
##      7        1.1256             nan     0.1000    0.0060
##      8        1.1112             nan     0.1000    0.0058
##      9        1.0978             nan     0.1000    0.0045
##     10        1.0869             nan     0.1000    0.0048
##     20        1.0072             nan     0.1000    0.0016
##     40        0.9268             nan     0.1000    0.0007
##     60        0.8888             nan     0.1000   -0.0005
##     80        0.8605             nan     0.1000   -0.0004
##    100        0.8464             nan     0.1000   -0.0004
##    120        0.8275             nan     0.1000   -0.0010
##    140        0.8135             nan     0.1000   -0.0010
##    160        0.8022             nan     0.1000   -0.0003
##    180        0.7932             nan     0.1000   -0.0003
##    200        0.7827             nan     0.1000   -0.0007
##    220        0.7765             nan     0.1000   -0.0014
##    240        0.7713             nan     0.1000   -0.0013
##    260        0.7652             nan     0.1000   -0.0008
##    280        0.7590             nan     0.1000   -0.0006
##    300        0.7543             nan     0.1000   -0.0006
##    320        0.7485             nan     0.1000   -0.0011
##    340        0.7447             nan     0.1000   -0.0015
##    360        0.7385             nan     0.1000   -0.0006
##    380        0.7332             nan     0.1000   -0.0025
##    400        0.7283             nan     0.1000   -0.0012
##    420        0.7232             nan     0.1000   -0.0021
##    440        0.7197             nan     0.1000   -0.0006
##    460        0.7157             nan     0.1000   -0.0004
##    480        0.7125             nan     0.1000   -0.0011
##    500        0.7088             nan     0.1000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2601             nan     0.1000    0.0148
##      2        1.2276             nan     0.1000    0.0132
##      3        1.2058             nan     0.1000    0.0088
##      4        1.1849             nan     0.1000    0.0097
##      5        1.1677             nan     0.1000    0.0074
##      6        1.1500             nan     0.1000    0.0071
##      7        1.1351             nan     0.1000    0.0065
##      8        1.1224             nan     0.1000    0.0055
##      9        1.1091             nan     0.1000    0.0058
##     10        1.0978             nan     0.1000    0.0036
##     20        1.0127             nan     0.1000    0.0027
##     40        0.9256             nan     0.1000    0.0004
##     60        0.8899             nan     0.1000   -0.0002
##     80        0.8637             nan     0.1000   -0.0010
##    100        0.8428             nan     0.1000    0.0003
##    120        0.8278             nan     0.1000   -0.0017
##    140        0.8137             nan     0.1000   -0.0009
##    160        0.8047             nan     0.1000   -0.0007
##    180        0.7955             nan     0.1000   -0.0015
##    200        0.7870             nan     0.1000   -0.0008
##    220        0.7809             nan     0.1000   -0.0011
##    240        0.7736             nan     0.1000   -0.0013
##    260        0.7678             nan     0.1000   -0.0013
##    280        0.7612             nan     0.1000   -0.0007
##    300        0.7528             nan     0.1000   -0.0011
##    320        0.7479             nan     0.1000   -0.0015
##    340        0.7435             nan     0.1000   -0.0018
##    360        0.7382             nan     0.1000   -0.0010
##    380        0.7362             nan     0.1000   -0.0007
##    400        0.7327             nan     0.1000   -0.0003
##    420        0.7269             nan     0.1000   -0.0006
##    440        0.7234             nan     0.1000   -0.0006
##    460        0.7187             nan     0.1000   -0.0011
##    480        0.7136             nan     0.1000   -0.0010
##    500        0.7097             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2553             nan     0.1000    0.0184
##      2        1.2156             nan     0.1000    0.0136
##      3        1.1853             nan     0.1000    0.0171
##      4        1.1560             nan     0.1000    0.0104
##      5        1.1306             nan     0.1000    0.0091
##      6        1.1075             nan     0.1000    0.0109
##      7        1.0858             nan     0.1000    0.0068
##      8        1.0716             nan     0.1000    0.0068
##      9        1.0570             nan     0.1000    0.0057
##     10        1.0430             nan     0.1000    0.0055
##     20        0.9404             nan     0.1000    0.0014
##     40        0.8547             nan     0.1000   -0.0005
##     60        0.8026             nan     0.1000   -0.0024
##     80        0.7711             nan     0.1000   -0.0004
##    100        0.7472             nan     0.1000   -0.0011
##    120        0.7214             nan     0.1000   -0.0011
##    140        0.6947             nan     0.1000   -0.0018
##    160        0.6712             nan     0.1000   -0.0023
##    180        0.6535             nan     0.1000   -0.0016
##    200        0.6351             nan     0.1000   -0.0022
##    220        0.6160             nan     0.1000    0.0002
##    240        0.6032             nan     0.1000   -0.0008
##    260        0.5872             nan     0.1000   -0.0014
##    280        0.5735             nan     0.1000   -0.0010
##    300        0.5544             nan     0.1000   -0.0022
##    320        0.5402             nan     0.1000   -0.0006
##    340        0.5282             nan     0.1000   -0.0006
##    360        0.5170             nan     0.1000   -0.0023
##    380        0.5055             nan     0.1000   -0.0017
##    400        0.4899             nan     0.1000   -0.0010
##    420        0.4792             nan     0.1000    0.0000
##    440        0.4674             nan     0.1000   -0.0014
##    460        0.4593             nan     0.1000   -0.0012
##    480        0.4514             nan     0.1000   -0.0014
##    500        0.4416             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2498             nan     0.1000    0.0176
##      2        1.2140             nan     0.1000    0.0165
##      3        1.1814             nan     0.1000    0.0145
##      4        1.1555             nan     0.1000    0.0109
##      5        1.1271             nan     0.1000    0.0128
##      6        1.1031             nan     0.1000    0.0094
##      7        1.0840             nan     0.1000    0.0089
##      8        1.0674             nan     0.1000    0.0046
##      9        1.0499             nan     0.1000    0.0064
##     10        1.0339             nan     0.1000    0.0052
##     20        0.9391             nan     0.1000    0.0019
##     40        0.8615             nan     0.1000   -0.0001
##     60        0.8163             nan     0.1000    0.0012
##     80        0.7763             nan     0.1000   -0.0008
##    100        0.7526             nan     0.1000   -0.0016
##    120        0.7291             nan     0.1000   -0.0018
##    140        0.7067             nan     0.1000   -0.0015
##    160        0.6897             nan     0.1000   -0.0003
##    180        0.6715             nan     0.1000   -0.0008
##    200        0.6533             nan     0.1000   -0.0007
##    220        0.6344             nan     0.1000   -0.0016
##    240        0.6184             nan     0.1000   -0.0003
##    260        0.6035             nan     0.1000   -0.0007
##    280        0.5902             nan     0.1000   -0.0020
##    300        0.5727             nan     0.1000   -0.0015
##    320        0.5606             nan     0.1000   -0.0013
##    340        0.5486             nan     0.1000   -0.0010
##    360        0.5370             nan     0.1000   -0.0008
##    380        0.5232             nan     0.1000   -0.0018
##    400        0.5084             nan     0.1000   -0.0008
##    420        0.4956             nan     0.1000   -0.0011
##    440        0.4820             nan     0.1000   -0.0009
##    460        0.4705             nan     0.1000   -0.0005
##    480        0.4604             nan     0.1000   -0.0001
##    500        0.4526             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2518             nan     0.1000    0.0189
##      2        1.2153             nan     0.1000    0.0180
##      3        1.1829             nan     0.1000    0.0124
##      4        1.1523             nan     0.1000    0.0144
##      5        1.1271             nan     0.1000    0.0107
##      6        1.1056             nan     0.1000    0.0090
##      7        1.0866             nan     0.1000    0.0065
##      8        1.0681             nan     0.1000    0.0059
##      9        1.0478             nan     0.1000    0.0060
##     10        1.0339             nan     0.1000    0.0042
##     20        0.9384             nan     0.1000    0.0002
##     40        0.8516             nan     0.1000    0.0003
##     60        0.8030             nan     0.1000   -0.0009
##     80        0.7725             nan     0.1000   -0.0011
##    100        0.7453             nan     0.1000   -0.0017
##    120        0.7252             nan     0.1000   -0.0033
##    140        0.6987             nan     0.1000   -0.0005
##    160        0.6792             nan     0.1000   -0.0012
##    180        0.6613             nan     0.1000   -0.0008
##    200        0.6424             nan     0.1000    0.0002
##    220        0.6284             nan     0.1000   -0.0011
##    240        0.6120             nan     0.1000   -0.0021
##    260        0.5982             nan     0.1000   -0.0020
##    280        0.5871             nan     0.1000   -0.0007
##    300        0.5733             nan     0.1000   -0.0014
##    320        0.5573             nan     0.1000   -0.0012
##    340        0.5441             nan     0.1000    0.0001
##    360        0.5320             nan     0.1000   -0.0013
##    380        0.5199             nan     0.1000   -0.0007
##    400        0.5083             nan     0.1000   -0.0010
##    420        0.4988             nan     0.1000   -0.0008
##    440        0.4856             nan     0.1000   -0.0012
##    460        0.4750             nan     0.1000   -0.0008
##    480        0.4656             nan     0.1000   -0.0006
##    500        0.4562             nan     0.1000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2407             nan     0.1000    0.0231
##      2        1.1945             nan     0.1000    0.0211
##      3        1.1599             nan     0.1000    0.0133
##      4        1.1306             nan     0.1000    0.0117
##      5        1.1074             nan     0.1000    0.0104
##      6        1.0824             nan     0.1000    0.0095
##      7        1.0616             nan     0.1000    0.0091
##      8        1.0444             nan     0.1000    0.0069
##      9        1.0260             nan     0.1000    0.0067
##     10        1.0126             nan     0.1000    0.0030
##     20        0.9037             nan     0.1000   -0.0004
##     40        0.8011             nan     0.1000   -0.0010
##     60        0.7468             nan     0.1000    0.0000
##     80        0.6969             nan     0.1000   -0.0007
##    100        0.6600             nan     0.1000   -0.0020
##    120        0.6235             nan     0.1000   -0.0007
##    140        0.5926             nan     0.1000   -0.0006
##    160        0.5664             nan     0.1000   -0.0009
##    180        0.5425             nan     0.1000   -0.0009
##    200        0.5188             nan     0.1000   -0.0008
##    220        0.4996             nan     0.1000   -0.0016
##    240        0.4805             nan     0.1000   -0.0020
##    260        0.4569             nan     0.1000   -0.0005
##    280        0.4378             nan     0.1000   -0.0005
##    300        0.4188             nan     0.1000   -0.0007
##    320        0.4008             nan     0.1000   -0.0013
##    340        0.3858             nan     0.1000   -0.0001
##    360        0.3714             nan     0.1000   -0.0012
##    380        0.3564             nan     0.1000   -0.0010
##    400        0.3427             nan     0.1000   -0.0010
##    420        0.3303             nan     0.1000   -0.0013
##    440        0.3208             nan     0.1000   -0.0007
##    460        0.3099             nan     0.1000   -0.0002
##    480        0.2985             nan     0.1000   -0.0010
##    500        0.2880             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2487             nan     0.1000    0.0205
##      2        1.2091             nan     0.1000    0.0164
##      3        1.1789             nan     0.1000    0.0104
##      4        1.1438             nan     0.1000    0.0141
##      5        1.1139             nan     0.1000    0.0118
##      6        1.0881             nan     0.1000    0.0077
##      7        1.0614             nan     0.1000    0.0093
##      8        1.0447             nan     0.1000    0.0050
##      9        1.0208             nan     0.1000    0.0102
##     10        1.0013             nan     0.1000    0.0057
##     20        0.9023             nan     0.1000    0.0013
##     40        0.8104             nan     0.1000   -0.0001
##     60        0.7623             nan     0.1000   -0.0032
##     80        0.7052             nan     0.1000    0.0001
##    100        0.6673             nan     0.1000   -0.0011
##    120        0.6335             nan     0.1000   -0.0016
##    140        0.5987             nan     0.1000   -0.0027
##    160        0.5719             nan     0.1000   -0.0017
##    180        0.5444             nan     0.1000   -0.0017
##    200        0.5161             nan     0.1000   -0.0011
##    220        0.4943             nan     0.1000   -0.0009
##    240        0.4736             nan     0.1000   -0.0006
##    260        0.4549             nan     0.1000   -0.0015
##    280        0.4386             nan     0.1000   -0.0010
##    300        0.4228             nan     0.1000   -0.0016
##    320        0.4065             nan     0.1000   -0.0006
##    340        0.3895             nan     0.1000   -0.0008
##    360        0.3764             nan     0.1000   -0.0003
##    380        0.3593             nan     0.1000   -0.0008
##    400        0.3458             nan     0.1000   -0.0013
##    420        0.3324             nan     0.1000   -0.0006
##    440        0.3181             nan     0.1000   -0.0006
##    460        0.3053             nan     0.1000   -0.0008
##    480        0.2914             nan     0.1000   -0.0002
##    500        0.2804             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2399             nan     0.1000    0.0238
##      2        1.1907             nan     0.1000    0.0178
##      3        1.1505             nan     0.1000    0.0170
##      4        1.1180             nan     0.1000    0.0151
##      5        1.0900             nan     0.1000    0.0123
##      6        1.0667             nan     0.1000    0.0072
##      7        1.0476             nan     0.1000    0.0078
##      8        1.0270             nan     0.1000    0.0070
##      9        1.0085             nan     0.1000    0.0053
##     10        0.9921             nan     0.1000    0.0040
##     20        0.8985             nan     0.1000    0.0010
##     40        0.8023             nan     0.1000   -0.0006
##     60        0.7365             nan     0.1000   -0.0010
##     80        0.6951             nan     0.1000   -0.0012
##    100        0.6596             nan     0.1000   -0.0028
##    120        0.6225             nan     0.1000   -0.0010
##    140        0.5959             nan     0.1000   -0.0012
##    160        0.5701             nan     0.1000   -0.0007
##    180        0.5437             nan     0.1000   -0.0006
##    200        0.5191             nan     0.1000   -0.0026
##    220        0.4961             nan     0.1000   -0.0003
##    240        0.4758             nan     0.1000   -0.0011
##    260        0.4544             nan     0.1000   -0.0008
##    280        0.4370             nan     0.1000   -0.0014
##    300        0.4203             nan     0.1000   -0.0004
##    320        0.4045             nan     0.1000   -0.0012
##    340        0.3893             nan     0.1000   -0.0011
##    360        0.3713             nan     0.1000   -0.0007
##    380        0.3571             nan     0.1000   -0.0003
##    400        0.3448             nan     0.1000   -0.0013
##    420        0.3310             nan     0.1000   -0.0010
##    440        0.3195             nan     0.1000   -0.0004
##    460        0.3066             nan     0.1000   -0.0009
##    480        0.2956             nan     0.1000   -0.0009
##    500        0.2842             nan     0.1000   -0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2316             nan     0.2000    0.0320
##      2        1.1764             nan     0.2000    0.0217
##      3        1.1432             nan     0.2000    0.0146
##      4        1.1174             nan     0.2000    0.0116
##      5        1.0939             nan     0.2000    0.0103
##      6        1.0682             nan     0.2000    0.0060
##      7        1.0482             nan     0.2000    0.0081
##      8        1.0342             nan     0.2000    0.0050
##      9        1.0201             nan     0.2000    0.0062
##     10        1.0068             nan     0.2000    0.0055
##     20        0.9299             nan     0.2000    0.0023
##     40        0.8673             nan     0.2000   -0.0004
##     60        0.8274             nan     0.2000   -0.0005
##     80        0.8119             nan     0.2000   -0.0004
##    100        0.7951             nan     0.2000   -0.0031
##    120        0.7816             nan     0.2000   -0.0019
##    140        0.7661             nan     0.2000   -0.0016
##    160        0.7540             nan     0.2000   -0.0019
##    180        0.7453             nan     0.2000   -0.0014
##    200        0.7361             nan     0.2000   -0.0010
##    220        0.7229             nan     0.2000   -0.0011
##    240        0.7153             nan     0.2000   -0.0021
##    260        0.7083             nan     0.2000   -0.0020
##    280        0.6984             nan     0.2000   -0.0023
##    300        0.6903             nan     0.2000   -0.0022
##    320        0.6868             nan     0.2000   -0.0033
##    340        0.6790             nan     0.2000   -0.0013
##    360        0.6753             nan     0.2000   -0.0028
##    380        0.6701             nan     0.2000   -0.0006
##    400        0.6673             nan     0.2000   -0.0013
##    420        0.6617             nan     0.2000   -0.0021
##    440        0.6553             nan     0.2000   -0.0029
##    460        0.6502             nan     0.2000   -0.0028
##    480        0.6420             nan     0.2000   -0.0031
##    500        0.6349             nan     0.2000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2229             nan     0.2000    0.0353
##      2        1.1764             nan     0.2000    0.0193
##      3        1.1463             nan     0.2000    0.0132
##      4        1.1188             nan     0.2000    0.0112
##      5        1.0916             nan     0.2000    0.0126
##      6        1.0706             nan     0.2000    0.0079
##      7        1.0460             nan     0.2000    0.0085
##      8        1.0279             nan     0.2000    0.0062
##      9        1.0146             nan     0.2000    0.0058
##     10        1.0069             nan     0.2000   -0.0025
##     20        0.9254             nan     0.2000    0.0009
##     40        0.8613             nan     0.2000   -0.0015
##     60        0.8320             nan     0.2000   -0.0013
##     80        0.8030             nan     0.2000   -0.0019
##    100        0.7891             nan     0.2000   -0.0024
##    120        0.7767             nan     0.2000   -0.0019
##    140        0.7664             nan     0.2000   -0.0032
##    160        0.7525             nan     0.2000   -0.0023
##    180        0.7424             nan     0.2000   -0.0039
##    200        0.7329             nan     0.2000   -0.0007
##    220        0.7226             nan     0.2000   -0.0005
##    240        0.7149             nan     0.2000   -0.0010
##    260        0.7048             nan     0.2000   -0.0022
##    280        0.6961             nan     0.2000   -0.0022
##    300        0.6919             nan     0.2000   -0.0035
##    320        0.6839             nan     0.2000   -0.0023
##    340        0.6756             nan     0.2000   -0.0015
##    360        0.6707             nan     0.2000   -0.0030
##    380        0.6653             nan     0.2000   -0.0021
##    400        0.6561             nan     0.2000   -0.0020
##    420        0.6514             nan     0.2000   -0.0032
##    440        0.6427             nan     0.2000   -0.0014
##    460        0.6360             nan     0.2000   -0.0012
##    480        0.6291             nan     0.2000   -0.0023
##    500        0.6251             nan     0.2000   -0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2259             nan     0.2000    0.0300
##      2        1.1870             nan     0.2000    0.0224
##      3        1.1522             nan     0.2000    0.0141
##      4        1.1206             nan     0.2000    0.0103
##      5        1.0966             nan     0.2000    0.0090
##      6        1.0739             nan     0.2000    0.0108
##      7        1.0541             nan     0.2000    0.0068
##      8        1.0356             nan     0.2000    0.0069
##      9        1.0179             nan     0.2000    0.0045
##     10        1.0049             nan     0.2000    0.0023
##     20        0.9269             nan     0.2000   -0.0010
##     40        0.8656             nan     0.2000   -0.0006
##     60        0.8291             nan     0.2000   -0.0001
##     80        0.8093             nan     0.2000   -0.0026
##    100        0.7997             nan     0.2000   -0.0033
##    120        0.7842             nan     0.2000   -0.0025
##    140        0.7721             nan     0.2000   -0.0021
##    160        0.7585             nan     0.2000   -0.0020
##    180        0.7469             nan     0.2000   -0.0025
##    200        0.7374             nan     0.2000   -0.0013
##    220        0.7256             nan     0.2000   -0.0016
##    240        0.7188             nan     0.2000   -0.0013
##    260        0.7124             nan     0.2000   -0.0012
##    280        0.7069             nan     0.2000    0.0001
##    300        0.7018             nan     0.2000   -0.0016
##    320        0.6951             nan     0.2000   -0.0011
##    340        0.6879             nan     0.2000   -0.0018
##    360        0.6816             nan     0.2000   -0.0014
##    380        0.6759             nan     0.2000   -0.0017
##    400        0.6668             nan     0.2000   -0.0016
##    420        0.6623             nan     0.2000   -0.0049
##    440        0.6549             nan     0.2000   -0.0020
##    460        0.6499             nan     0.2000   -0.0011
##    480        0.6475             nan     0.2000   -0.0019
##    500        0.6381             nan     0.2000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2167             nan     0.2000    0.0344
##      2        1.1613             nan     0.2000    0.0294
##      3        1.1108             nan     0.2000    0.0201
##      4        1.0784             nan     0.2000    0.0114
##      5        1.0550             nan     0.2000    0.0090
##      6        1.0344             nan     0.2000    0.0011
##      7        1.0142             nan     0.2000    0.0085
##      8        0.9963             nan     0.2000    0.0029
##      9        0.9762             nan     0.2000    0.0061
##     10        0.9638             nan     0.2000   -0.0011
##     20        0.8660             nan     0.2000    0.0016
##     40        0.7917             nan     0.2000   -0.0000
##     60        0.7464             nan     0.2000   -0.0034
##     80        0.7008             nan     0.2000   -0.0026
##    100        0.6612             nan     0.2000   -0.0009
##    120        0.6295             nan     0.2000   -0.0039
##    140        0.5985             nan     0.2000   -0.0025
##    160        0.5657             nan     0.2000   -0.0041
##    180        0.5356             nan     0.2000   -0.0015
##    200        0.5129             nan     0.2000   -0.0030
##    220        0.4848             nan     0.2000   -0.0014
##    240        0.4605             nan     0.2000   -0.0021
##    260        0.4416             nan     0.2000   -0.0010
##    280        0.4221             nan     0.2000   -0.0014
##    300        0.4099             nan     0.2000   -0.0019
##    320        0.3875             nan     0.2000   -0.0030
##    340        0.3677             nan     0.2000   -0.0014
##    360        0.3571             nan     0.2000   -0.0007
##    380        0.3408             nan     0.2000   -0.0006
##    400        0.3256             nan     0.2000   -0.0007
##    420        0.3090             nan     0.2000   -0.0004
##    440        0.3016             nan     0.2000   -0.0016
##    460        0.2911             nan     0.2000   -0.0009
##    480        0.2794             nan     0.2000   -0.0012
##    500        0.2685             nan     0.2000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2026             nan     0.2000    0.0391
##      2        1.1475             nan     0.2000    0.0253
##      3        1.0988             nan     0.2000    0.0189
##      4        1.0679             nan     0.2000    0.0123
##      5        1.0315             nan     0.2000    0.0150
##      6        1.0042             nan     0.2000    0.0104
##      7        0.9839             nan     0.2000    0.0054
##      8        0.9685             nan     0.2000    0.0026
##      9        0.9513             nan     0.2000    0.0028
##     10        0.9360             nan     0.2000    0.0043
##     20        0.8570             nan     0.2000    0.0015
##     40        0.7835             nan     0.2000   -0.0005
##     60        0.7464             nan     0.2000   -0.0031
##     80        0.7004             nan     0.2000   -0.0045
##    100        0.6631             nan     0.2000   -0.0017
##    120        0.6261             nan     0.2000   -0.0012
##    140        0.5904             nan     0.2000   -0.0031
##    160        0.5705             nan     0.2000   -0.0013
##    180        0.5489             nan     0.2000   -0.0017
##    200        0.5205             nan     0.2000   -0.0007
##    220        0.4933             nan     0.2000   -0.0011
##    240        0.4678             nan     0.2000   -0.0025
##    260        0.4451             nan     0.2000   -0.0032
##    280        0.4229             nan     0.2000   -0.0019
##    300        0.4039             nan     0.2000   -0.0008
##    320        0.3847             nan     0.2000   -0.0014
##    340        0.3718             nan     0.2000   -0.0012
##    360        0.3591             nan     0.2000   -0.0021
##    380        0.3435             nan     0.2000   -0.0014
##    400        0.3311             nan     0.2000   -0.0005
##    420        0.3173             nan     0.2000   -0.0012
##    440        0.3097             nan     0.2000   -0.0008
##    460        0.2976             nan     0.2000   -0.0021
##    480        0.2895             nan     0.2000   -0.0013
##    500        0.2771             nan     0.2000   -0.0008
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2080             nan     0.2000    0.0381
##      2        1.1467             nan     0.2000    0.0268
##      3        1.1022             nan     0.2000    0.0152
##      4        1.0598             nan     0.2000    0.0144
##      5        1.0321             nan     0.2000    0.0100
##      6        1.0038             nan     0.2000    0.0105
##      7        0.9852             nan     0.2000    0.0014
##      8        0.9677             nan     0.2000    0.0036
##      9        0.9529             nan     0.2000    0.0025
##     10        0.9379             nan     0.2000    0.0045
##     20        0.8624             nan     0.2000    0.0014
##     40        0.7884             nan     0.2000   -0.0028
##     60        0.7305             nan     0.2000   -0.0016
##     80        0.6906             nan     0.2000   -0.0012
##    100        0.6627             nan     0.2000   -0.0035
##    120        0.6256             nan     0.2000   -0.0041
##    140        0.5998             nan     0.2000   -0.0052
##    160        0.5572             nan     0.2000   -0.0023
##    180        0.5269             nan     0.2000    0.0003
##    200        0.5003             nan     0.2000   -0.0014
##    220        0.4825             nan     0.2000   -0.0016
##    240        0.4632             nan     0.2000   -0.0016
##    260        0.4410             nan     0.2000   -0.0034
##    280        0.4163             nan     0.2000   -0.0009
##    300        0.3999             nan     0.2000   -0.0009
##    320        0.3861             nan     0.2000   -0.0015
##    340        0.3671             nan     0.2000   -0.0007
##    360        0.3547             nan     0.2000   -0.0009
##    380        0.3428             nan     0.2000   -0.0016
##    400        0.3264             nan     0.2000   -0.0014
##    420        0.3140             nan     0.2000   -0.0011
##    440        0.3007             nan     0.2000   -0.0009
##    460        0.2903             nan     0.2000   -0.0006
##    480        0.2810             nan     0.2000   -0.0005
##    500        0.2706             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1891             nan     0.2000    0.0418
##      2        1.1308             nan     0.2000    0.0224
##      3        1.0811             nan     0.2000    0.0209
##      4        1.0398             nan     0.2000    0.0149
##      5        1.0074             nan     0.2000    0.0047
##      6        0.9826             nan     0.2000    0.0067
##      7        0.9592             nan     0.2000    0.0057
##      8        0.9373             nan     0.2000    0.0057
##      9        0.9202             nan     0.2000    0.0038
##     10        0.9063             nan     0.2000    0.0013
##     20        0.8114             nan     0.2000   -0.0019
##     40        0.7089             nan     0.2000   -0.0015
##     60        0.6327             nan     0.2000   -0.0029
##     80        0.5843             nan     0.2000   -0.0009
##    100        0.5323             nan     0.2000   -0.0025
##    120        0.4818             nan     0.2000   -0.0025
##    140        0.4398             nan     0.2000   -0.0014
##    160        0.4043             nan     0.2000   -0.0010
##    180        0.3761             nan     0.2000   -0.0018
##    200        0.3430             nan     0.2000   -0.0020
##    220        0.3190             nan     0.2000   -0.0017
##    240        0.2974             nan     0.2000   -0.0010
##    260        0.2770             nan     0.2000   -0.0021
##    280        0.2563             nan     0.2000   -0.0006
##    300        0.2403             nan     0.2000   -0.0017
##    320        0.2265             nan     0.2000   -0.0017
##    340        0.2124             nan     0.2000   -0.0009
##    360        0.1982             nan     0.2000   -0.0010
##    380        0.1842             nan     0.2000    0.0000
##    400        0.1749             nan     0.2000   -0.0013
##    420        0.1638             nan     0.2000   -0.0004
##    440        0.1557             nan     0.2000   -0.0010
##    460        0.1488             nan     0.2000   -0.0008
##    480        0.1397             nan     0.2000   -0.0001
##    500        0.1322             nan     0.2000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2050             nan     0.2000    0.0390
##      2        1.1255             nan     0.2000    0.0329
##      3        1.0773             nan     0.2000    0.0172
##      4        1.0318             nan     0.2000    0.0175
##      5        0.9958             nan     0.2000    0.0130
##      6        0.9693             nan     0.2000    0.0088
##      7        0.9441             nan     0.2000    0.0031
##      8        0.9285             nan     0.2000    0.0067
##      9        0.9137             nan     0.2000    0.0025
##     10        0.8978             nan     0.2000    0.0013
##     20        0.8067             nan     0.2000   -0.0012
##     40        0.7004             nan     0.2000   -0.0029
##     60        0.6217             nan     0.2000   -0.0026
##     80        0.5618             nan     0.2000   -0.0030
##    100        0.5248             nan     0.2000   -0.0042
##    120        0.4750             nan     0.2000   -0.0046
##    140        0.4403             nan     0.2000   -0.0009
##    160        0.4031             nan     0.2000   -0.0012
##    180        0.3682             nan     0.2000   -0.0007
##    200        0.3425             nan     0.2000   -0.0017
##    220        0.3184             nan     0.2000   -0.0014
##    240        0.2959             nan     0.2000   -0.0014
##    260        0.2746             nan     0.2000   -0.0020
##    280        0.2572             nan     0.2000   -0.0007
##    300        0.2411             nan     0.2000   -0.0028
##    320        0.2215             nan     0.2000   -0.0005
##    340        0.2082             nan     0.2000   -0.0015
##    360        0.1956             nan     0.2000   -0.0013
##    380        0.1829             nan     0.2000   -0.0012
##    400        0.1710             nan     0.2000   -0.0003
##    420        0.1611             nan     0.2000   -0.0000
##    440        0.1524             nan     0.2000   -0.0005
##    460        0.1439             nan     0.2000   -0.0013
##    480        0.1369             nan     0.2000   -0.0008
##    500        0.1288             nan     0.2000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1818             nan     0.2000    0.0475
##      2        1.1079             nan     0.2000    0.0321
##      3        1.0626             nan     0.2000    0.0206
##      4        1.0321             nan     0.2000    0.0093
##      5        0.9942             nan     0.2000    0.0159
##      6        0.9664             nan     0.2000    0.0053
##      7        0.9471             nan     0.2000    0.0040
##      8        0.9371             nan     0.2000   -0.0020
##      9        0.9174             nan     0.2000    0.0034
##     10        0.9085             nan     0.2000   -0.0062
##     20        0.8075             nan     0.2000   -0.0036
##     40        0.7012             nan     0.2000   -0.0036
##     60        0.6445             nan     0.2000   -0.0060
##     80        0.5794             nan     0.2000   -0.0022
##    100        0.5320             nan     0.2000   -0.0014
##    120        0.4886             nan     0.2000   -0.0030
##    140        0.4435             nan     0.2000   -0.0008
##    160        0.4099             nan     0.2000   -0.0028
##    180        0.3755             nan     0.2000   -0.0023
##    200        0.3458             nan     0.2000   -0.0020
##    220        0.3187             nan     0.2000   -0.0023
##    240        0.2937             nan     0.2000   -0.0008
##    260        0.2694             nan     0.2000   -0.0025
##    280        0.2525             nan     0.2000   -0.0022
##    300        0.2345             nan     0.2000   -0.0015
##    320        0.2177             nan     0.2000   -0.0007
##    340        0.2049             nan     0.2000   -0.0006
##    360        0.1924             nan     0.2000   -0.0007
##    380        0.1812             nan     0.2000   -0.0007
##    400        0.1693             nan     0.2000   -0.0011
##    420        0.1598             nan     0.2000   -0.0021
##    440        0.1473             nan     0.2000   -0.0003
##    460        0.1393             nan     0.2000   -0.0007
##    480        0.1296             nan     0.2000   -0.0004
##    500        0.1212             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2063             nan     0.3000    0.0450
##      2        1.1461             nan     0.3000    0.0245
##      3        1.1171             nan     0.3000    0.0101
##      4        1.0833             nan     0.3000    0.0142
##      5        1.0503             nan     0.3000    0.0134
##      6        1.0306             nan     0.3000    0.0053
##      7        1.0137             nan     0.3000    0.0063
##      8        0.9974             nan     0.3000    0.0046
##      9        0.9853             nan     0.3000    0.0010
##     10        0.9693             nan     0.3000    0.0027
##     20        0.9064             nan     0.3000   -0.0019
##     40        0.8477             nan     0.3000   -0.0030
##     60        0.8143             nan     0.3000   -0.0016
##     80        0.7931             nan     0.3000   -0.0041
##    100        0.7717             nan     0.3000   -0.0034
##    120        0.7584             nan     0.3000   -0.0024
##    140        0.7444             nan     0.3000   -0.0035
##    160        0.7269             nan     0.3000   -0.0006
##    180        0.7184             nan     0.3000   -0.0010
##    200        0.7037             nan     0.3000   -0.0019
##    220        0.6907             nan     0.3000   -0.0024
##    240        0.6826             nan     0.3000   -0.0023
##    260        0.6728             nan     0.3000   -0.0071
##    280        0.6572             nan     0.3000   -0.0001
##    300        0.6469             nan     0.3000   -0.0025
##    320        0.6398             nan     0.3000   -0.0035
##    340        0.6314             nan     0.3000   -0.0029
##    360        0.6251             nan     0.3000   -0.0036
##    380        0.6211             nan     0.3000   -0.0032
##    400        0.6154             nan     0.3000   -0.0035
##    420        0.6069             nan     0.3000   -0.0033
##    440        0.6033             nan     0.3000   -0.0038
##    460        0.5996             nan     0.3000   -0.0033
##    480        0.5965             nan     0.3000   -0.0054
##    500        0.5901             nan     0.3000   -0.0021
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2010             nan     0.3000    0.0381
##      2        1.1415             nan     0.3000    0.0187
##      3        1.1026             nan     0.3000    0.0135
##      4        1.0664             nan     0.3000    0.0144
##      5        1.0415             nan     0.3000    0.0123
##      6        1.0220             nan     0.3000    0.0014
##      7        0.9999             nan     0.3000    0.0072
##      8        0.9804             nan     0.3000    0.0035
##      9        0.9658             nan     0.3000    0.0044
##     10        0.9564             nan     0.3000    0.0002
##     20        0.8872             nan     0.3000   -0.0025
##     40        0.8412             nan     0.3000   -0.0006
##     60        0.8092             nan     0.3000   -0.0082
##     80        0.7864             nan     0.3000   -0.0033
##    100        0.7726             nan     0.3000   -0.0041
##    120        0.7559             nan     0.3000   -0.0023
##    140        0.7448             nan     0.3000   -0.0037
##    160        0.7316             nan     0.3000   -0.0041
##    180        0.7160             nan     0.3000   -0.0053
##    200        0.7028             nan     0.3000   -0.0037
##    220        0.6885             nan     0.3000   -0.0015
##    240        0.6826             nan     0.3000   -0.0025
##    260        0.6696             nan     0.3000   -0.0034
##    280        0.6592             nan     0.3000   -0.0013
##    300        0.6488             nan     0.3000   -0.0038
##    320        0.6397             nan     0.3000   -0.0018
##    340        0.6338             nan     0.3000   -0.0016
##    360        0.6259             nan     0.3000   -0.0016
##    380        0.6172             nan     0.3000   -0.0041
##    400        0.6079             nan     0.3000   -0.0033
##    420        0.5998             nan     0.3000   -0.0025
##    440        0.5930             nan     0.3000   -0.0039
##    460        0.5905             nan     0.3000   -0.0034
##    480        0.5880             nan     0.3000   -0.0041
##    500        0.5802             nan     0.3000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2031             nan     0.3000    0.0412
##      2        1.1475             nan     0.3000    0.0198
##      3        1.1100             nan     0.3000    0.0151
##      4        1.0760             nan     0.3000    0.0073
##      5        1.0401             nan     0.3000    0.0137
##      6        1.0218             nan     0.3000    0.0042
##      7        0.9974             nan     0.3000    0.0078
##      8        0.9826             nan     0.3000    0.0061
##      9        0.9694             nan     0.3000    0.0041
##     10        0.9607             nan     0.3000    0.0011
##     20        0.8827             nan     0.3000   -0.0008
##     40        0.8395             nan     0.3000   -0.0016
##     60        0.8009             nan     0.3000   -0.0010
##     80        0.7783             nan     0.3000   -0.0036
##    100        0.7566             nan     0.3000   -0.0025
##    120        0.7454             nan     0.3000   -0.0049
##    140        0.7319             nan     0.3000   -0.0023
##    160        0.7155             nan     0.3000   -0.0024
##    180        0.7030             nan     0.3000   -0.0027
##    200        0.6887             nan     0.3000   -0.0015
##    220        0.6756             nan     0.3000   -0.0022
##    240        0.6605             nan     0.3000   -0.0015
##    260        0.6478             nan     0.3000   -0.0006
##    280        0.6392             nan     0.3000   -0.0019
##    300        0.6322             nan     0.3000   -0.0011
##    320        0.6222             nan     0.3000   -0.0008
##    340        0.6102             nan     0.3000   -0.0010
##    360        0.6060             nan     0.3000   -0.0040
##    380        0.5970             nan     0.3000   -0.0046
##    400        0.5899             nan     0.3000   -0.0024
##    420        0.5847             nan     0.3000   -0.0050
##    440        0.5747             nan     0.3000   -0.0014
##    460        0.5710             nan     0.3000   -0.0020
##    480        0.5677             nan     0.3000   -0.0028
##    500        0.5650             nan     0.3000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1808             nan     0.3000    0.0589
##      2        1.1047             nan     0.3000    0.0312
##      3        1.0520             nan     0.3000    0.0183
##      4        1.0048             nan     0.3000    0.0204
##      5        0.9769             nan     0.3000    0.0070
##      6        0.9541             nan     0.3000    0.0036
##      7        0.9355             nan     0.3000    0.0052
##      8        0.9251             nan     0.3000   -0.0087
##      9        0.9080             nan     0.3000    0.0004
##     10        0.9052             nan     0.3000   -0.0087
##     20        0.8225             nan     0.3000   -0.0012
##     40        0.7202             nan     0.3000   -0.0062
##     60        0.6711             nan     0.3000   -0.0056
##     80        0.6206             nan     0.3000   -0.0069
##    100        0.5753             nan     0.3000   -0.0011
##    120        0.5347             nan     0.3000   -0.0003
##    140        0.4962             nan     0.3000   -0.0022
##    160        0.4569             nan     0.3000   -0.0020
##    180        0.4282             nan     0.3000   -0.0054
##    200        0.4033             nan     0.3000   -0.0022
##    220        0.3829             nan     0.3000   -0.0020
##    240        0.3684             nan     0.3000   -0.0021
##    260        0.3542             nan     0.3000   -0.0038
##    280        0.3307             nan     0.3000   -0.0023
##    300        0.3145             nan     0.3000   -0.0022
##    320        0.2989             nan     0.3000   -0.0091
##    340        0.2785             nan     0.3000   -0.0011
##    360        0.2626             nan     0.3000   -0.0018
##    380        0.2497             nan     0.3000   -0.0025
##    400        0.2344             nan     0.3000   -0.0010
##    420        0.2234             nan     0.3000   -0.0023
##    440        0.2083             nan     0.3000   -0.0021
##    460        0.1936             nan     0.3000   -0.0006
##    480        0.1849             nan     0.3000   -0.0010
##    500        0.1764             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1639             nan     0.3000    0.0580
##      2        1.0921             nan     0.3000    0.0354
##      3        1.0474             nan     0.3000    0.0149
##      4        0.9972             nan     0.3000    0.0190
##      5        0.9733             nan     0.3000   -0.0008
##      6        0.9440             nan     0.3000    0.0070
##      7        0.9323             nan     0.3000   -0.0006
##      8        0.9135             nan     0.3000    0.0042
##      9        0.8980             nan     0.3000    0.0037
##     10        0.8845             nan     0.3000    0.0022
##     20        0.8140             nan     0.3000   -0.0076
##     40        0.7324             nan     0.3000   -0.0002
##     60        0.6726             nan     0.3000   -0.0035
##     80        0.6498             nan     0.3000   -0.0527
##    100        0.5510             nan     0.3000   -0.0041
##    120        0.5132             nan     0.3000   -0.0013
##    140        0.4798             nan     0.3000   -0.0023
##    160        0.4480             nan     0.3000   -0.0024
##    180        0.4174             nan     0.3000   -0.0018
##    200        0.3905             nan     0.3000   -0.0024
##    220        0.3591             nan     0.3000   -0.0032
##    240        0.3392             nan     0.3000   -0.0037
##    260        0.3241             nan     0.3000   -0.0006
##    280        0.3061             nan     0.3000   -0.0040
##    300        0.2922             nan     0.3000   -0.0017
##    320        0.2754             nan     0.3000   -0.0012
##    340        0.2574             nan     0.3000   -0.0028
##    360        0.2413             nan     0.3000   -0.0010
##    380        0.2298             nan     0.3000   -0.0018
##    400        0.2163             nan     0.3000   -0.0015
##    420        0.2053             nan     0.3000   -0.0019
##    440        0.1935             nan     0.3000   -0.0016
##    460        0.1830             nan     0.3000   -0.0005
##    480        0.1753             nan     0.3000   -0.0017
##    500        0.1647             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1686             nan     0.3000    0.0514
##      2        1.1001             nan     0.3000    0.0317
##      3        1.0493             nan     0.3000    0.0212
##      4        1.0197             nan     0.3000    0.0053
##      5        0.9896             nan     0.3000    0.0062
##      6        0.9630             nan     0.3000    0.0097
##      7        0.9463             nan     0.3000    0.0048
##      8        0.9331             nan     0.3000   -0.0050
##      9        0.9192             nan     0.3000   -0.0003
##     10        0.9038             nan     0.3000    0.0020
##     20        0.8344             nan     0.3000   -0.0013
##     40        0.7323             nan     0.3000   -0.0065
##     60        0.6700             nan     0.3000   -0.0045
##     80        0.6233             nan     0.3000   -0.0038
##    100        0.5752             nan     0.3000   -0.0006
##    120        0.5197             nan     0.3000   -0.0014
##    140        0.4869             nan     0.3000   -0.0041
##    160        0.4556             nan     0.3000   -0.0046
##    180        0.4234             nan     0.3000   -0.0047
##    200        0.4022             nan     0.3000   -0.0029
##    220        0.3868             nan     0.3000   -0.0037
##    240        0.3597             nan     0.3000   -0.0042
##    260        0.3381             nan     0.3000   -0.0016
##    280        0.3132             nan     0.3000   -0.0021
##    300        0.2939             nan     0.3000   -0.0005
##    320        0.2774             nan     0.3000   -0.0019
##    340        0.2651             nan     0.3000   -0.0032
##    360        0.2508             nan     0.3000   -0.0020
##    380        0.2366             nan     0.3000   -0.0030
##    400        0.2213             nan     0.3000   -0.0008
##    420        0.2101             nan     0.3000   -0.0017
##    440        0.1989             nan     0.3000   -0.0014
##    460        0.1900             nan     0.3000   -0.0027
##    480        0.1820             nan     0.3000   -0.0022
##    500        0.1706             nan     0.3000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1731             nan     0.3000    0.0565
##      2        1.0974             nan     0.3000    0.0270
##      3        1.0169             nan     0.3000    0.0370
##      4        0.9890             nan     0.3000    0.0055
##      5        0.9581             nan     0.3000    0.0019
##      6        0.9266             nan     0.3000    0.0120
##      7        0.9087             nan     0.3000   -0.0028
##      8        0.8921             nan     0.3000   -0.0024
##      9        0.8778             nan     0.3000    0.0010
##     10        0.8683             nan     0.3000   -0.0069
##     20        0.7720             nan     0.3000   -0.0053
##     40        0.6442             nan     0.3000   -0.0022
##     60        0.5469             nan     0.3000   -0.0024
##     80           inf             nan     0.3000       nan
##    100           inf             nan     0.3000       nan
##    120           inf             nan     0.3000       nan
##    140           inf             nan     0.3000       nan
##    160           inf             nan     0.3000       nan
##    180           inf             nan     0.3000       nan
##    200           inf             nan     0.3000       nan
##    220           inf             nan     0.3000       nan
##    240           inf             nan     0.3000       nan
##    260           inf             nan     0.3000       nan
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1664             nan     0.3000    0.0543
##      2        1.0712             nan     0.3000    0.0388
##      3        1.0175             nan     0.3000    0.0216
##      4        0.9846             nan     0.3000    0.0051
##      5        0.9583             nan     0.3000    0.0003
##      6        0.9312             nan     0.3000    0.0079
##      7        0.9129             nan     0.3000   -0.0007
##      8        0.8940             nan     0.3000   -0.0024
##      9        0.8792             nan     0.3000    0.0011
##     10        0.8651             nan     0.3000   -0.0044
##     20        0.7710             nan     0.3000   -0.0099
##     40        0.6613             nan     0.3000   -0.0085
##     60        0.5938             nan     0.3000   -0.0058
##     80        0.5135             nan     0.3000   -0.0036
##    100        0.4488             nan     0.3000   -0.0038
##    120        0.3954             nan     0.3000   -0.0046
##    140        0.3439             nan     0.3000   -0.0008
##    160        0.3064             nan     0.3000   -0.0036
##    180        0.2721             nan     0.3000   -0.0014
##    200        0.2480             nan     0.3000   -0.0013
##    220        0.2199             nan     0.3000   -0.0007
##    240        0.2000             nan     0.3000   -0.0009
##    260        0.1814             nan     0.3000   -0.0017
##    280        0.1664             nan     0.3000   -0.0009
##    300        0.1508             nan     0.3000   -0.0004
##    320        0.1389             nan     0.3000   -0.0008
##    340        0.1261             nan     0.3000   -0.0020
##    360        0.1143             nan     0.3000   -0.0004
##    380        0.1055             nan     0.3000   -0.0007
##    400        0.0985             nan     0.3000   -0.0008
##    420        0.0909             nan     0.3000   -0.0008
##    440        0.0845             nan     0.3000   -0.0003
##    460        0.0802             nan     0.3000   -0.0007
##    480        0.0749             nan     0.3000   -0.0006
##    500        0.0682             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1644             nan     0.3000    0.0599
##      2        1.0754             nan     0.3000    0.0393
##      3        1.0250             nan     0.3000    0.0158
##      4        0.9852             nan     0.3000    0.0094
##      5        0.9557             nan     0.3000    0.0063
##      6        0.9267             nan     0.3000    0.0023
##      7        0.9028             nan     0.3000    0.0044
##      8        0.8873             nan     0.3000   -0.0013
##      9        0.8754             nan     0.3000   -0.0043
##     10        0.8695             nan     0.3000   -0.0042
##     20        0.7786             nan     0.3000   -0.0058
##     40        0.6823             nan     0.3000   -0.0009
##     60        0.5898             nan     0.3000   -0.0049
##     80        0.5117             nan     0.3000   -0.0041
##    100        0.4442             nan     0.3000   -0.0030
##    120        0.3951             nan     0.3000   -0.0033
##    140        0.3618             nan     0.3000   -0.0046
##    160        0.3161             nan     0.3000   -0.0045
##    180        0.2865             nan     0.3000   -0.0014
##    200        0.2593             nan     0.3000   -0.0028
##    220        0.2319             nan     0.3000   -0.0031
##    240        0.2099             nan     0.3000   -0.0025
##    260        0.1897             nan     0.3000   -0.0022
##    280        0.1750             nan     0.3000   -0.0006
##    300        0.1574             nan     0.3000   -0.0005
##    320        0.1455             nan     0.3000   -0.0021
##    340        0.1329             nan     0.3000   -0.0011
##    360        0.1227             nan     0.3000   -0.0011
##    380        0.1118             nan     0.3000   -0.0005
##    400        0.1016             nan     0.3000   -0.0002
##    420        0.0938             nan     0.3000   -0.0010
##    440        0.0856             nan     0.3000   -0.0004
##    460        0.0792             nan     0.3000   -0.0015
##    480        0.0744             nan     0.3000   -0.0008
##    500        0.0691             nan     0.3000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1693             nan     0.5000    0.0504
##      2        1.1053             nan     0.5000    0.0283
##      3        1.0604             nan     0.5000    0.0177
##      4        1.0144             nan     0.5000    0.0140
##      5        0.9877             nan     0.5000   -0.0027
##      6        0.9641             nan     0.5000    0.0050
##      7        0.9533             nan     0.5000   -0.0009
##      8        0.9379             nan     0.5000    0.0067
##      9        0.9355             nan     0.5000   -0.0055
##     10        0.9269             nan     0.5000    0.0021
##     20        0.8707             nan     0.5000   -0.0046
##     40        0.8156             nan     0.5000   -0.0155
##     60        0.7671             nan     0.5000   -0.0099
##     80        0.7424             nan     0.5000   -0.0012
##    100        0.7199             nan     0.5000   -0.0031
##    120        1.1378             nan     0.5000   -0.0026
##    140        1.1256             nan     0.5000   -0.0007
##    160        1.1151             nan     0.5000    0.0000
##    180        1.1090             nan     0.5000   -0.0001
##    200        1.1015             nan     0.5000    0.0001
##    220        1.0973             nan     0.5000   -0.0014
##    240        1.0882             nan     0.5000   -0.0042
##    260        1.0799             nan     0.5000   -0.0006
##    280        1.0744             nan     0.5000   -0.0001
##    300        1.0685             nan     0.5000   -0.0000
##    320        1.0657             nan     0.5000   -0.0000
##    340        1.0583             nan     0.5000    0.0014
##    360        1.0478             nan     0.5000   -0.0011
##    380        1.0425             nan     0.5000   -0.0047
##    400        1.0453             nan     0.5000    0.0001
##    420        1.0388             nan     0.5000   -0.0040
##    440        1.0294             nan     0.5000    0.0001
##    460        1.0203             nan     0.5000   -0.0005
##    480        1.0231             nan     0.5000    0.0000
##    500        1.0195             nan     0.5000   -0.0046
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1610             nan     0.5000    0.0722
##      2        1.1090             nan     0.5000    0.0228
##      3        1.0572             nan     0.5000    0.0192
##      4        1.0093             nan     0.5000    0.0124
##      5        0.9755             nan     0.5000    0.0110
##      6        0.9681             nan     0.5000   -0.0044
##      7        0.9548             nan     0.5000    0.0010
##      8        0.9418             nan     0.5000    0.0007
##      9        0.9343             nan     0.5000   -0.0045
##     10        0.9263             nan     0.5000   -0.0009
##     20        0.8777             nan     0.5000   -0.0015
##     40        0.8057             nan     0.5000   -0.0024
##     60        0.7792             nan     0.5000   -0.0092
##     80        0.7743             nan     0.5000   -0.0093
##    100        0.7471             nan     0.5000   -0.0042
##    120        0.7252             nan     0.5000   -0.0086
##    140        0.7077             nan     0.5000   -0.0061
##    160        0.6982             nan     0.5000   -0.0017
##    180        0.6729             nan     0.5000   -0.0003
##    200        0.6513             nan     0.5000    0.0004
##    220        0.6273             nan     0.5000   -0.0101
##    240        0.6103             nan     0.5000   -0.0053
##    260        0.6062             nan     0.5000   -0.0066
##    280        0.6035             nan     0.5000   -0.0128
##    300        0.5857             nan     0.5000   -0.0023
##    320        0.5774             nan     0.5000   -0.0067
##    340        0.5655             nan     0.5000   -0.0072
##    360        0.5630             nan     0.5000   -0.0061
##    380        0.5569             nan     0.5000   -0.0047
##    400        0.5604             nan     0.5000   -0.0062
##    420        0.5482             nan     0.5000   -0.0040
##    440        0.5278             nan     0.5000   -0.0026
##    460        0.5212             nan     0.5000   -0.0067
##    480        0.5158             nan     0.5000   -0.0012
##    500        0.5086             nan     0.5000   -0.0028
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1656             nan     0.5000    0.0703
##      2        1.1074             nan     0.5000    0.0222
##      3        1.0508             nan     0.5000    0.0190
##      4        1.0375             nan     0.5000   -0.0020
##      5        1.0005             nan     0.5000    0.0090
##      6        0.9642             nan     0.5000    0.0182
##      7        0.9418             nan     0.5000    0.0058
##      8        0.9327             nan     0.5000   -0.0007
##      9        0.9272             nan     0.5000   -0.0049
##     10        0.9158             nan     0.5000    0.0006
##     20        0.8471             nan     0.5000    0.0022
##     40        0.8009             nan     0.5000    0.0015
##     60        0.7717             nan     0.5000   -0.0059
##     80        0.7658             nan     0.5000   -0.0017
##    100        0.7369             nan     0.5000    0.0021
##    120        0.7331             nan     0.5000   -0.0099
##    140        0.6976             nan     0.5000   -0.0024
##    160        0.6880             nan     0.5000   -0.0085
##    180        0.6733             nan     0.5000   -0.0050
##    200        0.6671             nan     0.5000   -0.0072
##    220        0.6496             nan     0.5000   -0.0050
##    240        0.6362             nan     0.5000   -0.0027
##    260        0.6216             nan     0.5000   -0.0048
##    280        0.6206             nan     0.5000   -0.0119
##    300        0.6081             nan     0.5000   -0.0070
##    320        0.5933             nan     0.5000   -0.0049
##    340        0.5891             nan     0.5000   -0.0020
##    360        0.5776             nan     0.5000   -0.0043
##    380        0.5714             nan     0.5000   -0.0050
##    400        0.5742             nan     0.5000   -0.0064
##    420        0.5608             nan     0.5000   -0.0024
##    440        0.5584             nan     0.5000   -0.0087
##    460        0.5537             nan     0.5000   -0.0056
##    480        0.5450             nan     0.5000   -0.0017
##    500        0.5366             nan     0.5000   -0.0063
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1302             nan     0.5000    0.0795
##      2        1.0581             nan     0.5000    0.0186
##      3        0.9991             nan     0.5000    0.0173
##      4        0.9443             nan     0.5000    0.0132
##      5        0.9236             nan     0.5000    0.0007
##      6        0.9018             nan     0.5000   -0.0051
##      7        0.8757             nan     0.5000    0.0058
##      8        0.8607             nan     0.5000   -0.0008
##      9        0.8495             nan     0.5000   -0.0070
##     10        0.8456             nan     0.5000   -0.0122
##     20        0.7878             nan     0.5000   -0.0017
##     40           inf             nan     0.5000       nan
##     60           inf             nan     0.5000       nan
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1340             nan     0.5000    0.0667
##      2        1.0541             nan     0.5000    0.0338
##      3        0.9878             nan     0.5000    0.0267
##      4        0.9471             nan     0.5000    0.0068
##      5        0.9260             nan     0.5000   -0.0013
##      6        0.9086             nan     0.5000   -0.0040
##      7        0.8981             nan     0.5000   -0.0053
##      8        0.8732             nan     0.5000    0.0085
##      9        0.8515             nan     0.5000    0.0076
##     10        0.8366             nan     0.5000   -0.0017
##     20        0.7779             nan     0.5000   -0.0090
##     40        0.6720             nan     0.5000   -0.0011
##     60        0.5910             nan     0.5000   -0.0093
##     80        0.5403             nan     0.5000   -0.0092
##    100        0.4812             nan     0.5000   -0.0023
##    120        0.4276             nan     0.5000   -0.0101
##    140        0.3778             nan     0.5000   -0.0015
##    160        0.3481             nan     0.5000   -0.0022
##    180        0.3279             nan     0.5000   -0.0100
##    200        0.2960             nan     0.5000   -0.0057
##    220        0.2792             nan     0.5000   -0.0001
##    240        0.2488             nan     0.5000   -0.0028
##    260        0.2267             nan     0.5000   -0.0044
##    280        0.2123             nan     0.5000   -0.0043
##    300        0.1985             nan     0.5000   -0.0027
##    320        0.1808             nan     0.5000   -0.0054
##    340        0.1670             nan     0.5000   -0.0037
##    360        0.1509             nan     0.5000   -0.0023
##    380        0.1417             nan     0.5000   -0.0032
##    400        0.1305             nan     0.5000   -0.0009
##    420        0.1195             nan     0.5000   -0.0013
##    440        0.1095             nan     0.5000   -0.0025
##    460        0.1013             nan     0.5000   -0.0004
##    480        0.0938             nan     0.5000   -0.0005
##    500        0.0880             nan     0.5000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1148             nan     0.5000    0.0853
##      2        1.0245             nan     0.5000    0.0414
##      3        0.9778             nan     0.5000    0.0134
##      4        0.9404             nan     0.5000    0.0058
##      5        0.9185             nan     0.5000   -0.0005
##      6        0.8973             nan     0.5000    0.0051
##      7        0.8760             nan     0.5000    0.0046
##      8        0.8628             nan     0.5000   -0.0102
##      9        0.8457             nan     0.5000   -0.0044
##     10        0.8323             nan     0.5000   -0.0080
##     20        0.7757             nan     0.5000   -0.0020
##     40        0.6974             nan     0.5000   -0.0102
##     60        0.6214             nan     0.5000    0.0004
##     80        0.5477             nan     0.5000   -0.0102
##    100        0.4994             nan     0.5000   -0.0123
##    120        0.4375             nan     0.5000   -0.0042
##    140        0.3946             nan     0.5000   -0.0055
##    160        0.3677             nan     0.5000   -0.0038
##    180        0.3334             nan     0.5000   -0.0109
##    200        0.2993             nan     0.5000   -0.0075
##    220        0.2646             nan     0.5000   -0.0051
##    240        0.2366             nan     0.5000   -0.0049
##    260        0.2218             nan     0.5000   -0.0048
##    280        0.2011             nan     0.5000   -0.0012
##    300        0.1862             nan     0.5000   -0.0039
##    320        0.1717             nan     0.5000   -0.0033
##    340        0.1557             nan     0.5000   -0.0009
##    360        0.1456             nan     0.5000   -0.0019
##    380        0.1319             nan     0.5000   -0.0004
##    400        0.1240             nan     0.5000   -0.0022
##    420        0.1137             nan     0.5000   -0.0024
##    440        0.1038             nan     0.5000    0.0001
##    460        0.0956             nan     0.5000   -0.0012
##    480        0.0888             nan     0.5000   -0.0019
##    500        0.0833             nan     0.5000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0994             nan     0.5000    0.0732
##      2        0.9947             nan     0.5000    0.0357
##      3        0.9356             nan     0.5000    0.0203
##      4        0.9074             nan     0.5000    0.0019
##      5        0.8821             nan     0.5000   -0.0022
##      6        0.8625             nan     0.5000   -0.0042
##      7        0.8429             nan     0.5000   -0.0004
##      8        0.8355             nan     0.5000   -0.0102
##      9        0.8220             nan     0.5000   -0.0113
##     10        0.8229             nan     0.5000   -0.0197
##     20        0.7154             nan     0.5000   -0.0051
##     40        0.5707             nan     0.5000   -0.0015
##     60        0.4747             nan     0.5000   -0.0093
##     80        0.3874             nan     0.5000    0.0012
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1009             nan     0.5000    0.1006
##      2        1.0358             nan     0.5000    0.0189
##      3        0.9757             nan     0.5000    0.0077
##      4        0.9417             nan     0.5000   -0.0103
##      5        0.8968             nan     0.5000    0.0075
##      6        0.8765             nan     0.5000   -0.0014
##      7        0.8691             nan     0.5000   -0.0198
##      8        0.8427             nan     0.5000   -0.0065
##      9        0.8081             nan     0.5000    0.0098
##     10        0.7999             nan     0.5000   -0.0085
##     20        0.7236             nan     0.5000   -0.0057
##     40        0.5600             nan     0.5000   -0.0075
##     60        0.4944             nan     0.5000   -0.0086
##     80        0.4134             nan     0.5000   -0.0102
##    100        0.3301             nan     0.5000   -0.0155
##    120 1303502062914.8240             nan     0.5000   -0.0136
##    140 1303502062914.7742             nan     0.5000   -0.0026
##    160 1303502062914.5544             nan     0.5000   -0.0063
##    180 1303502062914.5261             nan     0.5000   -0.0016
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0859             nan     0.5000    0.0899
##      2        0.9978             nan     0.5000    0.0309
##      3        0.9470             nan     0.5000    0.0063
##      4        0.9172             nan     0.5000   -0.0059
##      5        0.8926             nan     0.5000   -0.0085
##      6        0.8731             nan     0.5000    0.0003
##      7        0.8600             nan     0.5000   -0.0097
##      8        0.8515             nan     0.5000   -0.0144
##      9        0.8473             nan     0.5000   -0.0157
##     10        0.8403             nan     0.5000   -0.0208
##     20        0.7580             nan     0.5000   -0.0153
##     40        0.6131             nan     0.5000   -0.0089
##     60        0.4810             nan     0.5000   -0.0018
##     80        0.3981             nan     0.5000   -0.0041
##    100        0.3461             nan     0.5000   -0.0022
##    120        0.2865             nan     0.5000   -0.0054
##    140        0.2376             nan     0.5000   -0.0014
##    160        0.1952             nan     0.5000   -0.0032
##    180        0.1632             nan     0.5000   -0.0016
##    200        0.1403             nan     0.5000   -0.0022
##    220        0.1270             nan     0.5000   -0.0012
##    240        0.1080             nan     0.5000   -0.0019
##    260        0.0952             nan     0.5000   -0.0017
##    280        0.0842             nan     0.5000   -0.0010
##    300        0.0745             nan     0.5000   -0.0012
##    320        0.0656             nan     0.5000   -0.0016
##    340        0.0575             nan     0.5000   -0.0004
##    360        0.0509             nan     0.5000   -0.0010
##    380        0.0463             nan     0.5000   -0.0003
##    400        0.0386             nan     0.5000   -0.0003
##    420        0.0356             nan     0.5000   -0.0001
##    440        0.0311             nan     0.5000   -0.0002
##    460        0.0285             nan     0.5000   -0.0006
##    480        0.0249             nan     0.5000   -0.0002
##    500        0.0213             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1309             nan     1.0000    0.0367
##      2        1.0473             nan     1.0000    0.0297
##      3        1.0225             nan     1.0000   -0.0124
##      4        0.9950             nan     1.0000    0.0097
##      5        0.9825             nan     1.0000   -0.0019
##      6        0.9880             nan     1.0000   -0.0178
##      7        0.9801             nan     1.0000   -0.0082
##      8        0.9637             nan     1.0000   -0.0115
##      9        0.9680             nan     1.0000   -0.0236
##     10        0.9985             nan     1.0000   -0.0519
##     20        0.9726             nan     1.0000   -0.0129
##     40        1.3207             nan     1.0000   -0.0199
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1181             nan     1.0000    0.0894
##      2        1.0595             nan     1.0000    0.0120
##      3        1.0036             nan     1.0000    0.0206
##      4        1.0045             nan     1.0000   -0.0137
##      5        1.0023             nan     1.0000   -0.0176
##      6        0.9766             nan     1.0000    0.0067
##      7        0.9625             nan     1.0000   -0.0052
##      8        0.9433             nan     1.0000    0.0059
##      9        0.9339             nan     1.0000   -0.0046
##     10        0.9200             nan     1.0000   -0.0082
##     20        0.9273             nan     1.0000   -0.0362
##     40        0.8884             nan     1.0000   -0.0052
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1348             nan     1.0000    0.0860
##      2        1.0704             nan     1.0000    0.0180
##      3        0.9984             nan     1.0000    0.0212
##      4        0.9552             nan     1.0000   -0.0004
##      5        0.9343             nan     1.0000   -0.0014
##      6        0.9225             nan     1.0000    0.0004
##      7        0.9222             nan     1.0000   -0.0181
##      8        0.9138             nan     1.0000   -0.0061
##      9        0.9645             nan     1.0000   -0.0515
##     10        0.9726             nan     1.0000   -0.0290
##     20        1.2712             nan     1.0000   -0.0049
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100 3647384380668679330886068.0000             nan     1.0000   -0.0008
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0726             nan     1.0000    0.1000
##      2        1.0014             nan     1.0000    0.0113
##      3        1.0265             nan     1.0000   -0.0499
##      4        1.0201             nan     1.0000   -0.0221
##      5        1.0225             nan     1.0000   -0.0377
##      6        1.0010             nan     1.0000   -0.0129
##      7        0.9602             nan     1.0000   -0.0101
##      8        0.9543             nan     1.0000   -0.0263
##      9        0.9354             nan     1.0000   -0.0062
##     10        0.9039             nan     1.0000   -0.0007
##     20        0.8749             nan     1.0000   -0.0185
##     40 102937901.8866             nan     1.0000   -0.0032
##     60 102937901.7232             nan     1.0000   -0.0104
##     80 102937901.7241             nan     1.0000   -0.0529
##    100           inf             nan     1.0000      -inf
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0714             nan     1.0000    0.0966
##      2        1.0074             nan     1.0000    0.0108
##      3        0.9714             nan     1.0000   -0.0007
##      4        0.9479             nan     1.0000   -0.0167
##      5        0.9406             nan     1.0000   -0.0186
##      6        0.9070             nan     1.0000   -0.0008
##      7        0.9141             nan     1.0000   -0.0453
##      8        0.9037             nan     1.0000   -0.0221
##      9        0.8802             nan     1.0000   -0.0176
##     10        0.8743             nan     1.0000   -0.0218
##     20        1.2974             nan     1.0000   -0.0476
##     40           inf             nan     1.0000       nan
##     60 546431245524951957524.0000             nan     1.0000   -0.0000
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0954             nan     1.0000    0.0674
##      2        1.0049             nan     1.0000    0.0297
##      3        0.9707             nan     1.0000    0.0030
##      4        0.9409             nan     1.0000   -0.0084
##      5        0.9579             nan     1.0000   -0.0418
##      6        0.9570             nan     1.0000   -0.0288
##      7        0.9802             nan     1.0000   -0.0557
##      8        0.9522             nan     1.0000   -0.0004
##      9        0.9395             nan     1.0000   -0.0197
##     10        0.9388             nan     1.0000   -0.0340
##     20        0.9449             nan     1.0000   -0.0365
##     40        0.8449             nan     1.0000   -0.0105
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0161             nan     1.0000    0.1046
##      2        0.9527             nan     1.0000    0.0123
##      3        0.9179             nan     1.0000   -0.0047
##      4        0.9106             nan     1.0000   -0.0336
##      5        0.9129             nan     1.0000   -0.0379
##      6        0.9077             nan     1.0000   -0.0411
##      7        0.8429             nan     1.0000    0.0039
##      8        0.8338             nan     1.0000   -0.0276
##      9        0.8851             nan     1.0000   -0.0820
##     10        0.9325             nan     1.0000   -0.0942
##     20        4.4509             nan     1.0000    0.0354
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0749             nan     1.0000    0.0447
##      2        1.0581             nan     1.0000   -0.0444
##      3        0.9998             nan     1.0000   -0.0004
##      4        1.0610             nan     1.0000   -0.0847
##      5        1.0375             nan     1.0000   -0.0315
##      6        1.0217             nan     1.0000   -0.0298
##      7        1.0469             nan     1.0000   -0.0850
##      8        1.0059             nan     1.0000   -0.0199
##      9        1.0423             nan     1.0000   -0.0730
##     10        1.0650             nan     1.0000   -0.0734
##     20 73544255.9217             nan     1.0000 -36718988.0668
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0404             nan     1.0000    0.0925
##      2        0.9940             nan     1.0000   -0.0034
##      3        0.9820             nan     1.0000   -0.0218
##      4        0.9741             nan     1.0000   -0.0371
##      5        0.9843             nan     1.0000   -0.0591
##      6        0.9850             nan     1.0000   -0.0444
##      7        1.0001             nan     1.0000   -0.0565
##      8        0.9667             nan     1.0000   -0.0109
##      9        1.0577             nan     1.0000   -0.1141
##     10        1.3607             nan     1.0000   -0.3619
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0001
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2863             nan     0.0010    0.0002
##     40        1.2795             nan     0.0010    0.0001
##     60        1.2731             nan     0.0010    0.0001
##     80        1.2666             nan     0.0010    0.0002
##    100        1.2603             nan     0.0010    0.0001
##    120        1.2547             nan     0.0010    0.0001
##    140        1.2490             nan     0.0010    0.0001
##    160        1.2434             nan     0.0010    0.0001
##    180        1.2380             nan     0.0010    0.0001
##    200        1.2328             nan     0.0010    0.0001
##    220        1.2276             nan     0.0010    0.0001
##    240        1.2226             nan     0.0010    0.0001
##    260        1.2179             nan     0.0010    0.0001
##    280        1.2132             nan     0.0010    0.0001
##    300        1.2086             nan     0.0010    0.0001
##    320        1.2042             nan     0.0010    0.0001
##    340        1.1997             nan     0.0010    0.0001
##    360        1.1954             nan     0.0010    0.0001
##    380        1.1912             nan     0.0010    0.0001
##    400        1.1871             nan     0.0010    0.0001
##    420        1.1831             nan     0.0010    0.0001
##    440        1.1792             nan     0.0010    0.0001
##    460        1.1753             nan     0.0010    0.0001
##    480        1.1716             nan     0.0010    0.0001
##    500        1.1679             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2862             nan     0.0010    0.0001
##     40        1.2795             nan     0.0010    0.0002
##     60        1.2730             nan     0.0010    0.0001
##     80        1.2665             nan     0.0010    0.0001
##    100        1.2603             nan     0.0010    0.0001
##    120        1.2543             nan     0.0010    0.0001
##    140        1.2490             nan     0.0010    0.0001
##    160        1.2434             nan     0.0010    0.0001
##    180        1.2381             nan     0.0010    0.0001
##    200        1.2330             nan     0.0010    0.0001
##    220        1.2278             nan     0.0010    0.0001
##    240        1.2229             nan     0.0010    0.0001
##    260        1.2182             nan     0.0010    0.0001
##    280        1.2134             nan     0.0010    0.0001
##    300        1.2088             nan     0.0010    0.0001
##    320        1.2043             nan     0.0010    0.0001
##    340        1.2000             nan     0.0010    0.0001
##    360        1.1958             nan     0.0010    0.0001
##    380        1.1916             nan     0.0010    0.0001
##    400        1.1876             nan     0.0010    0.0001
##    420        1.1837             nan     0.0010    0.0001
##    440        1.1797             nan     0.0010    0.0001
##    460        1.1759             nan     0.0010    0.0001
##    480        1.1721             nan     0.0010    0.0001
##    500        1.1684             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0001
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0001
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0001
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2863             nan     0.0010    0.0001
##     40        1.2796             nan     0.0010    0.0002
##     60        1.2729             nan     0.0010    0.0002
##     80        1.2665             nan     0.0010    0.0001
##    100        1.2604             nan     0.0010    0.0001
##    120        1.2544             nan     0.0010    0.0001
##    140        1.2486             nan     0.0010    0.0001
##    160        1.2432             nan     0.0010    0.0001
##    180        1.2376             nan     0.0010    0.0001
##    200        1.2323             nan     0.0010    0.0001
##    220        1.2272             nan     0.0010    0.0001
##    240        1.2224             nan     0.0010    0.0001
##    260        1.2175             nan     0.0010    0.0001
##    280        1.2129             nan     0.0010    0.0001
##    300        1.2085             nan     0.0010    0.0001
##    320        1.2039             nan     0.0010    0.0001
##    340        1.1996             nan     0.0010    0.0001
##    360        1.1953             nan     0.0010    0.0001
##    380        1.1913             nan     0.0010    0.0001
##    400        1.1873             nan     0.0010    0.0001
##    420        1.1834             nan     0.0010    0.0001
##    440        1.1796             nan     0.0010    0.0001
##    460        1.1757             nan     0.0010    0.0001
##    480        1.1719             nan     0.0010    0.0001
##    500        1.1681             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2887             nan     0.0010    0.0002
##     20        1.2839             nan     0.0010    0.0002
##     40        1.2749             nan     0.0010    0.0002
##     60        1.2662             nan     0.0010    0.0002
##     80        1.2578             nan     0.0010    0.0002
##    100        1.2497             nan     0.0010    0.0002
##    120        1.2415             nan     0.0010    0.0002
##    140        1.2339             nan     0.0010    0.0002
##    160        1.2263             nan     0.0010    0.0002
##    180        1.2194             nan     0.0010    0.0001
##    200        1.2122             nan     0.0010    0.0001
##    220        1.2053             nan     0.0010    0.0001
##    240        1.1986             nan     0.0010    0.0001
##    260        1.1920             nan     0.0010    0.0001
##    280        1.1858             nan     0.0010    0.0001
##    300        1.1797             nan     0.0010    0.0001
##    320        1.1736             nan     0.0010    0.0001
##    340        1.1677             nan     0.0010    0.0001
##    360        1.1619             nan     0.0010    0.0001
##    380        1.1565             nan     0.0010    0.0001
##    400        1.1511             nan     0.0010    0.0001
##    420        1.1460             nan     0.0010    0.0001
##    440        1.1406             nan     0.0010    0.0001
##    460        1.1356             nan     0.0010    0.0001
##    480        1.1306             nan     0.0010    0.0001
##    500        1.1258             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2892             nan     0.0010    0.0002
##     10        1.2888             nan     0.0010    0.0002
##     20        1.2842             nan     0.0010    0.0002
##     40        1.2754             nan     0.0010    0.0002
##     60        1.2665             nan     0.0010    0.0002
##     80        1.2580             nan     0.0010    0.0002
##    100        1.2502             nan     0.0010    0.0002
##    120        1.2423             nan     0.0010    0.0002
##    140        1.2345             nan     0.0010    0.0001
##    160        1.2271             nan     0.0010    0.0001
##    180        1.2198             nan     0.0010    0.0002
##    200        1.2127             nan     0.0010    0.0001
##    220        1.2058             nan     0.0010    0.0001
##    240        1.1991             nan     0.0010    0.0001
##    260        1.1926             nan     0.0010    0.0001
##    280        1.1865             nan     0.0010    0.0001
##    300        1.1805             nan     0.0010    0.0001
##    320        1.1745             nan     0.0010    0.0001
##    340        1.1687             nan     0.0010    0.0001
##    360        1.1628             nan     0.0010    0.0001
##    380        1.1572             nan     0.0010    0.0001
##    400        1.1519             nan     0.0010    0.0001
##    420        1.1464             nan     0.0010    0.0001
##    440        1.1413             nan     0.0010    0.0001
##    460        1.1361             nan     0.0010    0.0001
##    480        1.1311             nan     0.0010    0.0001
##    500        1.1262             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2841             nan     0.0010    0.0002
##     40        1.2752             nan     0.0010    0.0002
##     60        1.2665             nan     0.0010    0.0002
##     80        1.2580             nan     0.0010    0.0002
##    100        1.2496             nan     0.0010    0.0002
##    120        1.2415             nan     0.0010    0.0002
##    140        1.2338             nan     0.0010    0.0002
##    160        1.2264             nan     0.0010    0.0002
##    180        1.2194             nan     0.0010    0.0001
##    200        1.2123             nan     0.0010    0.0001
##    220        1.2055             nan     0.0010    0.0001
##    240        1.1989             nan     0.0010    0.0001
##    260        1.1922             nan     0.0010    0.0001
##    280        1.1859             nan     0.0010    0.0001
##    300        1.1796             nan     0.0010    0.0001
##    320        1.1737             nan     0.0010    0.0001
##    340        1.1680             nan     0.0010    0.0001
##    360        1.1623             nan     0.0010    0.0001
##    380        1.1568             nan     0.0010    0.0001
##    400        1.1513             nan     0.0010    0.0001
##    420        1.1461             nan     0.0010    0.0001
##    440        1.1410             nan     0.0010    0.0001
##    460        1.1359             nan     0.0010    0.0001
##    480        1.1309             nan     0.0010    0.0001
##    500        1.1261             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0003
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2894             nan     0.0010    0.0003
##      8        1.2889             nan     0.0010    0.0002
##      9        1.2884             nan     0.0010    0.0002
##     10        1.2878             nan     0.0010    0.0003
##     20        1.2825             nan     0.0010    0.0002
##     40        1.2722             nan     0.0010    0.0002
##     60        1.2622             nan     0.0010    0.0002
##     80        1.2523             nan     0.0010    0.0002
##    100        1.2426             nan     0.0010    0.0002
##    120        1.2332             nan     0.0010    0.0002
##    140        1.2243             nan     0.0010    0.0002
##    160        1.2156             nan     0.0010    0.0002
##    180        1.2069             nan     0.0010    0.0002
##    200        1.1987             nan     0.0010    0.0002
##    220        1.1909             nan     0.0010    0.0002
##    240        1.1833             nan     0.0010    0.0001
##    260        1.1756             nan     0.0010    0.0002
##    280        1.1685             nan     0.0010    0.0002
##    300        1.1615             nan     0.0010    0.0001
##    320        1.1545             nan     0.0010    0.0001
##    340        1.1477             nan     0.0010    0.0001
##    360        1.1413             nan     0.0010    0.0001
##    380        1.1351             nan     0.0010    0.0001
##    400        1.1288             nan     0.0010    0.0002
##    420        1.1225             nan     0.0010    0.0001
##    440        1.1165             nan     0.0010    0.0001
##    460        1.1109             nan     0.0010    0.0001
##    480        1.1053             nan     0.0010    0.0001
##    500        1.0999             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0003
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2907             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2886             nan     0.0010    0.0002
##     10        1.2881             nan     0.0010    0.0002
##     20        1.2827             nan     0.0010    0.0002
##     40        1.2721             nan     0.0010    0.0002
##     60        1.2620             nan     0.0010    0.0002
##     80        1.2522             nan     0.0010    0.0002
##    100        1.2427             nan     0.0010    0.0002
##    120        1.2334             nan     0.0010    0.0002
##    140        1.2243             nan     0.0010    0.0001
##    160        1.2160             nan     0.0010    0.0002
##    180        1.2075             nan     0.0010    0.0002
##    200        1.1993             nan     0.0010    0.0001
##    220        1.1914             nan     0.0010    0.0002
##    240        1.1834             nan     0.0010    0.0002
##    260        1.1759             nan     0.0010    0.0002
##    280        1.1684             nan     0.0010    0.0002
##    300        1.1615             nan     0.0010    0.0002
##    320        1.1546             nan     0.0010    0.0001
##    340        1.1477             nan     0.0010    0.0001
##    360        1.1412             nan     0.0010    0.0001
##    380        1.1351             nan     0.0010    0.0001
##    400        1.1287             nan     0.0010    0.0001
##    420        1.1227             nan     0.0010    0.0001
##    440        1.1165             nan     0.0010    0.0001
##    460        1.1109             nan     0.0010    0.0001
##    480        1.1054             nan     0.0010    0.0001
##    500        1.0997             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0003
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2917             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0003
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0002
##      9        1.2885             nan     0.0010    0.0003
##     10        1.2880             nan     0.0010    0.0002
##     20        1.2825             nan     0.0010    0.0003
##     40        1.2717             nan     0.0010    0.0002
##     60        1.2614             nan     0.0010    0.0002
##     80        1.2515             nan     0.0010    0.0002
##    100        1.2423             nan     0.0010    0.0002
##    120        1.2330             nan     0.0010    0.0002
##    140        1.2239             nan     0.0010    0.0002
##    160        1.2153             nan     0.0010    0.0002
##    180        1.2068             nan     0.0010    0.0002
##    200        1.1987             nan     0.0010    0.0002
##    220        1.1907             nan     0.0010    0.0002
##    240        1.1831             nan     0.0010    0.0002
##    260        1.1756             nan     0.0010    0.0002
##    280        1.1683             nan     0.0010    0.0001
##    300        1.1615             nan     0.0010    0.0001
##    320        1.1547             nan     0.0010    0.0001
##    340        1.1480             nan     0.0010    0.0001
##    360        1.1416             nan     0.0010    0.0002
##    380        1.1353             nan     0.0010    0.0001
##    400        1.1290             nan     0.0010    0.0001
##    420        1.1231             nan     0.0010    0.0001
##    440        1.1172             nan     0.0010    0.0001
##    460        1.1117             nan     0.0010    0.0001
##    480        1.1057             nan     0.0010    0.0001
##    500        1.1005             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2556             nan     0.1000    0.0153
##      2        1.2249             nan     0.1000    0.0134
##      3        1.2043             nan     0.1000    0.0079
##      4        1.1816             nan     0.1000    0.0084
##      5        1.1616             nan     0.1000    0.0083
##      6        1.1445             nan     0.1000    0.0073
##      7        1.1298             nan     0.1000    0.0062
##      8        1.1138             nan     0.1000    0.0076
##      9        1.1012             nan     0.1000    0.0033
##     10        1.0905             nan     0.1000    0.0046
##     20        1.0005             nan     0.1000    0.0015
##     40        0.9197             nan     0.1000    0.0001
##     60        0.8748             nan     0.1000   -0.0002
##     80        0.8491             nan     0.1000    0.0001
##    100        0.8303             nan     0.1000   -0.0005
##    120        0.8153             nan     0.1000   -0.0002
##    140        0.8057             nan     0.1000   -0.0008
##    160        0.7962             nan     0.1000   -0.0015
##    180        0.7904             nan     0.1000   -0.0007
##    200        0.7827             nan     0.1000   -0.0004
##    220        0.7766             nan     0.1000   -0.0016
##    240        0.7682             nan     0.1000   -0.0010
##    260        0.7614             nan     0.1000   -0.0024
##    280        0.7563             nan     0.1000   -0.0015
##    300        0.7505             nan     0.1000   -0.0009
##    320        0.7446             nan     0.1000   -0.0007
##    340        0.7392             nan     0.1000   -0.0011
##    360        0.7348             nan     0.1000   -0.0012
##    380        0.7311             nan     0.1000   -0.0013
##    400        0.7262             nan     0.1000   -0.0009
##    420        0.7220             nan     0.1000   -0.0003
##    440        0.7187             nan     0.1000   -0.0007
##    460        0.7138             nan     0.1000   -0.0013
##    480        0.7070             nan     0.1000   -0.0010
##    500        0.7042             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2589             nan     0.1000    0.0155
##      2        1.2320             nan     0.1000    0.0110
##      3        1.2027             nan     0.1000    0.0113
##      4        1.1845             nan     0.1000    0.0073
##      5        1.1642             nan     0.1000    0.0084
##      6        1.1456             nan     0.1000    0.0070
##      7        1.1282             nan     0.1000    0.0078
##      8        1.1116             nan     0.1000    0.0056
##      9        1.0966             nan     0.1000    0.0057
##     10        1.0860             nan     0.1000    0.0045
##     20        0.9962             nan     0.1000    0.0023
##     40        0.9226             nan     0.1000   -0.0001
##     60        0.8796             nan     0.1000    0.0004
##     80        0.8551             nan     0.1000   -0.0013
##    100        0.8343             nan     0.1000   -0.0004
##    120        0.8216             nan     0.1000   -0.0011
##    140        0.8113             nan     0.1000   -0.0005
##    160        0.8036             nan     0.1000   -0.0007
##    180        0.7929             nan     0.1000   -0.0010
##    200        0.7859             nan     0.1000   -0.0010
##    220        0.7787             nan     0.1000   -0.0010
##    240        0.7706             nan     0.1000   -0.0008
##    260        0.7631             nan     0.1000   -0.0001
##    280        0.7553             nan     0.1000   -0.0008
##    300        0.7490             nan     0.1000   -0.0005
##    320        0.7438             nan     0.1000   -0.0007
##    340        0.7395             nan     0.1000   -0.0015
##    360        0.7320             nan     0.1000   -0.0010
##    380        0.7260             nan     0.1000   -0.0010
##    400        0.7201             nan     0.1000   -0.0010
##    420        0.7138             nan     0.1000   -0.0009
##    440        0.7094             nan     0.1000   -0.0008
##    460        0.7054             nan     0.1000   -0.0004
##    480        0.6999             nan     0.1000   -0.0012
##    500        0.6947             nan     0.1000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2569             nan     0.1000    0.0158
##      2        1.2327             nan     0.1000    0.0116
##      3        1.2075             nan     0.1000    0.0113
##      4        1.1886             nan     0.1000    0.0090
##      5        1.1689             nan     0.1000    0.0065
##      6        1.1525             nan     0.1000    0.0074
##      7        1.1380             nan     0.1000    0.0047
##      8        1.1231             nan     0.1000    0.0057
##      9        1.1091             nan     0.1000    0.0067
##     10        1.0939             nan     0.1000    0.0057
##     20        1.0028             nan     0.1000    0.0018
##     40        0.9197             nan     0.1000    0.0000
##     60        0.8805             nan     0.1000   -0.0003
##     80        0.8533             nan     0.1000    0.0000
##    100        0.8358             nan     0.1000   -0.0005
##    120        0.8193             nan     0.1000   -0.0009
##    140        0.8077             nan     0.1000   -0.0011
##    160        0.7983             nan     0.1000   -0.0014
##    180        0.7890             nan     0.1000   -0.0023
##    200        0.7804             nan     0.1000   -0.0017
##    220        0.7739             nan     0.1000   -0.0009
##    240        0.7666             nan     0.1000   -0.0007
##    260        0.7604             nan     0.1000   -0.0018
##    280        0.7547             nan     0.1000   -0.0009
##    300        0.7494             nan     0.1000   -0.0007
##    320        0.7449             nan     0.1000   -0.0010
##    340        0.7399             nan     0.1000   -0.0007
##    360        0.7345             nan     0.1000   -0.0010
##    380        0.7279             nan     0.1000   -0.0006
##    400        0.7244             nan     0.1000   -0.0007
##    420        0.7195             nan     0.1000   -0.0008
##    440        0.7141             nan     0.1000   -0.0005
##    460        0.7113             nan     0.1000   -0.0007
##    480        0.7074             nan     0.1000   -0.0016
##    500        0.7042             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2472             nan     0.1000    0.0196
##      2        1.2117             nan     0.1000    0.0173
##      3        1.1779             nan     0.1000    0.0155
##      4        1.1490             nan     0.1000    0.0112
##      5        1.1263             nan     0.1000    0.0081
##      6        1.1028             nan     0.1000    0.0102
##      7        1.0822             nan     0.1000    0.0082
##      8        1.0665             nan     0.1000    0.0063
##      9        1.0520             nan     0.1000    0.0065
##     10        1.0335             nan     0.1000    0.0059
##     20        0.9409             nan     0.1000    0.0015
##     40        0.8544             nan     0.1000   -0.0007
##     60        0.8092             nan     0.1000   -0.0002
##     80        0.7754             nan     0.1000   -0.0005
##    100        0.7449             nan     0.1000    0.0002
##    120        0.7236             nan     0.1000   -0.0005
##    140        0.6999             nan     0.1000   -0.0010
##    160        0.6803             nan     0.1000   -0.0018
##    180        0.6569             nan     0.1000   -0.0007
##    200        0.6353             nan     0.1000   -0.0016
##    220        0.6180             nan     0.1000   -0.0017
##    240        0.6039             nan     0.1000   -0.0018
##    260        0.5883             nan     0.1000   -0.0010
##    280        0.5762             nan     0.1000   -0.0011
##    300        0.5622             nan     0.1000   -0.0011
##    320        0.5470             nan     0.1000   -0.0006
##    340        0.5354             nan     0.1000   -0.0010
##    360        0.5216             nan     0.1000   -0.0011
##    380        0.5087             nan     0.1000   -0.0011
##    400        0.4944             nan     0.1000   -0.0002
##    420        0.4838             nan     0.1000   -0.0004
##    440        0.4697             nan     0.1000   -0.0010
##    460        0.4590             nan     0.1000   -0.0003
##    480        0.4487             nan     0.1000   -0.0002
##    500        0.4389             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2513             nan     0.1000    0.0196
##      2        1.2161             nan     0.1000    0.0157
##      3        1.1878             nan     0.1000    0.0133
##      4        1.1558             nan     0.1000    0.0100
##      5        1.1358             nan     0.1000    0.0072
##      6        1.1098             nan     0.1000    0.0116
##      7        1.0912             nan     0.1000    0.0062
##      8        1.0739             nan     0.1000    0.0084
##      9        1.0544             nan     0.1000    0.0067
##     10        1.0409             nan     0.1000    0.0029
##     20        0.9391             nan     0.1000    0.0013
##     40        0.8553             nan     0.1000   -0.0009
##     60        0.8121             nan     0.1000   -0.0005
##     80        0.7780             nan     0.1000   -0.0015
##    100        0.7510             nan     0.1000   -0.0004
##    120        0.7290             nan     0.1000   -0.0008
##    140        0.7111             nan     0.1000   -0.0020
##    160        0.6847             nan     0.1000   -0.0010
##    180        0.6682             nan     0.1000   -0.0023
##    200        0.6493             nan     0.1000   -0.0019
##    220        0.6330             nan     0.1000   -0.0022
##    240        0.6131             nan     0.1000   -0.0009
##    260        0.5993             nan     0.1000   -0.0017
##    280        0.5835             nan     0.1000   -0.0012
##    300        0.5693             nan     0.1000   -0.0009
##    320        0.5492             nan     0.1000   -0.0022
##    340        0.5373             nan     0.1000   -0.0008
##    360        0.5278             nan     0.1000   -0.0006
##    380        0.5136             nan     0.1000   -0.0009
##    400        0.5034             nan     0.1000   -0.0015
##    420        0.4894             nan     0.1000   -0.0004
##    440        0.4768             nan     0.1000   -0.0007
##    460        0.4657             nan     0.1000   -0.0010
##    480        0.4555             nan     0.1000   -0.0006
##    500        0.4433             nan     0.1000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2482             nan     0.1000    0.0189
##      2        1.2095             nan     0.1000    0.0168
##      3        1.1747             nan     0.1000    0.0137
##      4        1.1445             nan     0.1000    0.0126
##      5        1.1205             nan     0.1000    0.0114
##      6        1.0979             nan     0.1000    0.0108
##      7        1.0818             nan     0.1000    0.0052
##      8        1.0609             nan     0.1000    0.0074
##      9        1.0440             nan     0.1000    0.0063
##     10        1.0321             nan     0.1000    0.0045
##     20        0.9311             nan     0.1000    0.0003
##     40        0.8469             nan     0.1000    0.0001
##     60        0.7988             nan     0.1000   -0.0022
##     80        0.7634             nan     0.1000   -0.0011
##    100        0.7367             nan     0.1000   -0.0002
##    120        0.7122             nan     0.1000   -0.0006
##    140        0.6909             nan     0.1000   -0.0001
##    160        0.6733             nan     0.1000   -0.0013
##    180        0.6536             nan     0.1000   -0.0012
##    200        0.6354             nan     0.1000   -0.0008
##    220        0.6185             nan     0.1000   -0.0000
##    240        0.6049             nan     0.1000   -0.0007
##    260        0.5882             nan     0.1000   -0.0008
##    280        0.5747             nan     0.1000   -0.0005
##    300        0.5565             nan     0.1000   -0.0004
##    320        0.5420             nan     0.1000   -0.0008
##    340        0.5310             nan     0.1000   -0.0007
##    360        0.5197             nan     0.1000   -0.0018
##    380        0.5072             nan     0.1000   -0.0013
##    400        0.4935             nan     0.1000   -0.0008
##    420        0.4838             nan     0.1000   -0.0008
##    440        0.4729             nan     0.1000   -0.0010
##    460        0.4639             nan     0.1000   -0.0001
##    480        0.4524             nan     0.1000   -0.0016
##    500        0.4398             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2355             nan     0.1000    0.0268
##      2        1.1914             nan     0.1000    0.0174
##      3        1.1525             nan     0.1000    0.0163
##      4        1.1181             nan     0.1000    0.0137
##      5        1.0899             nan     0.1000    0.0133
##      6        1.0679             nan     0.1000    0.0061
##      7        1.0467             nan     0.1000    0.0064
##      8        1.0258             nan     0.1000    0.0077
##      9        1.0090             nan     0.1000    0.0067
##     10        0.9932             nan     0.1000    0.0038
##     20        0.8897             nan     0.1000   -0.0002
##     40        0.7980             nan     0.1000   -0.0012
##     60        0.7421             nan     0.1000   -0.0008
##     80        0.6925             nan     0.1000   -0.0024
##    100        0.6543             nan     0.1000   -0.0014
##    120        0.6175             nan     0.1000   -0.0034
##    140        0.5821             nan     0.1000   -0.0008
##    160        0.5513             nan     0.1000   -0.0012
##    180        0.5272             nan     0.1000   -0.0011
##    200        0.5064             nan     0.1000   -0.0014
##    220        0.4851             nan     0.1000   -0.0012
##    240        0.4614             nan     0.1000   -0.0007
##    260        0.4399             nan     0.1000   -0.0019
##    280        0.4230             nan     0.1000   -0.0014
##    300        0.4056             nan     0.1000   -0.0003
##    320        0.3909             nan     0.1000   -0.0008
##    340        0.3759             nan     0.1000   -0.0005
##    360        0.3612             nan     0.1000   -0.0009
##    380        0.3495             nan     0.1000   -0.0008
##    400        0.3361             nan     0.1000   -0.0013
##    420        0.3220             nan     0.1000   -0.0009
##    440        0.3109             nan     0.1000   -0.0006
##    460        0.2986             nan     0.1000   -0.0001
##    480        0.2875             nan     0.1000   -0.0002
##    500        0.2763             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2395             nan     0.1000    0.0251
##      2        1.1995             nan     0.1000    0.0138
##      3        1.1638             nan     0.1000    0.0176
##      4        1.1349             nan     0.1000    0.0090
##      5        1.1028             nan     0.1000    0.0108
##      6        1.0804             nan     0.1000    0.0080
##      7        1.0538             nan     0.1000    0.0100
##      8        1.0367             nan     0.1000    0.0064
##      9        1.0171             nan     0.1000    0.0068
##     10        1.0012             nan     0.1000    0.0049
##     20        0.8970             nan     0.1000   -0.0003
##     40        0.8025             nan     0.1000   -0.0020
##     60        0.7465             nan     0.1000    0.0003
##     80        0.7031             nan     0.1000   -0.0025
##    100        0.6696             nan     0.1000   -0.0022
##    120        0.6359             nan     0.1000   -0.0016
##    140        0.6076             nan     0.1000   -0.0015
##    160        0.5819             nan     0.1000    0.0001
##    180        0.5563             nan     0.1000   -0.0012
##    200        0.5290             nan     0.1000   -0.0017
##    220        0.5070             nan     0.1000   -0.0009
##    240        0.4828             nan     0.1000   -0.0012
##    260        0.4630             nan     0.1000   -0.0017
##    280        0.4430             nan     0.1000   -0.0010
##    300        0.4217             nan     0.1000   -0.0016
##    320        0.4065             nan     0.1000   -0.0012
##    340        0.3907             nan     0.1000   -0.0008
##    360        0.3781             nan     0.1000   -0.0014
##    380        0.3641             nan     0.1000   -0.0004
##    400        0.3500             nan     0.1000   -0.0011
##    420        0.3361             nan     0.1000   -0.0009
##    440        0.3227             nan     0.1000   -0.0011
##    460        0.3124             nan     0.1000   -0.0005
##    480        0.3006             nan     0.1000   -0.0002
##    500        0.2907             nan     0.1000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2484             nan     0.1000    0.0193
##      2        1.2075             nan     0.1000    0.0180
##      3        1.1678             nan     0.1000    0.0137
##      4        1.1352             nan     0.1000    0.0129
##      5        1.1087             nan     0.1000    0.0098
##      6        1.0774             nan     0.1000    0.0147
##      7        1.0578             nan     0.1000    0.0056
##      8        1.0372             nan     0.1000    0.0062
##      9        1.0173             nan     0.1000    0.0068
##     10        0.9979             nan     0.1000    0.0086
##     20        0.8925             nan     0.1000    0.0003
##     40        0.8019             nan     0.1000    0.0002
##     60        0.7512             nan     0.1000   -0.0030
##     80        0.7068             nan     0.1000   -0.0013
##    100        0.6711             nan     0.1000   -0.0024
##    120        0.6403             nan     0.1000   -0.0006
##    140        0.6155             nan     0.1000   -0.0017
##    160        0.5850             nan     0.1000   -0.0016
##    180        0.5548             nan     0.1000   -0.0012
##    200        0.5282             nan     0.1000   -0.0002
##    220        0.5038             nan     0.1000   -0.0007
##    240        0.4838             nan     0.1000   -0.0011
##    260        0.4601             nan     0.1000   -0.0009
##    280        0.4374             nan     0.1000   -0.0003
##    300        0.4224             nan     0.1000   -0.0009
##    320        0.4051             nan     0.1000   -0.0015
##    340        0.3874             nan     0.1000   -0.0007
##    360        0.3710             nan     0.1000   -0.0015
##    380        0.3578             nan     0.1000   -0.0009
##    400        0.3458             nan     0.1000   -0.0010
##    420        0.3303             nan     0.1000   -0.0008
##    440        0.3179             nan     0.1000   -0.0006
##    460        0.3060             nan     0.1000   -0.0005
##    480        0.2936             nan     0.1000   -0.0012
##    500        0.2825             nan     0.1000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2234             nan     0.2000    0.0286
##      2        1.1873             nan     0.2000    0.0146
##      3        1.1497             nan     0.2000    0.0144
##      4        1.1172             nan     0.2000    0.0161
##      5        1.0869             nan     0.2000    0.0131
##      6        1.0671             nan     0.2000    0.0059
##      7        1.0432             nan     0.2000    0.0099
##      8        1.0275             nan     0.2000    0.0041
##      9        1.0177             nan     0.2000    0.0024
##     10        1.0086             nan     0.2000   -0.0028
##     20        0.9204             nan     0.2000    0.0014
##     40        0.8572             nan     0.2000   -0.0010
##     60        0.8279             nan     0.2000   -0.0025
##     80        0.8049             nan     0.2000   -0.0019
##    100        0.7864             nan     0.2000   -0.0025
##    120        0.7743             nan     0.2000   -0.0020
##    140        0.7599             nan     0.2000   -0.0015
##    160        0.7519             nan     0.2000   -0.0034
##    180        0.7387             nan     0.2000   -0.0022
##    200        0.7297             nan     0.2000   -0.0011
##    220        0.7196             nan     0.2000   -0.0018
##    240        0.7086             nan     0.2000   -0.0016
##    260        0.7025             nan     0.2000   -0.0015
##    280        0.6944             nan     0.2000   -0.0015
##    300        0.6883             nan     0.2000   -0.0023
##    320        0.6781             nan     0.2000   -0.0018
##    340        0.6723             nan     0.2000   -0.0011
##    360        0.6672             nan     0.2000   -0.0019
##    380        0.6612             nan     0.2000   -0.0032
##    400        0.6530             nan     0.2000   -0.0003
##    420        0.6469             nan     0.2000   -0.0012
##    440        0.6408             nan     0.2000   -0.0019
##    460        0.6372             nan     0.2000   -0.0021
##    480        0.6351             nan     0.2000   -0.0016
##    500        0.6293             nan     0.2000   -0.0017
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2259             nan     0.2000    0.0348
##      2        1.1767             nan     0.2000    0.0204
##      3        1.1419             nan     0.2000    0.0149
##      4        1.1166             nan     0.2000    0.0101
##      5        1.0895             nan     0.2000    0.0128
##      6        1.0678             nan     0.2000    0.0079
##      7        1.0485             nan     0.2000    0.0071
##      8        1.0310             nan     0.2000    0.0067
##      9        1.0180             nan     0.2000    0.0043
##     10        1.0009             nan     0.2000    0.0066
##     20        0.9205             nan     0.2000    0.0004
##     40        0.8560             nan     0.2000    0.0003
##     60        0.8259             nan     0.2000   -0.0020
##     80        0.8003             nan     0.2000   -0.0012
##    100        0.7862             nan     0.2000   -0.0016
##    120        0.7729             nan     0.2000   -0.0018
##    140        0.7607             nan     0.2000   -0.0039
##    160        0.7497             nan     0.2000   -0.0018
##    180        0.7364             nan     0.2000    0.0001
##    200        0.7273             nan     0.2000   -0.0005
##    220        0.7185             nan     0.2000   -0.0029
##    240        0.7102             nan     0.2000   -0.0019
##    260        0.6992             nan     0.2000   -0.0028
##    280        0.6902             nan     0.2000   -0.0018
##    300        0.6807             nan     0.2000   -0.0015
##    320        0.6737             nan     0.2000   -0.0027
##    340        0.6679             nan     0.2000   -0.0019
##    360        0.6619             nan     0.2000   -0.0029
##    380        0.6524             nan     0.2000   -0.0024
##    400        0.6455             nan     0.2000   -0.0007
##    420        0.6393             nan     0.2000   -0.0022
##    440        0.6340             nan     0.2000   -0.0010
##    460        0.6277             nan     0.2000   -0.0010
##    480        0.6252             nan     0.2000   -0.0015
##    500        0.6203             nan     0.2000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2296             nan     0.2000    0.0310
##      2        1.1923             nan     0.2000    0.0132
##      3        1.1507             nan     0.2000    0.0211
##      4        1.1277             nan     0.2000    0.0063
##      5        1.0944             nan     0.2000    0.0117
##      6        1.0705             nan     0.2000    0.0092
##      7        1.0445             nan     0.2000    0.0117
##      8        1.0274             nan     0.2000    0.0044
##      9        1.0153             nan     0.2000    0.0015
##     10        0.9996             nan     0.2000    0.0065
##     20        0.9160             nan     0.2000    0.0010
##     40        0.8463             nan     0.2000   -0.0003
##     60        0.8180             nan     0.2000   -0.0010
##     80        0.7983             nan     0.2000   -0.0009
##    100        0.7747             nan     0.2000   -0.0039
##    120        0.7600             nan     0.2000   -0.0014
##    140        0.7492             nan     0.2000   -0.0014
##    160        0.7385             nan     0.2000   -0.0037
##    180        0.7317             nan     0.2000   -0.0011
##    200        0.7245             nan     0.2000   -0.0026
##    220        0.7128             nan     0.2000   -0.0007
##    240        0.7054             nan     0.2000   -0.0011
##    260        0.6968             nan     0.2000   -0.0012
##    280        0.6897             nan     0.2000   -0.0024
##    300        0.6803             nan     0.2000   -0.0039
##    320        0.6717             nan     0.2000   -0.0038
##    340        0.6670             nan     0.2000   -0.0010
##    360        0.6611             nan     0.2000   -0.0013
##    380        0.6549             nan     0.2000   -0.0007
##    400        0.6497             nan     0.2000   -0.0027
##    420        0.6420             nan     0.2000   -0.0005
##    440        0.6382             nan     0.2000   -0.0003
##    460        0.6296             nan     0.2000   -0.0024
##    480        0.6249             nan     0.2000   -0.0001
##    500        0.6207             nan     0.2000   -0.0029
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2043             nan     0.2000    0.0330
##      2        1.1431             nan     0.2000    0.0270
##      3        1.0975             nan     0.2000    0.0200
##      4        1.0626             nan     0.2000    0.0103
##      5        1.0239             nan     0.2000    0.0134
##      6        1.0047             nan     0.2000    0.0042
##      7        0.9805             nan     0.2000    0.0108
##      8        0.9649             nan     0.2000    0.0036
##      9        0.9440             nan     0.2000    0.0052
##     10        0.9321             nan     0.2000    0.0012
##     20        0.8482             nan     0.2000    0.0013
##     40        0.7808             nan     0.2000   -0.0037
##     60        0.7188             nan     0.2000   -0.0043
##     80        0.6811             nan     0.2000   -0.0008
##    100        0.6400             nan     0.2000   -0.0030
##    120        0.6071             nan     0.2000   -0.0020
##    140        0.5904             nan     0.2000   -0.0021
##    160        0.5591             nan     0.2000   -0.0037
##    180        0.5177             nan     0.2000   -0.0006
##    200        0.4895             nan     0.2000   -0.0029
##    220        0.4676             nan     0.2000   -0.0018
##    240        0.4474             nan     0.2000   -0.0008
##    260        0.4287             nan     0.2000   -0.0033
##    280        0.4047             nan     0.2000   -0.0011
##    300        0.3879             nan     0.2000   -0.0027
##    320        0.3719             nan     0.2000   -0.0003
##    340        0.3567             nan     0.2000   -0.0011
##    360        0.3372             nan     0.2000   -0.0012
##    380        0.3271             nan     0.2000   -0.0013
##    400        0.3118             nan     0.2000   -0.0019
##    420        0.2987             nan     0.2000   -0.0008
##    440        0.2892             nan     0.2000   -0.0013
##    460        0.2782             nan     0.2000   -0.0007
##    480        0.2696             nan     0.2000   -0.0004
##    500        0.2596             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1934             nan     0.2000    0.0330
##      2        1.1288             nan     0.2000    0.0253
##      3        1.0845             nan     0.2000    0.0201
##      4        1.0433             nan     0.2000    0.0189
##      5        1.0201             nan     0.2000    0.0070
##      6        0.9968             nan     0.2000    0.0055
##      7        0.9742             nan     0.2000    0.0078
##      8        0.9575             nan     0.2000    0.0039
##      9        0.9408             nan     0.2000    0.0025
##     10        0.9264             nan     0.2000    0.0039
##     20        0.8481             nan     0.2000   -0.0009
##     40        0.7838             nan     0.2000   -0.0019
##     60        0.7397             nan     0.2000   -0.0024
##     80        0.7002             nan     0.2000   -0.0027
##    100        0.6682             nan     0.2000   -0.0005
##    120        0.6286             nan     0.2000   -0.0042
##    140        0.5939             nan     0.2000   -0.0027
##    160        0.5594             nan     0.2000   -0.0025
##    180        0.5300             nan     0.2000   -0.0005
##    200        0.5014             nan     0.2000   -0.0013
##    220        0.4829             nan     0.2000   -0.0002
##    240        0.4586             nan     0.2000   -0.0020
##    260        0.4427             nan     0.2000   -0.0025
##    280        0.4154             nan     0.2000   -0.0009
##    300        0.3970             nan     0.2000   -0.0005
##    320        0.3804             nan     0.2000   -0.0013
##    340        0.3685             nan     0.2000   -0.0024
##    360        0.3543             nan     0.2000   -0.0025
##    380        0.3449             nan     0.2000   -0.0007
##    400        0.3298             nan     0.2000   -0.0011
##    420        0.3199             nan     0.2000   -0.0016
##    440        0.3081             nan     0.2000   -0.0020
##    460        0.2999             nan     0.2000   -0.0009
##    480        0.2870             nan     0.2000   -0.0011
##    500        0.2761             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2087             nan     0.2000    0.0401
##      2        1.1453             nan     0.2000    0.0259
##      3        1.0956             nan     0.2000    0.0162
##      4        1.0582             nan     0.2000    0.0129
##      5        1.0277             nan     0.2000    0.0103
##      6        1.0057             nan     0.2000    0.0081
##      7        0.9859             nan     0.2000    0.0043
##      8        0.9678             nan     0.2000    0.0018
##      9        0.9513             nan     0.2000    0.0037
##     10        0.9326             nan     0.2000    0.0032
##     20        0.8491             nan     0.2000    0.0027
##     40        0.7712             nan     0.2000   -0.0022
##     60        0.7246             nan     0.2000   -0.0025
##     80        0.6914             nan     0.2000   -0.0031
##    100        0.6498             nan     0.2000   -0.0014
##    120        0.6145             nan     0.2000   -0.0025
##    140        0.5764             nan     0.2000   -0.0013
##    160        0.5553             nan     0.2000   -0.0026
##    180        0.5254             nan     0.2000   -0.0005
##    200        0.5001             nan     0.2000   -0.0015
##    220        0.4796             nan     0.2000   -0.0014
##    240        0.4682             nan     0.2000   -0.0034
##    260        0.4469             nan     0.2000   -0.0027
##    280        0.4257             nan     0.2000   -0.0005
##    300        0.4092             nan     0.2000   -0.0017
##    320        0.3933             nan     0.2000   -0.0019
##    340        0.3806             nan     0.2000   -0.0016
##    360        0.3682             nan     0.2000   -0.0017
##    380        0.3530             nan     0.2000   -0.0006
##    400        0.3393             nan     0.2000   -0.0015
##    420        0.3239             nan     0.2000   -0.0019
##    440        0.3129             nan     0.2000   -0.0006
##    460        0.3004             nan     0.2000   -0.0024
##    480        0.2931             nan     0.2000   -0.0012
##    500        0.2816             nan     0.2000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1997             nan     0.2000    0.0473
##      2        1.1360             nan     0.2000    0.0238
##      3        1.0807             nan     0.2000    0.0241
##      4        1.0356             nan     0.2000    0.0164
##      5        1.0042             nan     0.2000    0.0085
##      6        0.9720             nan     0.2000    0.0104
##      7        0.9520             nan     0.2000    0.0049
##      8        0.9289             nan     0.2000    0.0057
##      9        0.9219             nan     0.2000   -0.0045
##     10        0.9044             nan     0.2000    0.0032
##     20        0.8108             nan     0.2000   -0.0024
##     40        0.7178             nan     0.2000   -0.0015
##     60        0.6429             nan     0.2000   -0.0027
##     80        0.5705             nan     0.2000   -0.0061
##    100        0.5230             nan     0.2000   -0.0018
##    120        0.4778             nan     0.2000   -0.0042
##    140        0.4356             nan     0.2000   -0.0023
##    160        0.4028             nan     0.2000   -0.0013
##    180        0.3745             nan     0.2000   -0.0018
##    200        0.3446             nan     0.2000   -0.0014
##    220        0.3171             nan     0.2000   -0.0006
##    240        0.2985             nan     0.2000   -0.0015
##    260        0.2780             nan     0.2000   -0.0021
##    280        0.2646             nan     0.2000   -0.0036
##    300        0.2524             nan     0.2000   -0.0016
##    320        0.2315             nan     0.2000   -0.0013
##    340        0.2177             nan     0.2000   -0.0011
##    360        0.2052             nan     0.2000   -0.0017
##    380        0.1927             nan     0.2000   -0.0008
##    400        0.1823             nan     0.2000   -0.0009
##    420        0.1727             nan     0.2000   -0.0008
##    440        0.1615             nan     0.2000   -0.0017
##    460        0.1524             nan     0.2000   -0.0010
##    480        0.1441             nan     0.2000   -0.0002
##    500        0.1363             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1973             nan     0.2000    0.0374
##      2        1.1342             nan     0.2000    0.0216
##      3        1.0726             nan     0.2000    0.0266
##      4        1.0219             nan     0.2000    0.0176
##      5        0.9851             nan     0.2000    0.0133
##      6        0.9599             nan     0.2000    0.0059
##      7        0.9436             nan     0.2000    0.0015
##      8        0.9238             nan     0.2000    0.0059
##      9        0.9076             nan     0.2000    0.0039
##     10        0.8940             nan     0.2000    0.0002
##     20        0.8078             nan     0.2000   -0.0028
##     40        0.7087             nan     0.2000   -0.0050
##     60        0.6349             nan     0.2000   -0.0002
##     80        0.5840             nan     0.2000   -0.0024
##    100        0.5246             nan     0.2000   -0.0021
##    120        0.4741             nan     0.2000    0.0002
##    140        0.4265             nan     0.2000   -0.0002
##    160        0.3938             nan     0.2000   -0.0031
##    180        0.3655             nan     0.2000   -0.0017
##    200        0.3337             nan     0.2000   -0.0011
##    220        0.3120             nan     0.2000   -0.0028
##    240        0.2887             nan     0.2000   -0.0026
##    260        0.2663             nan     0.2000   -0.0020
##    280        0.2503             nan     0.2000   -0.0022
##    300        0.2319             nan     0.2000   -0.0014
##    320        0.2157             nan     0.2000   -0.0014
##    340        0.2033             nan     0.2000   -0.0011
##    360        0.1910             nan     0.2000   -0.0012
##    380        0.1792             nan     0.2000   -0.0001
##    400        0.1690             nan     0.2000   -0.0008
##    420        0.1606             nan     0.2000   -0.0008
##    440        0.1510             nan     0.2000   -0.0008
##    460        0.1421             nan     0.2000   -0.0005
##    480        0.1348             nan     0.2000   -0.0006
##    500        0.1274             nan     0.2000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1980             nan     0.2000    0.0438
##      2        1.1159             nan     0.2000    0.0327
##      3        1.0701             nan     0.2000    0.0161
##      4        1.0349             nan     0.2000    0.0089
##      5        1.0032             nan     0.2000    0.0113
##      6        0.9724             nan     0.2000    0.0086
##      7        0.9471             nan     0.2000    0.0048
##      8        0.9276             nan     0.2000    0.0054
##      9        0.9098             nan     0.2000   -0.0021
##     10        0.8946             nan     0.2000    0.0001
##     20        0.8082             nan     0.2000   -0.0025
##     40        0.7109             nan     0.2000   -0.0004
##     60        0.6505             nan     0.2000   -0.0036
##     80        0.5905             nan     0.2000   -0.0039
##    100        0.5431             nan     0.2000   -0.0034
##    120        0.5073             nan     0.2000   -0.0042
##    140        0.4617             nan     0.2000   -0.0037
##    160        0.4223             nan     0.2000   -0.0018
##    180        0.3885             nan     0.2000   -0.0023
##    200        0.3555             nan     0.2000   -0.0010
##    220        0.3329             nan     0.2000   -0.0014
##    240        0.3055             nan     0.2000   -0.0015
##    260        0.2839             nan     0.2000   -0.0014
##    280        0.2634             nan     0.2000   -0.0009
##    300        0.2452             nan     0.2000   -0.0016
##    320        0.2263             nan     0.2000   -0.0013
##    340        0.2130             nan     0.2000   -0.0018
##    360        0.2026             nan     0.2000   -0.0017
##    380        0.1892             nan     0.2000   -0.0022
##    400        0.1745             nan     0.2000   -0.0004
##    420        0.1633             nan     0.2000   -0.0004
##    440        0.1528             nan     0.2000   -0.0006
##    460        0.1455             nan     0.2000   -0.0006
##    480        0.1360             nan     0.2000   -0.0004
##    500        0.1297             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2014             nan     0.3000    0.0427
##      2        1.1527             nan     0.3000    0.0196
##      3        1.1077             nan     0.3000    0.0213
##      4        1.0677             nan     0.3000    0.0185
##      5        1.0391             nan     0.3000    0.0122
##      6        1.0168             nan     0.3000    0.0066
##      7        0.9913             nan     0.3000    0.0101
##      8        0.9735             nan     0.3000    0.0068
##      9        0.9632             nan     0.3000    0.0003
##     10        0.9524             nan     0.3000    0.0001
##     20        0.8785             nan     0.3000   -0.0049
##     40        0.8227             nan     0.3000   -0.0021
##     60        0.7910             nan     0.3000   -0.0010
##     80        0.7696             nan     0.3000   -0.0007
##    100        0.7560             nan     0.3000   -0.0007
##    120        0.7431             nan     0.3000   -0.0012
##    140        0.7321             nan     0.3000   -0.0030
##    160        0.7209             nan     0.3000   -0.0005
##    180        0.7021             nan     0.3000   -0.0022
##    200        0.6910             nan     0.3000   -0.0012
##    220        0.6759             nan     0.3000   -0.0027
##    240        0.6627             nan     0.3000   -0.0004
##    260        0.6486             nan     0.3000   -0.0023
##    280        0.6396             nan     0.3000   -0.0033
##    300        0.6268             nan     0.3000   -0.0004
##    320        0.6234             nan     0.3000   -0.0020
##    340        0.6184             nan     0.3000   -0.0070
##    360        0.6146             nan     0.3000   -0.0020
##    380        0.6098             nan     0.3000   -0.0022
##    400        0.6010             nan     0.3000   -0.0020
##    420        0.5945             nan     0.3000   -0.0058
##    440        0.5847             nan     0.3000   -0.0024
##    460        0.5795             nan     0.3000   -0.0021
##    480        0.5765             nan     0.3000   -0.0026
##    500        0.5683             nan     0.3000   -0.0019
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1942             nan     0.3000    0.0384
##      2        1.1378             nan     0.3000    0.0221
##      3        1.0958             nan     0.3000    0.0189
##      4        1.0670             nan     0.3000    0.0098
##      5        1.0345             nan     0.3000    0.0129
##      6        1.0087             nan     0.3000    0.0081
##      7        0.9868             nan     0.3000    0.0061
##      8        0.9793             nan     0.3000   -0.0003
##      9        0.9580             nan     0.3000    0.0074
##     10        0.9477             nan     0.3000    0.0024
##     20        0.8764             nan     0.3000   -0.0017
##     40        0.8113             nan     0.3000   -0.0033
##     60        0.7853             nan     0.3000    0.0010
##     80        0.7684             nan     0.3000   -0.0043
##    100        0.7566             nan     0.3000   -0.0022
##    120        0.7462             nan     0.3000   -0.0037
##    140        0.7292             nan     0.3000   -0.0028
##    160        0.7196             nan     0.3000   -0.0045
##    180        0.7077             nan     0.3000   -0.0030
##    200        0.6941             nan     0.3000   -0.0001
##    220        0.6780             nan     0.3000   -0.0010
##    240        0.6697             nan     0.3000   -0.0005
##    260        0.6562             nan     0.3000   -0.0022
##    280        0.6496             nan     0.3000   -0.0024
##    300        0.6467             nan     0.3000   -0.0025
##    320        0.6353             nan     0.3000   -0.0037
##    340        0.6268             nan     0.3000   -0.0006
##    360        0.6199             nan     0.3000   -0.0026
##    380        0.6118             nan     0.3000   -0.0005
##    400        0.6055             nan     0.3000   -0.0029
##    420        0.6010             nan     0.3000   -0.0031
##    440        0.5960             nan     0.3000   -0.0021
##    460        0.5845             nan     0.3000   -0.0050
##    480        0.5797             nan     0.3000   -0.0025
##    500        0.5752             nan     0.3000   -0.0011
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2039             nan     0.3000    0.0466
##      2        1.1409             nan     0.3000    0.0273
##      3        1.1004             nan     0.3000    0.0182
##      4        1.0580             nan     0.3000    0.0200
##      5        1.0365             nan     0.3000    0.0089
##      6        1.0122             nan     0.3000    0.0093
##      7        0.9864             nan     0.3000    0.0081
##      8        0.9726             nan     0.3000    0.0032
##      9        0.9549             nan     0.3000    0.0083
##     10        0.9479             nan     0.3000   -0.0017
##     20        0.8834             nan     0.3000    0.0002
##     40        0.8213             nan     0.3000   -0.0004
##     60        0.7931             nan     0.3000   -0.0029
##     80        0.7794             nan     0.3000   -0.0059
##    100        0.7603             nan     0.3000   -0.0013
##    120        0.7458             nan     0.3000   -0.0036
##    140        0.7374             nan     0.3000   -0.0028
##    160        0.7200             nan     0.3000   -0.0035
##    180        0.7122             nan     0.3000   -0.0031
##    200        0.6913             nan     0.3000   -0.0042
##    220        0.6854             nan     0.3000   -0.0039
##    240        0.6721             nan     0.3000   -0.0038
##    260        0.6648             nan     0.3000   -0.0044
##    280        0.6560             nan     0.3000   -0.0018
##    300        0.6457             nan     0.3000   -0.0052
##    320        0.6349             nan     0.3000   -0.0024
##    340        0.6263             nan     0.3000   -0.0052
##    360        0.6187             nan     0.3000   -0.0048
##    380        0.6085             nan     0.3000   -0.0048
##    400        0.6036             nan     0.3000   -0.0040
##    420        0.5931             nan     0.3000   -0.0015
##    440        0.5864             nan     0.3000   -0.0008
##    460        0.5830             nan     0.3000   -0.0016
##    480        0.5770             nan     0.3000   -0.0040
##    500        0.5724             nan     0.3000   -0.0024
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1735             nan     0.3000    0.0487
##      2        1.1025             nan     0.3000    0.0334
##      3        1.0471             nan     0.3000    0.0216
##      4        0.9992             nan     0.3000    0.0219
##      5        0.9824             nan     0.3000   -0.0011
##      6        0.9569             nan     0.3000    0.0027
##      7        0.9335             nan     0.3000    0.0082
##      8        0.9227             nan     0.3000   -0.0024
##      9        0.9058             nan     0.3000    0.0031
##     10        0.8973             nan     0.3000   -0.0012
##     20        0.8221             nan     0.3000    0.0023
##     40        0.7310             nan     0.3000   -0.0038
##     60        0.6742             nan     0.3000   -0.0064
##     80        0.6222             nan     0.3000   -0.0037
##    100        0.5735             nan     0.3000   -0.0006
##    120        0.5360             nan     0.3000   -0.0026
##    140        0.4928             nan     0.3000   -0.0036
##    160        0.4548             nan     0.3000   -0.0039
##    180        0.4236             nan     0.3000   -0.0007
##    200        0.3927             nan     0.3000   -0.0033
##    220        0.3649             nan     0.3000   -0.0019
##    240        0.3385             nan     0.3000   -0.0009
##    260        0.3204             nan     0.3000   -0.0008
##    280        0.2980             nan     0.3000   -0.0015
##    300        0.2780             nan     0.3000   -0.0029
##    320        0.2613             nan     0.3000   -0.0014
##    340        0.2459             nan     0.3000   -0.0016
##    360        0.2333             nan     0.3000   -0.0010
##    380        0.2229             nan     0.3000   -0.0004
##    400        0.2110             nan     0.3000   -0.0021
##    420        0.1996             nan     0.3000   -0.0019
##    440        0.1878             nan     0.3000   -0.0009
##    460        0.1790             nan     0.3000   -0.0007
##    480        0.1685             nan     0.3000   -0.0010
##    500        0.1648             nan     0.3000   -0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1806             nan     0.3000    0.0540
##      2        1.0943             nan     0.3000    0.0415
##      3        1.0415             nan     0.3000    0.0183
##      4        1.0056             nan     0.3000    0.0034
##      5        0.9743             nan     0.3000    0.0119
##      6        0.9433             nan     0.3000    0.0104
##      7        0.9313             nan     0.3000    0.0004
##      8        0.9250             nan     0.3000   -0.0042
##      9        0.9116             nan     0.3000   -0.0006
##     10        0.8949             nan     0.3000   -0.0004
##     20        0.8240             nan     0.3000   -0.0055
##     40        0.7480             nan     0.3000   -0.0092
##     60        0.6894             nan     0.3000   -0.0029
##     80        0.6413             nan     0.3000   -0.0016
##    100        0.5886             nan     0.3000   -0.0027
##    120        0.5564             nan     0.3000   -0.0036
##    140        0.5301             nan     0.3000   -0.0016
##    160        0.4890             nan     0.3000   -0.0043
##    180        0.4679             nan     0.3000   -0.0056
##    200        0.4388             nan     0.3000   -0.0039
##    220        0.4181             nan     0.3000   -0.0023
##    240        0.3918             nan     0.3000   -0.0013
##    260        0.3688             nan     0.3000   -0.0051
##    280        0.3369             nan     0.3000   -0.0029
##    300        0.3212             nan     0.3000   -0.0014
##    320        0.3033             nan     0.3000   -0.0023
##    340        0.2870             nan     0.3000   -0.0036
##    360        0.2702             nan     0.3000   -0.0021
##    380        0.2590             nan     0.3000   -0.0007
##    400        0.2438             nan     0.3000   -0.0021
##    420        0.2313             nan     0.3000   -0.0014
##    440        0.2206             nan     0.3000   -0.0005
##    460        0.2108             nan     0.3000   -0.0015
##    480        0.1993             nan     0.3000   -0.0009
##    500        0.1884             nan     0.3000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1734             nan     0.3000    0.0553
##      2        1.1017             nan     0.3000    0.0350
##      3        1.0522             nan     0.3000    0.0091
##      4        1.0002             nan     0.3000    0.0149
##      5        0.9698             nan     0.3000    0.0093
##      6        0.9528             nan     0.3000    0.0049
##      7        0.9260             nan     0.3000    0.0091
##      8        0.9124             nan     0.3000   -0.0009
##      9        0.8956             nan     0.3000    0.0045
##     10        0.8851             nan     0.3000   -0.0000
##     20        0.8093             nan     0.3000   -0.0064
##     40        0.7341             nan     0.3000   -0.0065
##     60        0.6847             nan     0.3000   -0.0028
##     80        0.6497             nan     0.3000   -0.0067
##    100        0.6056             nan     0.3000   -0.0034
##    120        0.5614             nan     0.3000   -0.0035
##    140        0.5271             nan     0.3000   -0.0049
##    160        0.4978             nan     0.3000   -0.0018
##    180        0.4719             nan     0.3000   -0.0018
##    200        0.4424             nan     0.3000   -0.0041
##    220        0.4168             nan     0.3000   -0.0006
##    240        0.3937             nan     0.3000   -0.0030
##    260        0.3752             nan     0.3000   -0.0023
##    280        0.3599             nan     0.3000   -0.0054
##    300        0.3357             nan     0.3000   -0.0015
##    320        0.3222             nan     0.3000   -0.0011
##    340        0.2954             nan     0.3000   -0.0026
##    360        0.2781             nan     0.3000   -0.0005
##    380        0.2613             nan     0.3000   -0.0018
##    400        0.2484             nan     0.3000   -0.0009
##    420        0.2318             nan     0.3000   -0.0018
##    440        0.2208             nan     0.3000   -0.0014
##    460        0.2104             nan     0.3000   -0.0028
##    480        0.1994             nan     0.3000   -0.0008
##    500        0.1905             nan     0.3000    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1534             nan     0.3000    0.0722
##      2        1.0635             nan     0.3000    0.0418
##      3        1.0078             nan     0.3000    0.0229
##      4        0.9575             nan     0.3000    0.0136
##      5        0.9286             nan     0.3000    0.0009
##      6        0.9129             nan     0.3000   -0.0032
##      7        0.8960             nan     0.3000   -0.0042
##      8        0.8708             nan     0.3000    0.0026
##      9        0.8571             nan     0.3000   -0.0003
##     10        0.8462             nan     0.3000   -0.0021
##     20        0.7633             nan     0.3000   -0.0061
##     40        0.6345             nan     0.3000   -0.0030
##     60        0.5607             nan     0.3000   -0.0026
##     80        0.4887             nan     0.3000   -0.0057
##    100        0.4339             nan     0.3000   -0.0031
##    120        0.3921             nan     0.3000   -0.0016
##    140        0.3573             nan     0.3000   -0.0019
##    160        0.3203             nan     0.3000   -0.0056
##    180        0.2806             nan     0.3000   -0.0039
##    200        0.2537             nan     0.3000   -0.0028
##    220        0.2309             nan     0.3000   -0.0033
##    240        0.2083             nan     0.3000   -0.0021
##    260        0.1923             nan     0.3000   -0.0014
##    280        0.1750             nan     0.3000   -0.0000
##    300        0.1583             nan     0.3000   -0.0007
##    320        0.1480             nan     0.3000   -0.0010
##    340        0.1374             nan     0.3000   -0.0007
##    360        0.1265             nan     0.3000   -0.0010
##    380        0.1169             nan     0.3000   -0.0008
##    400        0.1085             nan     0.3000   -0.0008
##    420        0.0997             nan     0.3000   -0.0011
##    440        0.0921             nan     0.3000   -0.0015
##    460        0.0850             nan     0.3000   -0.0008
##    480        0.0786             nan     0.3000   -0.0002
##    500        0.0720             nan     0.3000   -0.0005
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1470             nan     0.3000    0.0619
##      2        1.0599             nan     0.3000    0.0381
##      3        1.0159             nan     0.3000    0.0152
##      4        0.9688             nan     0.3000    0.0176
##      5        0.9412             nan     0.3000    0.0084
##      6        0.9272             nan     0.3000   -0.0047
##      7        0.9065             nan     0.3000    0.0043
##      8        0.8871             nan     0.3000   -0.0001
##      9        0.8724             nan     0.3000   -0.0048
##     10        0.8529             nan     0.3000    0.0004
##     20        0.7597             nan     0.3000   -0.0076
##     40        0.6567             nan     0.3000   -0.0035
##     60        0.5548             nan     0.3000   -0.0054
##     80        0.5311             nan     0.3000   -0.0057
##    100        0.4518             nan     0.3000   -0.0027
##    120        0.3985             nan     0.3000   -0.0058
##    140        0.3590             nan     0.3000   -0.0040
##    160        0.3174             nan     0.3000   -0.0022
##    180        0.2799             nan     0.3000   -0.0027
##    200        0.2535             nan     0.3000   -0.0024
##    220        0.2259             nan     0.3000   -0.0034
##    240        0.2074             nan     0.3000   -0.0014
##    260        0.1905             nan     0.3000   -0.0001
##    280        0.1753             nan     0.3000   -0.0023
##    300        0.1589             nan     0.3000   -0.0029
##    320        0.1449             nan     0.3000   -0.0010
##    340        0.1304             nan     0.3000   -0.0012
##    360        0.1201             nan     0.3000   -0.0005
##    380        0.1101             nan     0.3000   -0.0011
##    400        0.1014             nan     0.3000   -0.0015
##    420        0.0943             nan     0.3000   -0.0005
##    440        0.0854             nan     0.3000   -0.0006
##    460        0.0798             nan     0.3000   -0.0009
##    480        0.0737             nan     0.3000   -0.0006
##    500        0.0686             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1532             nan     0.3000    0.0663
##      2        1.0668             nan     0.3000    0.0318
##      3        1.0164             nan     0.3000    0.0128
##      4        0.9844             nan     0.3000    0.0002
##      5        0.9469             nan     0.3000    0.0125
##      6        0.9205             nan     0.3000    0.0072
##      7        0.8955             nan     0.3000    0.0081
##      8        0.8802             nan     0.3000   -0.0007
##      9        0.8638             nan     0.3000    0.0008
##     10        0.8600             nan     0.3000   -0.0102
##     20        0.7777             nan     0.3000   -0.0053
##     40        0.6731             nan     0.3000   -0.0050
##     60        0.5818             nan     0.3000   -0.0018
##     80        0.5078             nan     0.3000   -0.0023
##    100        0.4466             nan     0.3000   -0.0059
##    120        0.3877             nan     0.3000   -0.0047
##    140        0.3484             nan     0.3000   -0.0037
##    160        0.3131             nan     0.3000   -0.0046
##    180        0.2761             nan     0.3000   -0.0035
##    200        0.2508             nan     0.3000   -0.0023
##    220        0.2246             nan     0.3000   -0.0011
##    240        0.2008             nan     0.3000   -0.0021
##    260        0.1836             nan     0.3000   -0.0026
##    280        0.1657             nan     0.3000   -0.0014
##    300        0.1537             nan     0.3000   -0.0018
##    320        0.1409             nan     0.3000   -0.0009
##    340        0.1275             nan     0.3000   -0.0017
##    360        0.1171             nan     0.3000   -0.0014
##    380        0.1058             nan     0.3000   -0.0002
##    400        0.0976             nan     0.3000   -0.0009
##    420        0.0898             nan     0.3000   -0.0004
##    440        0.0801             nan     0.3000   -0.0005
##    460        0.0747             nan     0.3000   -0.0006
##    480        0.0691             nan     0.3000   -0.0006
##    500        0.0642             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1497             nan     0.5000    0.0571
##      2        1.0895             nan     0.5000    0.0193
##      3        1.0334             nan     0.5000    0.0318
##      4        0.9967             nan     0.5000    0.0170
##      5        0.9726             nan     0.5000    0.0044
##      6        0.9478             nan     0.5000    0.0060
##      7        0.9367             nan     0.5000   -0.0065
##      8        0.9273             nan     0.5000   -0.0004
##      9        0.9168             nan     0.5000   -0.0029
##     10        0.9092             nan     0.5000   -0.0005
##     20        0.8462             nan     0.5000   -0.0015
##     40        0.7823             nan     0.5000   -0.0033
##     60        0.7621             nan     0.5000   -0.0049
##     80        0.7272             nan     0.5000   -0.0013
##    100        0.7062             nan     0.5000   -0.0044
##    120        0.6907             nan     0.5000   -0.0043
##    140        0.6794             nan     0.5000    0.0003
##    160        0.6676             nan     0.5000   -0.0083
##    180        0.6617             nan     0.5000   -0.0125
##    200        0.6412             nan     0.5000   -0.0016
##    220        0.6262             nan     0.5000   -0.0025
##    240        0.6153             nan     0.5000   -0.0021
##    260        0.6061             nan     0.5000   -0.0052
##    280        0.5908             nan     0.5000   -0.0048
##    300        0.5844             nan     0.5000   -0.0023
##    320        0.5820             nan     0.5000   -0.0038
##    340        0.5778             nan     0.5000   -0.0055
##    360        0.5593             nan     0.5000   -0.0025
##    380        0.5577             nan     0.5000   -0.0021
##    400        0.5498             nan     0.5000   -0.0084
##    420        0.5421             nan     0.5000   -0.0053
##    440        0.5314             nan     0.5000   -0.0033
##    460        0.5296             nan     0.5000   -0.0093
##    480        0.5120             nan     0.5000   -0.0016
##    500        0.5021             nan     0.5000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1659             nan     0.5000    0.0659
##      2        1.1016             nan     0.5000    0.0251
##      3        1.0403             nan     0.5000    0.0258
##      4        0.9887             nan     0.5000    0.0266
##      5        0.9740             nan     0.5000   -0.0011
##      6        0.9473             nan     0.5000    0.0066
##      7        0.9183             nan     0.5000    0.0046
##      8        0.9068             nan     0.5000    0.0039
##      9        0.8933             nan     0.5000    0.0023
##     10        0.8851             nan     0.5000    0.0018
##     20        0.8550             nan     0.5000   -0.0087
##     40        0.7986             nan     0.5000   -0.0128
##     60        0.7500             nan     0.5000   -0.0128
##     80        0.7297             nan     0.5000   -0.0017
##    100        0.7070             nan     0.5000   -0.0087
##    120        0.6803             nan     0.5000   -0.0062
##    140        0.6700             nan     0.5000   -0.0064
##    160        0.6540             nan     0.5000   -0.0057
##    180        0.6391             nan     0.5000   -0.0038
##    200        0.6268             nan     0.5000   -0.0029
##    220        0.6142             nan     0.5000   -0.0049
##    240        0.6039             nan     0.5000    0.0016
##    260        0.5881             nan     0.5000   -0.0027
##    280        0.5799             nan     0.5000   -0.0037
##    300        0.5709             nan     0.5000   -0.0054
##    320        0.5619             nan     0.5000   -0.0067
##    340        0.5520             nan     0.5000   -0.0016
##    360        0.5535             nan     0.5000   -0.0069
##    380        0.5412             nan     0.5000   -0.0050
##    400        0.5278             nan     0.5000   -0.0025
##    420        0.5198             nan     0.5000   -0.0030
##    440        0.5159             nan     0.5000   -0.0031
##    460        0.5075             nan     0.5000   -0.0021
##    480        0.5044             nan     0.5000   -0.0061
##    500        0.4995             nan     0.5000   -0.0038
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1771             nan     0.5000    0.0592
##      2        1.1010             nan     0.5000    0.0233
##      3        1.0271             nan     0.5000    0.0245
##      4        0.9846             nan     0.5000    0.0139
##      5        0.9739             nan     0.5000   -0.0036
##      6        0.9569             nan     0.5000    0.0051
##      7        0.9468             nan     0.5000   -0.0026
##      8        0.9469             nan     0.5000   -0.0105
##      9        0.9227             nan     0.5000    0.0058
##     10        0.9115             nan     0.5000    0.0023
##     20        0.8495             nan     0.5000   -0.0110
##     40        0.8056             nan     0.5000   -0.0004
##     60        0.7712             nan     0.5000   -0.0049
##     80        0.7529             nan     0.5000   -0.0020
##    100        0.7276             nan     0.5000   -0.0035
##    120        0.7132             nan     0.5000   -0.0035
##    140        0.7037             nan     0.5000   -0.0076
##    160        0.6783             nan     0.5000   -0.0029
##    180        0.6678             nan     0.5000   -0.0078
##    200        0.6601             nan     0.5000   -0.0044
##    220        0.6519             nan     0.5000   -0.0016
##    240        0.6439             nan     0.5000   -0.0015
##    260        0.6238             nan     0.5000   -0.0024
##    280        0.6013             nan     0.5000   -0.0050
##    300        0.5889             nan     0.5000   -0.0026
##    320        0.5803             nan     0.5000   -0.0060
##    340        0.5690             nan     0.5000   -0.0126
##    360        0.5521             nan     0.5000   -0.0049
##    380        0.5447             nan     0.5000   -0.0017
##    400        0.5413             nan     0.5000   -0.0075
##    420        0.5410             nan     0.5000   -0.0132
##    440        0.5287             nan     0.5000   -0.0058
##    460        0.5167             nan     0.5000   -0.0027
##    480        0.5080             nan     0.5000   -0.0005
##    500        0.5025             nan     0.5000   -0.0027
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1235             nan     0.5000    0.0776
##      2        1.0245             nan     0.5000    0.0444
##      3        0.9753             nan     0.5000    0.0142
##      4        0.9380             nan     0.5000    0.0146
##      5        0.9337             nan     0.5000   -0.0087
##      6        0.9141             nan     0.5000   -0.0006
##      7        0.8974             nan     0.5000   -0.0029
##      8        0.8792             nan     0.5000   -0.0105
##      9        0.8652             nan     0.5000   -0.0009
##     10        0.8566             nan     0.5000   -0.0024
##     20        0.7918             nan     0.5000   -0.0096
##     40        0.7138             nan     0.5000   -0.0084
##     60        0.6579             nan     0.5000   -0.0082
##     80        0.5877             nan     0.5000   -0.0084
##    100        0.5498             nan     0.5000   -0.0117
##    120        0.4692             nan     0.5000   -0.0029
##    140        0.4318             nan     0.5000   -0.0034
##    160           inf             nan     0.5000   -0.0008
##    180           inf             nan     0.5000   -0.0052
##    200           inf             nan     0.5000   -0.0046
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000   -0.0057
##    280           inf             nan     0.5000   -0.0016
##    300           inf             nan     0.5000   -0.0012
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000   -0.0012
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000   -0.0021
##    440           inf             nan     0.5000   -0.0009
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1238             nan     0.5000    0.0748
##      2        1.0589             nan     0.5000    0.0066
##      3        0.9995             nan     0.5000    0.0266
##      4        0.9521             nan     0.5000    0.0095
##      5        0.9168             nan     0.5000    0.0115
##      6        0.9001             nan     0.5000   -0.0010
##      7        0.8913             nan     0.5000   -0.0079
##      8        0.8801             nan     0.5000   -0.0012
##      9        0.8732             nan     0.5000   -0.0089
##     10        0.8631             nan     0.5000   -0.0077
##     20        0.8020             nan     0.5000   -0.0122
##     40        0.6974             nan     0.5000   -0.0071
##     60        0.6182             nan     0.5000   -0.0063
##     80        0.5483             nan     0.5000   -0.0021
##    100        0.4799             nan     0.5000   -0.0099
##    120        0.4298             nan     0.5000   -0.0026
##    140        0.4028             nan     0.5000   -0.0081
##    160           inf             nan     0.5000   -0.0019
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1236             nan     0.5000    0.0824
##      2        1.0211             nan     0.5000    0.0478
##      3        0.9817             nan     0.5000    0.0120
##      4        0.9320             nan     0.5000    0.0153
##      5        0.9097             nan     0.5000    0.0033
##      6        0.8927             nan     0.5000   -0.0021
##      7        0.8859             nan     0.5000   -0.0091
##      8        0.8770             nan     0.5000   -0.0141
##      9        0.8653             nan     0.5000   -0.0004
##     10        0.8544             nan     0.5000   -0.0090
##     20        0.7789             nan     0.5000   -0.0109
##     40        0.6959             nan     0.5000   -0.0043
##     60        0.6279             nan     0.5000   -0.0117
##     80        0.5824             nan     0.5000   -0.0103
##    100        0.5372             nan     0.5000   -0.0089
##    120        0.4897             nan     0.5000   -0.0033
##    140        0.4295             nan     0.5000   -0.0002
##    160        0.3777             nan     0.5000   -0.0026
##    180        0.3501             nan     0.5000   -0.0075
##    200        0.3174             nan     0.5000   -0.0044
##    220        0.2909             nan     0.5000   -0.0049
##    240        1.9869             nan     0.5000   -0.0035
##    260        1.9932             nan     0.5000   -0.0035
##    280        1.9436             nan     0.5000   -0.0022
##    300        1.9344             nan     0.5000   -0.0011
##    320        1.8872             nan     0.5000   -0.0010
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0884             nan     0.5000    0.0660
##      2        1.0019             nan     0.5000    0.0262
##      3        0.9721             nan     0.5000   -0.0079
##      4        0.9212             nan     0.5000    0.0050
##      5        0.9031             nan     0.5000   -0.0069
##      6        0.8769             nan     0.5000   -0.0053
##      7        0.8533             nan     0.5000   -0.0010
##      8        0.8525             nan     0.5000   -0.0241
##      9        0.8336             nan     0.5000   -0.0070
##     10        0.8225             nan     0.5000   -0.0163
##     20        0.7443             nan     0.5000   -0.0043
##     40        0.5889             nan     0.5000   -0.0129
##     60        0.4409             nan     0.5000   -0.0051
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0853             nan     0.5000    0.0958
##      2        0.9787             nan     0.5000    0.0530
##      3        0.9329             nan     0.5000    0.0085
##      4        0.9057             nan     0.5000   -0.0025
##      5        0.8761             nan     0.5000    0.0033
##      6        0.8642             nan     0.5000   -0.0130
##      7        0.8447             nan     0.5000   -0.0041
##      8        0.8332             nan     0.5000   -0.0121
##      9        0.8121             nan     0.5000   -0.0173
##     10        0.8020             nan     0.5000   -0.0111
##     20        0.6941             nan     0.5000   -0.0071
##     40        0.5532             nan     0.5000   -0.0116
##     60        0.4491             nan     0.5000   -0.0051
##     80        0.3712             nan     0.5000   -0.0089
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0800             nan     0.5000    0.0844
##      2        1.0056             nan     0.5000    0.0152
##      3        0.9650             nan     0.5000    0.0035
##      4        0.9345             nan     0.5000   -0.0041
##      5        0.9169             nan     0.5000   -0.0094
##      6        0.9172             nan     0.5000   -0.0288
##      7        0.8965             nan     0.5000   -0.0084
##      8        0.8691             nan     0.5000    0.0023
##      9        0.8599             nan     0.5000   -0.0162
##     10        0.8403             nan     0.5000   -0.0033
##     20        0.7463             nan     0.5000   -0.0200
##     40        0.6019             nan     0.5000   -0.0032
##     60        0.4912             nan     0.5000   -0.0049
##     80        0.3913             nan     0.5000   -0.0020
##    100        0.3315             nan     0.5000   -0.0027
##    120        0.2897             nan     0.5000   -0.0032
##    140        0.2438             nan     0.5000   -0.0035
##    160        0.1947             nan     0.5000   -0.0019
##    180        0.1599             nan     0.5000   -0.0017
##    200        0.1371             nan     0.5000   -0.0007
##    220        0.1165             nan     0.5000   -0.0010
##    240        0.0999             nan     0.5000   -0.0017
##    260        0.0865             nan     0.5000   -0.0009
##    280        0.0709             nan     0.5000    0.0008
##    300        0.0623             nan     0.5000   -0.0010
##    320        0.0564             nan     0.5000   -0.0008
##    340        0.0482             nan     0.5000   -0.0010
##    360        0.0424             nan     0.5000   -0.0010
##    380        0.0367             nan     0.5000   -0.0008
##    400        0.0324             nan     0.5000   -0.0006
##    420        0.0295             nan     0.5000   -0.0004
##    440        0.0260             nan     0.5000   -0.0009
##    460        0.0232             nan     0.5000   -0.0000
##    480        0.0212             nan     0.5000   -0.0004
##    500        0.0194             nan     0.5000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1354             nan     1.0000    0.0739
##      2        1.0544             nan     1.0000    0.0357
##      3        0.9959             nan     1.0000    0.0238
##      4        0.9879             nan     1.0000   -0.0094
##      5        0.9635             nan     1.0000    0.0101
##      6        0.9562             nan     1.0000   -0.0103
##      7        0.9331             nan     1.0000    0.0084
##      8        0.9204             nan     1.0000   -0.0021
##      9        0.9269             nan     1.0000   -0.0221
##     10        0.9360             nan     1.0000   -0.0338
##     20        0.9084             nan     1.0000   -0.0034
##     40        0.9427             nan     1.0000   -0.0077
##     60  2307022.2009             nan     1.0000    0.0009
##     80  2307022.1771             nan     1.0000   -0.0247
##    100  2307055.8115             nan     1.0000    0.0008
##    120  2307155.3230             nan     1.0000   -0.0017
##    140  2307155.2661             nan     1.0000   -0.0026
##    160  2307155.2807             nan     1.0000   -0.0041
##    180  2307155.2784             nan     1.0000    0.0000
##    200  2307155.2584             nan     1.0000   -0.0072
##    220  2307155.7183             nan     1.0000    0.0017
##    240  2307155.6843             nan     1.0000    0.0009
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1281             nan     1.0000    0.0779
##      2        1.0602             nan     1.0000    0.0182
##      3        1.0046             nan     1.0000    0.0142
##      4        0.9746             nan     1.0000    0.0066
##      5        0.9609             nan     1.0000   -0.0076
##      6        0.9620             nan     1.0000   -0.0159
##      7        0.9634             nan     1.0000   -0.0234
##      8        0.9311             nan     1.0000    0.0118
##      9        0.9164             nan     1.0000    0.0028
##     10        0.9127             nan     1.0000   -0.0098
##     20        0.9192             nan     1.0000   -0.0072
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1282             nan     1.0000    0.0610
##      2        1.0372             nan     1.0000    0.0307
##      3        1.0225             nan     1.0000   -0.0056
##      4        0.9719             nan     1.0000    0.0260
##      5        0.9557             nan     1.0000   -0.0047
##      6        0.9476             nan     1.0000   -0.0060
##      7        0.9537             nan     1.0000   -0.0238
##      8        0.9562             nan     1.0000   -0.0258
##      9        0.9593             nan     1.0000   -0.0280
##     10        0.9229             nan     1.0000    0.0136
##     20        0.9107             nan     1.0000   -0.0148
##     40        0.8964             nan     1.0000   -0.0430
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0806             nan     1.0000    0.0628
##      2        0.9996             nan     1.0000    0.0217
##      3        0.9709             nan     1.0000    0.0006
##      4        0.9704             nan     1.0000   -0.0296
##      5        0.9993             nan     1.0000   -0.0696
##      6        0.9720             nan     1.0000   -0.0101
##      7        0.9780             nan     1.0000   -0.0331
##      8        0.9477             nan     1.0000    0.0013
##      9        0.9227             nan     1.0000    0.0015
##     10        0.9276             nan     1.0000   -0.0309
##     20        0.9003             nan     1.0000   -0.0325
##     40        0.8617             nan     1.0000   -0.0748
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000   -0.0345
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000   -0.0068
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000   -0.0006
##    220           inf             nan     1.0000   -0.0022
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000   -0.0042
##    280           inf             nan     1.0000   -0.0038
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000    0.0015
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0774             nan     1.0000    0.0953
##      2        1.0034             nan     1.0000    0.0170
##      3        0.9184             nan     1.0000    0.0292
##      4        0.8923             nan     1.0000   -0.0126
##      5        0.8933             nan     1.0000   -0.0395
##      6        0.8888             nan     1.0000   -0.0206
##      7        0.8769             nan     1.0000   -0.0124
##      8        0.8616             nan     1.0000   -0.0039
##      9        0.8576             nan     1.0000   -0.0238
##     10        0.8707             nan     1.0000   -0.0435
##     20        0.7942             nan     1.0000   -0.0376
##     40 24016620229.5012             nan     1.0000   -0.0170
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0751             nan     1.0000    0.1100
##      2        0.9957             nan     1.0000   -0.0013
##      3        0.9605             nan     1.0000   -0.0096
##      4        0.9749             nan     1.0000   -0.0398
##      5        0.9327             nan     1.0000   -0.0062
##      6        0.8872             nan     1.0000    0.0164
##      7        0.9750             nan     1.0000   -0.1076
##      8        1.0760             nan     1.0000   -0.1346
##      9        4.0625             nan     1.0000   -2.0685
##     10        4.0406             nan     1.0000   -0.0278
##     20        3.9680             nan     1.0000   -0.0084
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0529             nan     1.0000    0.0872
##      2        0.9814             nan     1.0000   -0.0033
##      3        0.9303             nan     1.0000    0.0042
##      4        0.9235             nan     1.0000   -0.0255
##      5        0.9294             nan     1.0000   -0.0417
##      6        0.8995             nan     1.0000   -0.0244
##      7        0.8918             nan     1.0000   -0.0339
##      8        0.8658             nan     1.0000   -0.0199
##      9        0.8923             nan     1.0000   -0.0706
##     10        0.9284             nan     1.0000   -0.0874
##     20       24.1232             nan     1.0000   -1.4419
##     40       24.7232             nan     1.0000    0.0000
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0633             nan     1.0000    0.0573
##      2        0.9747             nan     1.0000    0.0243
##      3        0.9786             nan     1.0000   -0.0437
##      4        0.9453             nan     1.0000   -0.0155
##      5        0.9071             nan     1.0000   -0.0120
##      6        0.9065             nan     1.0000   -0.0390
##      7        0.9082             nan     1.0000   -0.0459
##      8        0.9102             nan     1.0000   -0.0434
##      9        1.0771             nan     1.0000   -0.1037
##     10        1.0580             nan     1.0000   -0.0083
##     20 89690508917.0235             nan     1.0000   -0.0270
##     40 89690508919.7059             nan     1.0000   -0.0335
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0575             nan     1.0000    0.0947
##      2        0.9709             nan     1.0000   -0.0034
##      3        0.9863             nan     1.0000   -0.0413
##      4        0.9464             nan     1.0000    0.0106
##      5        0.9325             nan     1.0000   -0.0124
##      6        0.9326             nan     1.0000   -0.0557
##      7        0.9017             nan     1.0000    0.0006
##      8        0.9761             nan     1.0000   -0.1186
##      9        0.9445             nan     1.0000   -0.0308
##     10        0.9521             nan     1.0000   -0.0562
##     20        2.5666             nan     1.0000   -0.0209
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2934             nan     0.0000    0.0000
##      2        1.2934             nan     0.0000    0.0000
##      3        1.2934             nan     0.0000    0.0000
##      4        1.2934             nan     0.0000    0.0000
##      5        1.2934             nan     0.0000    0.0000
##      6        1.2934             nan     0.0000    0.0000
##      7        1.2934             nan     0.0000    0.0000
##      8        1.2934             nan     0.0000    0.0000
##      9        1.2934             nan     0.0000    0.0000
##     10        1.2934             nan     0.0000    0.0000
##     20        1.2934             nan     0.0000    0.0000
##     40        1.2934             nan     0.0000    0.0000
##     60        1.2934             nan     0.0000    0.0000
##     80        1.2934             nan     0.0000    0.0000
##    100        1.2934             nan     0.0000    0.0000
##    120        1.2934             nan     0.0000    0.0000
##    140        1.2934             nan     0.0000    0.0000
##    160        1.2934             nan     0.0000    0.0000
##    180        1.2934             nan     0.0000    0.0000
##    200        1.2934             nan     0.0000    0.0000
##    220        1.2934             nan     0.0000    0.0000
##    240        1.2934             nan     0.0000    0.0000
##    260        1.2934             nan     0.0000    0.0000
##    280        1.2934             nan     0.0000    0.0000
##    300        1.2934             nan     0.0000    0.0000
##    320        1.2934             nan     0.0000    0.0000
##    340        1.2934             nan     0.0000    0.0000
##    360        1.2934             nan     0.0000    0.0000
##    380        1.2934             nan     0.0000    0.0000
##    400        1.2934             nan     0.0000    0.0000
##    420        1.2934             nan     0.0000    0.0000
##    440        1.2934             nan     0.0000    0.0000
##    460        1.2934             nan     0.0000    0.0000
##    480        1.2934             nan     0.0000    0.0000
##    500        1.2934             nan     0.0000    0.0000
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2923             nan     0.0010    0.0001
##      4        1.2920             nan     0.0010    0.0002
##      5        1.2916             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2909             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2902             nan     0.0010    0.0002
##     10        1.2898             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2792             nan     0.0010    0.0002
##     60        1.2725             nan     0.0010    0.0002
##     80        1.2659             nan     0.0010    0.0001
##    100        1.2595             nan     0.0010    0.0001
##    120        1.2532             nan     0.0010    0.0001
##    140        1.2473             nan     0.0010    0.0001
##    160        1.2413             nan     0.0010    0.0001
##    180        1.2359             nan     0.0010    0.0001
##    200        1.2304             nan     0.0010    0.0001
##    220        1.2251             nan     0.0010    0.0001
##    240        1.2200             nan     0.0010    0.0001
##    260        1.2151             nan     0.0010    0.0001
##    280        1.2104             nan     0.0010    0.0001
##    300        1.2059             nan     0.0010    0.0001
##    320        1.2012             nan     0.0010    0.0001
##    340        1.1969             nan     0.0010    0.0001
##    360        1.1926             nan     0.0010    0.0001
##    380        1.1883             nan     0.0010    0.0001
##    400        1.1842             nan     0.0010    0.0001
##    420        1.1801             nan     0.0010    0.0001
##    440        1.1762             nan     0.0010    0.0001
##    460        1.1724             nan     0.0010    0.0001
##    480        1.1686             nan     0.0010    0.0001
##    500        1.1648             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2919             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2911             nan     0.0010    0.0002
##      7        1.2907             nan     0.0010    0.0002
##      8        1.2904             nan     0.0010    0.0002
##      9        1.2900             nan     0.0010    0.0002
##     10        1.2896             nan     0.0010    0.0002
##     20        1.2860             nan     0.0010    0.0001
##     40        1.2790             nan     0.0010    0.0002
##     60        1.2724             nan     0.0010    0.0001
##     80        1.2659             nan     0.0010    0.0002
##    100        1.2595             nan     0.0010    0.0001
##    120        1.2534             nan     0.0010    0.0001
##    140        1.2475             nan     0.0010    0.0001
##    160        1.2419             nan     0.0010    0.0001
##    180        1.2364             nan     0.0010    0.0001
##    200        1.2311             nan     0.0010    0.0001
##    220        1.2257             nan     0.0010    0.0001
##    240        1.2206             nan     0.0010    0.0001
##    260        1.2156             nan     0.0010    0.0001
##    280        1.2109             nan     0.0010    0.0001
##    300        1.2062             nan     0.0010    0.0001
##    320        1.2018             nan     0.0010    0.0001
##    340        1.1974             nan     0.0010    0.0001
##    360        1.1931             nan     0.0010    0.0001
##    380        1.1890             nan     0.0010    0.0001
##    400        1.1848             nan     0.0010    0.0001
##    420        1.1807             nan     0.0010    0.0001
##    440        1.1768             nan     0.0010    0.0001
##    460        1.1729             nan     0.0010    0.0001
##    480        1.1692             nan     0.0010    0.0001
##    500        1.1656             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2930             nan     0.0010    0.0002
##      2        1.2926             nan     0.0010    0.0002
##      3        1.2922             nan     0.0010    0.0002
##      4        1.2918             nan     0.0010    0.0002
##      5        1.2915             nan     0.0010    0.0002
##      6        1.2912             nan     0.0010    0.0002
##      7        1.2908             nan     0.0010    0.0002
##      8        1.2905             nan     0.0010    0.0002
##      9        1.2901             nan     0.0010    0.0002
##     10        1.2897             nan     0.0010    0.0002
##     20        1.2861             nan     0.0010    0.0002
##     40        1.2790             nan     0.0010    0.0002
##     60        1.2724             nan     0.0010    0.0002
##     80        1.2658             nan     0.0010    0.0002
##    100        1.2594             nan     0.0010    0.0002
##    120        1.2534             nan     0.0010    0.0002
##    140        1.2475             nan     0.0010    0.0001
##    160        1.2418             nan     0.0010    0.0001
##    180        1.2362             nan     0.0010    0.0001
##    200        1.2308             nan     0.0010    0.0001
##    220        1.2256             nan     0.0010    0.0001
##    240        1.2206             nan     0.0010    0.0001
##    260        1.2156             nan     0.0010    0.0001
##    280        1.2108             nan     0.0010    0.0001
##    300        1.2060             nan     0.0010    0.0001
##    320        1.2016             nan     0.0010    0.0001
##    340        1.1972             nan     0.0010    0.0001
##    360        1.1929             nan     0.0010    0.0001
##    380        1.1887             nan     0.0010    0.0001
##    400        1.1846             nan     0.0010    0.0001
##    420        1.1807             nan     0.0010    0.0001
##    440        1.1768             nan     0.0010    0.0001
##    460        1.1729             nan     0.0010    0.0001
##    480        1.1691             nan     0.0010    0.0001
##    500        1.1654             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2901             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2838             nan     0.0010    0.0002
##     40        1.2747             nan     0.0010    0.0002
##     60        1.2660             nan     0.0010    0.0002
##     80        1.2575             nan     0.0010    0.0002
##    100        1.2492             nan     0.0010    0.0002
##    120        1.2411             nan     0.0010    0.0002
##    140        1.2334             nan     0.0010    0.0002
##    160        1.2260             nan     0.0010    0.0001
##    180        1.2186             nan     0.0010    0.0002
##    200        1.2113             nan     0.0010    0.0002
##    220        1.2043             nan     0.0010    0.0002
##    240        1.1977             nan     0.0010    0.0002
##    260        1.1915             nan     0.0010    0.0001
##    280        1.1852             nan     0.0010    0.0001
##    300        1.1790             nan     0.0010    0.0001
##    320        1.1728             nan     0.0010    0.0001
##    340        1.1670             nan     0.0010    0.0001
##    360        1.1614             nan     0.0010    0.0001
##    380        1.1557             nan     0.0010    0.0001
##    400        1.1503             nan     0.0010    0.0001
##    420        1.1451             nan     0.0010    0.0001
##    440        1.1400             nan     0.0010    0.0001
##    460        1.1349             nan     0.0010    0.0001
##    480        1.1301             nan     0.0010    0.0001
##    500        1.1253             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2924             nan     0.0010    0.0002
##      3        1.2919             nan     0.0010    0.0002
##      4        1.2914             nan     0.0010    0.0002
##      5        1.2909             nan     0.0010    0.0002
##      6        1.2904             nan     0.0010    0.0002
##      7        1.2899             nan     0.0010    0.0002
##      8        1.2894             nan     0.0010    0.0002
##      9        1.2889             nan     0.0010    0.0002
##     10        1.2885             nan     0.0010    0.0002
##     20        1.2839             nan     0.0010    0.0002
##     40        1.2747             nan     0.0010    0.0002
##     60        1.2660             nan     0.0010    0.0002
##     80        1.2576             nan     0.0010    0.0002
##    100        1.2492             nan     0.0010    0.0002
##    120        1.2413             nan     0.0010    0.0002
##    140        1.2337             nan     0.0010    0.0001
##    160        1.2265             nan     0.0010    0.0002
##    180        1.2192             nan     0.0010    0.0002
##    200        1.2121             nan     0.0010    0.0002
##    220        1.2052             nan     0.0010    0.0001
##    240        1.1987             nan     0.0010    0.0001
##    260        1.1921             nan     0.0010    0.0001
##    280        1.1857             nan     0.0010    0.0001
##    300        1.1795             nan     0.0010    0.0001
##    320        1.1736             nan     0.0010    0.0001
##    340        1.1678             nan     0.0010    0.0001
##    360        1.1623             nan     0.0010    0.0001
##    380        1.1566             nan     0.0010    0.0001
##    400        1.1511             nan     0.0010    0.0001
##    420        1.1459             nan     0.0010    0.0001
##    440        1.1409             nan     0.0010    0.0001
##    460        1.1358             nan     0.0010    0.0001
##    480        1.1308             nan     0.0010    0.0001
##    500        1.1259             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2929             nan     0.0010    0.0002
##      2        1.2925             nan     0.0010    0.0002
##      3        1.2920             nan     0.0010    0.0002
##      4        1.2915             nan     0.0010    0.0002
##      5        1.2910             nan     0.0010    0.0002
##      6        1.2905             nan     0.0010    0.0002
##      7        1.2900             nan     0.0010    0.0002
##      8        1.2896             nan     0.0010    0.0002
##      9        1.2891             nan     0.0010    0.0002
##     10        1.2886             nan     0.0010    0.0002
##     20        1.2840             nan     0.0010    0.0002
##     40        1.2748             nan     0.0010    0.0002
##     60        1.2658             nan     0.0010    0.0002
##     80        1.2573             nan     0.0010    0.0002
##    100        1.2493             nan     0.0010    0.0002
##    120        1.2415             nan     0.0010    0.0002
##    140        1.2339             nan     0.0010    0.0002
##    160        1.2265             nan     0.0010    0.0002
##    180        1.2191             nan     0.0010    0.0001
##    200        1.2119             nan     0.0010    0.0002
##    220        1.2049             nan     0.0010    0.0002
##    240        1.1982             nan     0.0010    0.0001
##    260        1.1915             nan     0.0010    0.0001
##    280        1.1852             nan     0.0010    0.0001
##    300        1.1789             nan     0.0010    0.0001
##    320        1.1730             nan     0.0010    0.0001
##    340        1.1671             nan     0.0010    0.0001
##    360        1.1613             nan     0.0010    0.0001
##    380        1.1559             nan     0.0010    0.0001
##    400        1.1504             nan     0.0010    0.0001
##    420        1.1453             nan     0.0010    0.0001
##    440        1.1401             nan     0.0010    0.0001
##    460        1.1350             nan     0.0010    0.0001
##    480        1.1301             nan     0.0010    0.0001
##    500        1.1255             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2913             nan     0.0010    0.0003
##      5        1.2907             nan     0.0010    0.0003
##      6        1.2902             nan     0.0010    0.0002
##      7        1.2897             nan     0.0010    0.0002
##      8        1.2892             nan     0.0010    0.0003
##      9        1.2887             nan     0.0010    0.0002
##     10        1.2882             nan     0.0010    0.0002
##     20        1.2830             nan     0.0010    0.0002
##     40        1.2723             nan     0.0010    0.0002
##     60        1.2623             nan     0.0010    0.0002
##     80        1.2523             nan     0.0010    0.0002
##    100        1.2427             nan     0.0010    0.0002
##    120        1.2335             nan     0.0010    0.0002
##    140        1.2245             nan     0.0010    0.0002
##    160        1.2160             nan     0.0010    0.0002
##    180        1.2077             nan     0.0010    0.0002
##    200        1.1995             nan     0.0010    0.0002
##    220        1.1913             nan     0.0010    0.0002
##    240        1.1837             nan     0.0010    0.0002
##    260        1.1762             nan     0.0010    0.0001
##    280        1.1690             nan     0.0010    0.0002
##    300        1.1619             nan     0.0010    0.0001
##    320        1.1549             nan     0.0010    0.0002
##    340        1.1483             nan     0.0010    0.0001
##    360        1.1420             nan     0.0010    0.0001
##    380        1.1356             nan     0.0010    0.0001
##    400        1.1295             nan     0.0010    0.0001
##    420        1.1234             nan     0.0010    0.0001
##    440        1.1175             nan     0.0010    0.0001
##    460        1.1118             nan     0.0010    0.0001
##    480        1.1062             nan     0.0010    0.0001
##    500        1.1007             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2922             nan     0.0010    0.0002
##      3        1.2916             nan     0.0010    0.0002
##      4        1.2911             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2900             nan     0.0010    0.0003
##      7        1.2895             nan     0.0010    0.0002
##      8        1.2889             nan     0.0010    0.0003
##      9        1.2884             nan     0.0010    0.0003
##     10        1.2879             nan     0.0010    0.0002
##     20        1.2827             nan     0.0010    0.0003
##     40        1.2723             nan     0.0010    0.0002
##     60        1.2620             nan     0.0010    0.0002
##     80        1.2521             nan     0.0010    0.0002
##    100        1.2427             nan     0.0010    0.0002
##    120        1.2331             nan     0.0010    0.0002
##    140        1.2242             nan     0.0010    0.0002
##    160        1.2156             nan     0.0010    0.0002
##    180        1.2074             nan     0.0010    0.0002
##    200        1.1994             nan     0.0010    0.0002
##    220        1.1915             nan     0.0010    0.0002
##    240        1.1837             nan     0.0010    0.0002
##    260        1.1763             nan     0.0010    0.0002
##    280        1.1689             nan     0.0010    0.0001
##    300        1.1617             nan     0.0010    0.0002
##    320        1.1550             nan     0.0010    0.0001
##    340        1.1483             nan     0.0010    0.0001
##    360        1.1419             nan     0.0010    0.0001
##    380        1.1357             nan     0.0010    0.0002
##    400        1.1295             nan     0.0010    0.0001
##    420        1.1233             nan     0.0010    0.0002
##    440        1.1174             nan     0.0010    0.0001
##    460        1.1117             nan     0.0010    0.0001
##    480        1.1063             nan     0.0010    0.0001
##    500        1.1007             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2928             nan     0.0010    0.0002
##      2        1.2923             nan     0.0010    0.0002
##      3        1.2918             nan     0.0010    0.0002
##      4        1.2912             nan     0.0010    0.0002
##      5        1.2906             nan     0.0010    0.0002
##      6        1.2901             nan     0.0010    0.0002
##      7        1.2896             nan     0.0010    0.0002
##      8        1.2891             nan     0.0010    0.0003
##      9        1.2885             nan     0.0010    0.0003
##     10        1.2879             nan     0.0010    0.0002
##     20        1.2826             nan     0.0010    0.0002
##     40        1.2721             nan     0.0010    0.0002
##     60        1.2621             nan     0.0010    0.0002
##     80        1.2521             nan     0.0010    0.0002
##    100        1.2429             nan     0.0010    0.0002
##    120        1.2337             nan     0.0010    0.0002
##    140        1.2247             nan     0.0010    0.0002
##    160        1.2162             nan     0.0010    0.0002
##    180        1.2079             nan     0.0010    0.0002
##    200        1.2000             nan     0.0010    0.0002
##    220        1.1922             nan     0.0010    0.0002
##    240        1.1847             nan     0.0010    0.0001
##    260        1.1771             nan     0.0010    0.0002
##    280        1.1699             nan     0.0010    0.0001
##    300        1.1626             nan     0.0010    0.0001
##    320        1.1556             nan     0.0010    0.0001
##    340        1.1489             nan     0.0010    0.0002
##    360        1.1421             nan     0.0010    0.0002
##    380        1.1359             nan     0.0010    0.0001
##    400        1.1296             nan     0.0010    0.0001
##    420        1.1234             nan     0.0010    0.0001
##    440        1.1177             nan     0.0010    0.0001
##    460        1.1119             nan     0.0010    0.0001
##    480        1.1064             nan     0.0010    0.0001
##    500        1.1011             nan     0.0010    0.0001
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2587             nan     0.1000    0.0140
##      2        1.2296             nan     0.1000    0.0143
##      3        1.2064             nan     0.1000    0.0111
##      4        1.1820             nan     0.1000    0.0104
##      5        1.1639             nan     0.1000    0.0076
##      6        1.1468             nan     0.1000    0.0062
##      7        1.1302             nan     0.1000    0.0076
##      8        1.1160             nan     0.1000    0.0061
##      9        1.1017             nan     0.1000    0.0052
##     10        1.0871             nan     0.1000    0.0065
##     20        1.0044             nan     0.1000    0.0018
##     40        0.9155             nan     0.1000   -0.0001
##     60        0.8719             nan     0.1000    0.0003
##     80        0.8425             nan     0.1000   -0.0016
##    100        0.8239             nan     0.1000   -0.0000
##    120        0.8083             nan     0.1000   -0.0003
##    140        0.7958             nan     0.1000   -0.0008
##    160        0.7858             nan     0.1000   -0.0019
##    180        0.7772             nan     0.1000   -0.0001
##    200        0.7685             nan     0.1000   -0.0008
##    220        0.7594             nan     0.1000   -0.0010
##    240        0.7497             nan     0.1000   -0.0011
##    260        0.7438             nan     0.1000   -0.0005
##    280        0.7362             nan     0.1000   -0.0009
##    300        0.7296             nan     0.1000   -0.0007
##    320        0.7237             nan     0.1000   -0.0004
##    340        0.7185             nan     0.1000   -0.0010
##    360        0.7136             nan     0.1000   -0.0011
##    380        0.7075             nan     0.1000   -0.0007
##    400        0.7032             nan     0.1000   -0.0009
##    420        0.6984             nan     0.1000   -0.0005
##    440        0.6936             nan     0.1000   -0.0004
##    460        0.6895             nan     0.1000   -0.0001
##    480        0.6846             nan     0.1000   -0.0011
##    500        0.6815             nan     0.1000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2567             nan     0.1000    0.0161
##      2        1.2299             nan     0.1000    0.0144
##      3        1.2047             nan     0.1000    0.0108
##      4        1.1847             nan     0.1000    0.0100
##      5        1.1645             nan     0.1000    0.0084
##      6        1.1459             nan     0.1000    0.0075
##      7        1.1302             nan     0.1000    0.0052
##      8        1.1169             nan     0.1000    0.0054
##      9        1.1042             nan     0.1000    0.0043
##     10        1.0910             nan     0.1000    0.0057
##     20        1.0034             nan     0.1000    0.0021
##     40        0.9158             nan     0.1000    0.0001
##     60        0.8726             nan     0.1000   -0.0004
##     80        0.8446             nan     0.1000   -0.0013
##    100        0.8277             nan     0.1000   -0.0015
##    120        0.8149             nan     0.1000   -0.0005
##    140        0.8009             nan     0.1000   -0.0014
##    160        0.7913             nan     0.1000   -0.0007
##    180        0.7832             nan     0.1000   -0.0011
##    200        0.7754             nan     0.1000   -0.0007
##    220        0.7679             nan     0.1000   -0.0010
##    240        0.7625             nan     0.1000   -0.0006
##    260        0.7519             nan     0.1000   -0.0007
##    280        0.7419             nan     0.1000   -0.0008
##    300        0.7343             nan     0.1000   -0.0005
##    320        0.7286             nan     0.1000   -0.0005
##    340        0.7247             nan     0.1000   -0.0006
##    360        0.7205             nan     0.1000   -0.0007
##    380        0.7155             nan     0.1000   -0.0003
##    400        0.7103             nan     0.1000   -0.0008
##    420        0.7062             nan     0.1000   -0.0009
##    440        0.7004             nan     0.1000   -0.0001
##    460        0.6974             nan     0.1000   -0.0020
##    480        0.6927             nan     0.1000   -0.0008
##    500        0.6878             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2580             nan     0.1000    0.0153
##      2        1.2295             nan     0.1000    0.0130
##      3        1.2030             nan     0.1000    0.0117
##      4        1.1828             nan     0.1000    0.0088
##      5        1.1610             nan     0.1000    0.0085
##      6        1.1432             nan     0.1000    0.0050
##      7        1.1268             nan     0.1000    0.0065
##      8        1.1149             nan     0.1000    0.0050
##      9        1.0991             nan     0.1000    0.0055
##     10        1.0866             nan     0.1000    0.0058
##     20        1.0016             nan     0.1000    0.0022
##     40        0.9177             nan     0.1000   -0.0004
##     60        0.8749             nan     0.1000   -0.0003
##     80        0.8437             nan     0.1000    0.0004
##    100        0.8248             nan     0.1000    0.0000
##    120        0.8090             nan     0.1000   -0.0001
##    140        0.7953             nan     0.1000   -0.0008
##    160        0.7845             nan     0.1000   -0.0010
##    180        0.7750             nan     0.1000   -0.0014
##    200        0.7663             nan     0.1000   -0.0013
##    220        0.7580             nan     0.1000   -0.0005
##    240        0.7509             nan     0.1000   -0.0005
##    260        0.7455             nan     0.1000   -0.0002
##    280        0.7391             nan     0.1000   -0.0004
##    300        0.7335             nan     0.1000   -0.0008
##    320        0.7276             nan     0.1000   -0.0011
##    340        0.7220             nan     0.1000   -0.0007
##    360        0.7157             nan     0.1000   -0.0006
##    380        0.7106             nan     0.1000   -0.0011
##    400        0.7065             nan     0.1000   -0.0014
##    420        0.7027             nan     0.1000   -0.0011
##    440        0.6974             nan     0.1000   -0.0000
##    460        0.6932             nan     0.1000   -0.0011
##    480        0.6873             nan     0.1000   -0.0008
##    500        0.6836             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2439             nan     0.1000    0.0204
##      2        1.2045             nan     0.1000    0.0167
##      3        1.1711             nan     0.1000    0.0140
##      4        1.1435             nan     0.1000    0.0098
##      5        1.1168             nan     0.1000    0.0113
##      6        1.0968             nan     0.1000    0.0077
##      7        1.0778             nan     0.1000    0.0071
##      8        1.0653             nan     0.1000    0.0031
##      9        1.0484             nan     0.1000    0.0058
##     10        1.0352             nan     0.1000    0.0045
##     20        0.9320             nan     0.1000    0.0014
##     40        0.8420             nan     0.1000    0.0003
##     60        0.7936             nan     0.1000   -0.0002
##     80        0.7567             nan     0.1000   -0.0016
##    100        0.7290             nan     0.1000   -0.0016
##    120        0.7029             nan     0.1000   -0.0009
##    140        0.6767             nan     0.1000   -0.0007
##    160        0.6534             nan     0.1000   -0.0014
##    180        0.6347             nan     0.1000   -0.0016
##    200        0.6173             nan     0.1000   -0.0006
##    220        0.5993             nan     0.1000   -0.0005
##    240        0.5813             nan     0.1000   -0.0009
##    260        0.5659             nan     0.1000    0.0000
##    280        0.5538             nan     0.1000   -0.0003
##    300        0.5378             nan     0.1000   -0.0009
##    320        0.5224             nan     0.1000   -0.0005
##    340        0.5111             nan     0.1000   -0.0011
##    360        0.4963             nan     0.1000   -0.0005
##    380        0.4849             nan     0.1000   -0.0013
##    400        0.4745             nan     0.1000   -0.0019
##    420        0.4621             nan     0.1000   -0.0014
##    440        0.4515             nan     0.1000   -0.0001
##    460        0.4407             nan     0.1000   -0.0005
##    480        0.4316             nan     0.1000   -0.0006
##    500        0.4210             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2470             nan     0.1000    0.0162
##      2        1.2135             nan     0.1000    0.0144
##      3        1.1796             nan     0.1000    0.0130
##      4        1.1534             nan     0.1000    0.0127
##      5        1.1259             nan     0.1000    0.0107
##      6        1.1003             nan     0.1000    0.0122
##      7        1.0779             nan     0.1000    0.0089
##      8        1.0565             nan     0.1000    0.0076
##      9        1.0390             nan     0.1000    0.0083
##     10        1.0249             nan     0.1000    0.0046
##     20        0.9352             nan     0.1000    0.0019
##     40        0.8366             nan     0.1000   -0.0016
##     60        0.7923             nan     0.1000   -0.0009
##     80        0.7563             nan     0.1000   -0.0013
##    100        0.7291             nan     0.1000   -0.0007
##    120        0.7056             nan     0.1000   -0.0014
##    140        0.6851             nan     0.1000   -0.0013
##    160        0.6576             nan     0.1000   -0.0007
##    180        0.6414             nan     0.1000   -0.0013
##    200        0.6198             nan     0.1000   -0.0007
##    220        0.6030             nan     0.1000   -0.0007
##    240        0.5911             nan     0.1000   -0.0013
##    260        0.5717             nan     0.1000   -0.0007
##    280        0.5559             nan     0.1000   -0.0019
##    300        0.5399             nan     0.1000   -0.0017
##    320        0.5252             nan     0.1000   -0.0012
##    340        0.5135             nan     0.1000   -0.0008
##    360        0.5015             nan     0.1000   -0.0013
##    380        0.4903             nan     0.1000   -0.0011
##    400        0.4770             nan     0.1000   -0.0007
##    420        0.4656             nan     0.1000   -0.0002
##    440        0.4539             nan     0.1000   -0.0010
##    460        0.4433             nan     0.1000   -0.0008
##    480        0.4320             nan     0.1000   -0.0006
##    500        0.4197             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2501             nan     0.1000    0.0137
##      2        1.2077             nan     0.1000    0.0186
##      3        1.1729             nan     0.1000    0.0159
##      4        1.1394             nan     0.1000    0.0127
##      5        1.1133             nan     0.1000    0.0119
##      6        1.0930             nan     0.1000    0.0084
##      7        1.0744             nan     0.1000    0.0088
##      8        1.0563             nan     0.1000    0.0073
##      9        1.0403             nan     0.1000    0.0051
##     10        1.0244             nan     0.1000    0.0059
##     20        0.9248             nan     0.1000    0.0021
##     40        0.8360             nan     0.1000    0.0007
##     60        0.7891             nan     0.1000   -0.0004
##     80        0.7508             nan     0.1000    0.0001
##    100        0.7294             nan     0.1000   -0.0009
##    120        0.7037             nan     0.1000   -0.0005
##    140        0.6828             nan     0.1000   -0.0015
##    160        0.6587             nan     0.1000   -0.0008
##    180        0.6376             nan     0.1000   -0.0003
##    200        0.6213             nan     0.1000   -0.0012
##    220        0.6053             nan     0.1000   -0.0021
##    240        0.5845             nan     0.1000   -0.0014
##    260        0.5719             nan     0.1000   -0.0012
##    280        0.5610             nan     0.1000   -0.0008
##    300        0.5481             nan     0.1000   -0.0003
##    320        0.5329             nan     0.1000   -0.0014
##    340        0.5190             nan     0.1000   -0.0012
##    360        0.5054             nan     0.1000   -0.0015
##    380        0.4921             nan     0.1000   -0.0005
##    400        0.4790             nan     0.1000   -0.0003
##    420        0.4667             nan     0.1000   -0.0002
##    440        0.4547             nan     0.1000   -0.0019
##    460        0.4468             nan     0.1000   -0.0009
##    480        0.4370             nan     0.1000   -0.0007
##    500        0.4250             nan     0.1000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2453             nan     0.1000    0.0202
##      2        1.2029             nan     0.1000    0.0185
##      3        1.1627             nan     0.1000    0.0169
##      4        1.1318             nan     0.1000    0.0123
##      5        1.1020             nan     0.1000    0.0133
##      6        1.0792             nan     0.1000    0.0075
##      7        1.0537             nan     0.1000    0.0101
##      8        1.0335             nan     0.1000    0.0075
##      9        1.0154             nan     0.1000    0.0074
##     10        0.9975             nan     0.1000    0.0066
##     20        0.8888             nan     0.1000    0.0001
##     40        0.7930             nan     0.1000   -0.0005
##     60        0.7375             nan     0.1000   -0.0015
##     80        0.6929             nan     0.1000   -0.0028
##    100        0.6567             nan     0.1000   -0.0021
##    120        0.6188             nan     0.1000   -0.0009
##    140        0.5859             nan     0.1000   -0.0010
##    160        0.5568             nan     0.1000   -0.0014
##    180        0.5348             nan     0.1000   -0.0017
##    200        0.5118             nan     0.1000   -0.0021
##    220        0.4938             nan     0.1000   -0.0023
##    240        0.4687             nan     0.1000   -0.0025
##    260        0.4488             nan     0.1000   -0.0007
##    280        0.4288             nan     0.1000   -0.0006
##    300        0.4118             nan     0.1000   -0.0014
##    320        0.3978             nan     0.1000   -0.0001
##    340        0.3783             nan     0.1000   -0.0005
##    360        0.3643             nan     0.1000   -0.0009
##    380        0.3489             nan     0.1000   -0.0010
##    400        0.3352             nan     0.1000   -0.0005
##    420        0.3242             nan     0.1000   -0.0009
##    440        0.3107             nan     0.1000   -0.0009
##    460        0.2991             nan     0.1000   -0.0009
##    480        0.2865             nan     0.1000   -0.0007
##    500        0.2750             nan     0.1000   -0.0012
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2343             nan     0.1000    0.0249
##      2        1.1899             nan     0.1000    0.0199
##      3        1.1527             nan     0.1000    0.0168
##      4        1.1228             nan     0.1000    0.0115
##      5        1.0949             nan     0.1000    0.0108
##      6        1.0709             nan     0.1000    0.0072
##      7        1.0472             nan     0.1000    0.0073
##      8        1.0312             nan     0.1000    0.0051
##      9        1.0138             nan     0.1000    0.0031
##     10        0.9964             nan     0.1000    0.0054
##     20        0.8973             nan     0.1000    0.0011
##     40        0.7976             nan     0.1000   -0.0029
##     60        0.7395             nan     0.1000   -0.0007
##     80        0.6956             nan     0.1000   -0.0014
##    100        0.6505             nan     0.1000   -0.0009
##    120        0.6175             nan     0.1000   -0.0017
##    140        0.5851             nan     0.1000   -0.0010
##    160        0.5619             nan     0.1000   -0.0007
##    180        0.5348             nan     0.1000   -0.0019
##    200        0.5133             nan     0.1000   -0.0009
##    220        0.4910             nan     0.1000   -0.0003
##    240        0.4673             nan     0.1000   -0.0013
##    260        0.4496             nan     0.1000   -0.0009
##    280        0.4258             nan     0.1000   -0.0007
##    300        0.4053             nan     0.1000   -0.0003
##    320        0.3865             nan     0.1000    0.0003
##    340        0.3726             nan     0.1000   -0.0017
##    360        0.3582             nan     0.1000   -0.0008
##    380        0.3430             nan     0.1000   -0.0007
##    400        0.3305             nan     0.1000   -0.0007
##    420        0.3177             nan     0.1000   -0.0006
##    440        0.3066             nan     0.1000   -0.0004
##    460        0.2937             nan     0.1000   -0.0006
##    480        0.2814             nan     0.1000   -0.0006
##    500        0.2713             nan     0.1000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2437             nan     0.1000    0.0240
##      2        1.2021             nan     0.1000    0.0186
##      3        1.1681             nan     0.1000    0.0117
##      4        1.1332             nan     0.1000    0.0111
##      5        1.1030             nan     0.1000    0.0117
##      6        1.0818             nan     0.1000    0.0077
##      7        1.0552             nan     0.1000    0.0112
##      8        1.0337             nan     0.1000    0.0079
##      9        1.0156             nan     0.1000    0.0047
##     10        1.0020             nan     0.1000    0.0026
##     20        0.8962             nan     0.1000   -0.0006
##     40        0.8014             nan     0.1000    0.0006
##     60        0.7438             nan     0.1000   -0.0012
##     80        0.6961             nan     0.1000   -0.0005
##    100        0.6560             nan     0.1000   -0.0006
##    120        0.6156             nan     0.1000   -0.0017
##    140        0.5785             nan     0.1000   -0.0006
##    160        0.5523             nan     0.1000   -0.0020
##    180        0.5266             nan     0.1000   -0.0008
##    200        0.5007             nan     0.1000   -0.0015
##    220        0.4756             nan     0.1000   -0.0011
##    240        0.4577             nan     0.1000   -0.0007
##    260        0.4353             nan     0.1000   -0.0002
##    280        0.4181             nan     0.1000   -0.0013
##    300        0.4031             nan     0.1000   -0.0014
##    320        0.3863             nan     0.1000   -0.0004
##    340        0.3685             nan     0.1000   -0.0007
##    360        0.3552             nan     0.1000   -0.0015
##    380        0.3386             nan     0.1000   -0.0001
##    400        0.3258             nan     0.1000   -0.0003
##    420        0.3135             nan     0.1000   -0.0007
##    440        0.3019             nan     0.1000   -0.0006
##    460        0.2879             nan     0.1000   -0.0005
##    480        0.2771             nan     0.1000   -0.0003
##    500        0.2668             nan     0.1000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2291             nan     0.2000    0.0307
##      2        1.1803             nan     0.2000    0.0242
##      3        1.1429             nan     0.2000    0.0170
##      4        1.1213             nan     0.2000    0.0085
##      5        1.0924             nan     0.2000    0.0110
##      6        1.0693             nan     0.2000    0.0064
##      7        1.0527             nan     0.2000    0.0039
##      8        1.0381             nan     0.2000    0.0042
##      9        1.0158             nan     0.2000    0.0105
##     10        1.0027             nan     0.2000    0.0049
##     20        0.9147             nan     0.2000    0.0030
##     40        0.8450             nan     0.2000    0.0003
##     60        0.8126             nan     0.2000   -0.0027
##     80        0.7917             nan     0.2000   -0.0018
##    100        0.7725             nan     0.2000   -0.0021
##    120        0.7583             nan     0.2000   -0.0009
##    140        0.7467             nan     0.2000   -0.0051
##    160        0.7325             nan     0.2000   -0.0012
##    180        0.7239             nan     0.2000   -0.0020
##    200        0.7160             nan     0.2000   -0.0025
##    220        0.7054             nan     0.2000   -0.0017
##    240        0.6973             nan     0.2000   -0.0020
##    260        0.6855             nan     0.2000   -0.0026
##    280        0.6734             nan     0.2000   -0.0006
##    300        0.6642             nan     0.2000   -0.0013
##    320        0.6578             nan     0.2000   -0.0005
##    340        0.6505             nan     0.2000    0.0002
##    360        0.6416             nan     0.2000   -0.0012
##    380        0.6353             nan     0.2000   -0.0006
##    400        0.6283             nan     0.2000   -0.0023
##    420        0.6233             nan     0.2000   -0.0027
##    440        0.6159             nan     0.2000   -0.0017
##    460        0.6108             nan     0.2000   -0.0011
##    480        0.6054             nan     0.2000   -0.0030
##    500        0.6004             nan     0.2000   -0.0016
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2269             nan     0.2000    0.0281
##      2        1.1770             nan     0.2000    0.0178
##      3        1.1455             nan     0.2000    0.0087
##      4        1.1140             nan     0.2000    0.0121
##      5        1.0880             nan     0.2000    0.0120
##      6        1.0649             nan     0.2000    0.0105
##      7        1.0447             nan     0.2000    0.0086
##      8        1.0282             nan     0.2000    0.0087
##      9        1.0181             nan     0.2000    0.0006
##     10        1.0047             nan     0.2000    0.0049
##     20        0.9257             nan     0.2000   -0.0001
##     40        0.8506             nan     0.2000   -0.0026
##     60        0.8100             nan     0.2000   -0.0016
##     80        0.7851             nan     0.2000   -0.0015
##    100        0.7669             nan     0.2000   -0.0010
##    120        0.7520             nan     0.2000   -0.0004
##    140        0.7403             nan     0.2000   -0.0019
##    160        0.7238             nan     0.2000   -0.0012
##    180        0.7145             nan     0.2000   -0.0017
##    200        0.7076             nan     0.2000   -0.0018
##    220        0.6949             nan     0.2000   -0.0012
##    240        0.6835             nan     0.2000   -0.0015
##    260        0.6745             nan     0.2000   -0.0017
##    280        0.6652             nan     0.2000   -0.0023
##    300        0.6576             nan     0.2000   -0.0012
##    320        0.6525             nan     0.2000   -0.0033
##    340        0.6459             nan     0.2000   -0.0019
##    360        0.6399             nan     0.2000   -0.0013
##    380        0.6329             nan     0.2000   -0.0021
##    400        0.6255             nan     0.2000   -0.0016
##    420        0.6202             nan     0.2000   -0.0019
##    440        0.6138             nan     0.2000   -0.0025
##    460        0.6088             nan     0.2000   -0.0022
##    480        0.6045             nan     0.2000   -0.0020
##    500        0.5997             nan     0.2000   -0.0022
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2282             nan     0.2000    0.0283
##      2        1.1790             nan     0.2000    0.0215
##      3        1.1374             nan     0.2000    0.0120
##      4        1.1090             nan     0.2000    0.0117
##      5        1.0852             nan     0.2000    0.0106
##      6        1.0605             nan     0.2000    0.0106
##      7        1.0432             nan     0.2000    0.0066
##      8        1.0369             nan     0.2000    0.0001
##      9        1.0243             nan     0.2000    0.0041
##     10        1.0096             nan     0.2000    0.0037
##     20        0.9222             nan     0.2000   -0.0018
##     40        0.8416             nan     0.2000    0.0003
##     60        0.8109             nan     0.2000   -0.0009
##     80        0.7915             nan     0.2000   -0.0032
##    100        0.7684             nan     0.2000   -0.0019
##    120        0.7542             nan     0.2000   -0.0037
##    140        0.7440             nan     0.2000   -0.0027
##    160        0.7293             nan     0.2000   -0.0022
##    180        0.7186             nan     0.2000   -0.0031
##    200        0.7080             nan     0.2000   -0.0018
##    220        0.6978             nan     0.2000   -0.0037
##    240        0.6916             nan     0.2000   -0.0014
##    260        0.6813             nan     0.2000   -0.0011
##    280        0.6725             nan     0.2000   -0.0005
##    300        0.6647             nan     0.2000   -0.0012
##    320        0.6581             nan     0.2000   -0.0021
##    340        0.6510             nan     0.2000   -0.0022
##    360        0.6445             nan     0.2000   -0.0012
##    380        0.6373             nan     0.2000   -0.0015
##    400        0.6337             nan     0.2000   -0.0014
##    420        0.6293             nan     0.2000   -0.0015
##    440        0.6224             nan     0.2000   -0.0029
##    460        0.6185             nan     0.2000   -0.0023
##    480        0.6133             nan     0.2000   -0.0012
##    500        0.6088             nan     0.2000   -0.0013
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2144             nan     0.2000    0.0356
##      2        1.1519             nan     0.2000    0.0326
##      3        1.1108             nan     0.2000    0.0177
##      4        1.0759             nan     0.2000    0.0125
##      5        1.0438             nan     0.2000    0.0127
##      6        1.0163             nan     0.2000    0.0079
##      7        0.9921             nan     0.2000    0.0105
##      8        0.9698             nan     0.2000    0.0086
##      9        0.9517             nan     0.2000    0.0053
##     10        0.9373             nan     0.2000    0.0040
##     20        0.8506             nan     0.2000   -0.0016
##     40        0.7576             nan     0.2000   -0.0009
##     60        0.7038             nan     0.2000   -0.0026
##     80        0.6661             nan     0.2000   -0.0024
##    100        0.6384             nan     0.2000   -0.0025
##    120           inf             nan     0.2000       nan
##    140           inf             nan     0.2000       nan
##    160           inf             nan     0.2000       nan
##    180           inf             nan     0.2000       nan
##    200           inf             nan     0.2000       nan
##    220           inf             nan     0.2000       nan
##    240           inf             nan     0.2000       nan
##    260           inf             nan     0.2000       nan
##    280           inf             nan     0.2000       nan
##    300           inf             nan     0.2000       nan
##    320           inf             nan     0.2000       nan
##    340           inf             nan     0.2000       nan
##    360           inf             nan     0.2000       nan
##    380           inf             nan     0.2000       nan
##    400           inf             nan     0.2000       nan
##    420           inf             nan     0.2000       nan
##    440           inf             nan     0.2000       nan
##    460           inf             nan     0.2000   -0.0005
##    480           inf             nan     0.2000       nan
##    500           inf             nan     0.2000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2105             nan     0.2000    0.0337
##      2        1.1650             nan     0.2000    0.0146
##      3        1.1212             nan     0.2000    0.0149
##      4        1.0782             nan     0.2000    0.0184
##      5        1.0362             nan     0.2000    0.0165
##      6        1.0035             nan     0.2000    0.0085
##      7        0.9821             nan     0.2000    0.0069
##      8        0.9658             nan     0.2000    0.0037
##      9        0.9465             nan     0.2000    0.0078
##     10        0.9337             nan     0.2000    0.0017
##     20        0.8491             nan     0.2000   -0.0004
##     40        0.7588             nan     0.2000   -0.0042
##     60        0.7005             nan     0.2000   -0.0010
##     80        0.6614             nan     0.2000   -0.0033
##    100        0.6220             nan     0.2000   -0.0034
##    120        0.5943             nan     0.2000   -0.0045
##    140        0.5673             nan     0.2000   -0.0022
##    160        0.5398             nan     0.2000   -0.0023
##    180        0.5184             nan     0.2000   -0.0015
##    200        0.4941             nan     0.2000   -0.0023
##    220        0.4734             nan     0.2000   -0.0017
##    240        0.4563             nan     0.2000   -0.0034
##    260        0.4376             nan     0.2000   -0.0005
##    280        0.4189             nan     0.2000   -0.0014
##    300        0.4011             nan     0.2000   -0.0018
##    320        0.3836             nan     0.2000   -0.0015
##    340        0.3680             nan     0.2000   -0.0009
##    360        0.3501             nan     0.2000   -0.0009
##    380        0.3361             nan     0.2000   -0.0015
##    400        0.3258             nan     0.2000   -0.0010
##    420        0.3146             nan     0.2000   -0.0015
##    440        0.3014             nan     0.2000   -0.0008
##    460        0.2824             nan     0.2000   -0.0018
##    480        0.2719             nan     0.2000   -0.0014
##    500        0.2629             nan     0.2000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2105             nan     0.2000    0.0339
##      2        1.1512             nan     0.2000    0.0228
##      3        1.1013             nan     0.2000    0.0260
##      4        1.0662             nan     0.2000    0.0123
##      5        1.0373             nan     0.2000    0.0106
##      6        1.0147             nan     0.2000    0.0088
##      7        0.9949             nan     0.2000    0.0037
##      8        0.9734             nan     0.2000    0.0028
##      9        0.9633             nan     0.2000    0.0007
##     10        0.9420             nan     0.2000    0.0085
##     20        0.8577             nan     0.2000   -0.0002
##     40        0.7715             nan     0.2000   -0.0033
##     60        0.7165             nan     0.2000   -0.0015
##     80        0.6665             nan     0.2000   -0.0021
##    100        0.6296             nan     0.2000   -0.0018
##    120        0.6005             nan     0.2000   -0.0016
##    140        0.5703             nan     0.2000   -0.0030
##    160        0.5465             nan     0.2000   -0.0031
##    180        0.5197             nan     0.2000   -0.0007
##    200        0.5004             nan     0.2000   -0.0013
##    220        0.4718             nan     0.2000   -0.0040
##    240        0.4540             nan     0.2000   -0.0013
##    260        0.4331             nan     0.2000   -0.0022
##    280        0.4122             nan     0.2000   -0.0007
##    300        0.3943             nan     0.2000   -0.0023
##    320        0.3788             nan     0.2000   -0.0025
##    340        0.3614             nan     0.2000   -0.0006
##    360        0.3445             nan     0.2000   -0.0035
##    380        0.3300             nan     0.2000   -0.0028
##    400        0.3169             nan     0.2000   -0.0010
##    420        0.3006             nan     0.2000   -0.0025
##    440        0.2895             nan     0.2000   -0.0021
##    460        0.2784             nan     0.2000   -0.0027
##    480        0.2663             nan     0.2000   -0.0004
##    500        0.2530             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1905             nan     0.2000    0.0381
##      2        1.1121             nan     0.2000    0.0309
##      3        1.0615             nan     0.2000    0.0200
##      4        1.0282             nan     0.2000    0.0121
##      5        0.9988             nan     0.2000    0.0124
##      6        0.9723             nan     0.2000    0.0099
##      7        0.9450             nan     0.2000    0.0080
##      8        0.9196             nan     0.2000    0.0071
##      9        0.9033             nan     0.2000    0.0013
##     10        0.8889             nan     0.2000    0.0017
##     20        0.8008             nan     0.2000   -0.0008
##     40        0.7010             nan     0.2000   -0.0030
##     60        0.6082             nan     0.2000   -0.0033
##     80        0.5405             nan     0.2000   -0.0042
##    100        0.4939             nan     0.2000   -0.0026
##    120        0.4445             nan     0.2000   -0.0003
##    140        0.4113             nan     0.2000   -0.0015
##    160        0.3762             nan     0.2000    0.0000
##    180        0.3454             nan     0.2000   -0.0018
##    200        0.3169             nan     0.2000   -0.0021
##    220        0.2964             nan     0.2000   -0.0008
##    240        0.2747             nan     0.2000   -0.0002
##    260        0.2524             nan     0.2000   -0.0014
##    280        0.2354             nan     0.2000   -0.0009
##    300        0.2218             nan     0.2000   -0.0012
##    320        0.2043             nan     0.2000   -0.0013
##    340        0.1908             nan     0.2000   -0.0013
##    360        0.1798             nan     0.2000   -0.0014
##    380        0.1683             nan     0.2000   -0.0004
##    400        0.1582             nan     0.2000   -0.0005
##    420        0.1468             nan     0.2000   -0.0010
##    440        0.1380             nan     0.2000   -0.0013
##    460        0.1289             nan     0.2000   -0.0012
##    480        0.1211             nan     0.2000   -0.0004
##    500        0.1139             nan     0.2000   -0.0010
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1951             nan     0.2000    0.0486
##      2        1.1185             nan     0.2000    0.0344
##      3        1.0683             nan     0.2000    0.0191
##      4        1.0322             nan     0.2000    0.0095
##      5        0.9970             nan     0.2000    0.0086
##      6        0.9681             nan     0.2000    0.0116
##      7        0.9470             nan     0.2000    0.0034
##      8        0.9252             nan     0.2000    0.0041
##      9        0.9112             nan     0.2000   -0.0000
##     10        0.9033             nan     0.2000   -0.0044
##     20        0.7925             nan     0.2000    0.0010
##     40        0.6873             nan     0.2000   -0.0045
##     60        0.6172             nan     0.2000   -0.0055
##     80        0.5596             nan     0.2000   -0.0012
##    100        0.5048             nan     0.2000   -0.0036
##    120        0.4620             nan     0.2000   -0.0013
##    140        0.4264             nan     0.2000   -0.0037
##    160        0.3964             nan     0.2000   -0.0018
##    180        0.3723             nan     0.2000   -0.0017
##    200        0.3427             nan     0.2000   -0.0010
##    220        0.3161             nan     0.2000   -0.0020
##    240        0.2911             nan     0.2000   -0.0020
##    260        0.2714             nan     0.2000   -0.0009
##    280        0.2506             nan     0.2000   -0.0026
##    300        0.2326             nan     0.2000   -0.0020
##    320        0.2157             nan     0.2000   -0.0012
##    340        0.2027             nan     0.2000   -0.0009
##    360        0.1899             nan     0.2000   -0.0018
##    380        0.1750             nan     0.2000   -0.0014
##    400        0.1633             nan     0.2000   -0.0004
##    420        0.1540             nan     0.2000   -0.0004
##    440        0.1430             nan     0.2000   -0.0003
##    460        0.1319             nan     0.2000   -0.0007
##    480        0.1245             nan     0.2000   -0.0002
##    500        0.1171             nan     0.2000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1948             nan     0.2000    0.0448
##      2        1.1231             nan     0.2000    0.0307
##      3        1.0650             nan     0.2000    0.0231
##      4        1.0257             nan     0.2000    0.0122
##      5        0.9942             nan     0.2000    0.0126
##      6        0.9723             nan     0.2000    0.0053
##      7        0.9529             nan     0.2000    0.0077
##      8        0.9384             nan     0.2000    0.0017
##      9        0.9201             nan     0.2000    0.0049
##     10        0.9047             nan     0.2000    0.0019
##     20        0.8068             nan     0.2000   -0.0023
##     40        0.6881             nan     0.2000    0.0002
##     60        0.6281             nan     0.2000   -0.0007
##     80        0.5729             nan     0.2000   -0.0018
##    100        0.5196             nan     0.2000   -0.0023
##    120        0.4729             nan     0.2000   -0.0013
##    140        0.4342             nan     0.2000   -0.0008
##    160        0.4068             nan     0.2000   -0.0025
##    180        0.3750             nan     0.2000   -0.0021
##    200        0.3488             nan     0.2000   -0.0007
##    220        0.3213             nan     0.2000   -0.0026
##    240        0.2968             nan     0.2000   -0.0032
##    260        0.2711             nan     0.2000   -0.0006
##    280        0.2513             nan     0.2000   -0.0006
##    300        0.2334             nan     0.2000   -0.0003
##    320        0.2155             nan     0.2000   -0.0006
##    340        0.1983             nan     0.2000   -0.0006
##    360        0.1871             nan     0.2000   -0.0007
##    380        0.1772             nan     0.2000   -0.0008
##    400        0.1642             nan     0.2000   -0.0004
##    420        0.1561             nan     0.2000   -0.0009
##    440        0.1444             nan     0.2000   -0.0003
##    460        0.1371             nan     0.2000   -0.0007
##    480        0.1285             nan     0.2000   -0.0002
##    500        0.1212             nan     0.2000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2037             nan     0.3000    0.0323
##      2        1.1443             nan     0.3000    0.0269
##      3        1.1033             nan     0.3000    0.0160
##      4        1.0688             nan     0.3000    0.0125
##      5        1.0412             nan     0.3000    0.0083
##      6        1.0085             nan     0.3000    0.0137
##      7        0.9895             nan     0.3000    0.0081
##      8        0.9811             nan     0.3000    0.0020
##      9        0.9695             nan     0.3000    0.0013
##     10        0.9554             nan     0.3000   -0.0005
##     20        0.8809             nan     0.3000   -0.0039
##     40        0.8179             nan     0.3000   -0.0030
##     60        0.7808             nan     0.3000   -0.0016
##     80        0.7626             nan     0.3000   -0.0029
##    100        0.7418             nan     0.3000   -0.0049
##    120        0.7300             nan     0.3000   -0.0018
##    140        0.7113             nan     0.3000   -0.0035
##    160        0.7008             nan     0.3000   -0.0028
##    180        0.6816             nan     0.3000   -0.0017
##    200        0.6689             nan     0.3000   -0.0024
##    220        0.6660             nan     0.3000   -0.0037
##    240        0.6554             nan     0.3000   -0.0033
##    260        0.6485             nan     0.3000   -0.0062
##    280        0.6407             nan     0.3000   -0.0012
##    300        0.6311             nan     0.3000   -0.0055
##    320        0.6247             nan     0.3000   -0.0042
##    340        0.6181             nan     0.3000   -0.0046
##    360        0.6068             nan     0.3000   -0.0041
##    380        0.5982             nan     0.3000   -0.0057
##    400        0.5952             nan     0.3000   -0.0037
##    420        0.5853             nan     0.3000   -0.0026
##    440        0.5758             nan     0.3000   -0.0019
##    460        0.5698             nan     0.3000   -0.0010
##    480        0.5639             nan     0.3000   -0.0030
##    500        0.5581             nan     0.3000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1961             nan     0.3000    0.0434
##      2        1.1558             nan     0.3000    0.0142
##      3        1.1101             nan     0.3000    0.0234
##      4        1.0753             nan     0.3000    0.0141
##      5        1.0486             nan     0.3000    0.0046
##      6        1.0204             nan     0.3000    0.0151
##      7        1.0093             nan     0.3000   -0.0006
##      8        0.9944             nan     0.3000    0.0057
##      9        0.9827             nan     0.3000    0.0020
##     10        0.9694             nan     0.3000    0.0039
##     20        0.8806             nan     0.3000   -0.0000
##     40        0.8202             nan     0.3000   -0.0000
##     60        0.7938             nan     0.3000   -0.0022
##     80        0.7751             nan     0.3000   -0.0046
##    100        0.7529             nan     0.3000   -0.0055
##    120        0.7317             nan     0.3000   -0.0025
##    140        0.7103             nan     0.3000   -0.0040
##    160        0.7004             nan     0.3000   -0.0023
##    180        0.6894             nan     0.3000   -0.0043
##    200        0.6789             nan     0.3000   -0.0011
##    220        0.6675             nan     0.3000   -0.0017
##    240        0.6557             nan     0.3000   -0.0033
##    260        0.6443             nan     0.3000   -0.0037
##    280        0.6321             nan     0.3000   -0.0023
##    300        0.6258             nan     0.3000   -0.0004
##    320        0.6141             nan     0.3000   -0.0016
##    340        0.6101             nan     0.3000   -0.0023
##    360        0.5968             nan     0.3000   -0.0024
##    380        0.5871             nan     0.3000   -0.0010
##    400        0.5800             nan     0.3000   -0.0026
##    420        0.5754             nan     0.3000   -0.0029
##    440        0.5721             nan     0.3000   -0.0035
##    460        0.5612             nan     0.3000   -0.0016
##    480        0.5531             nan     0.3000   -0.0021
##    500        0.5451             nan     0.3000   -0.0034
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1997             nan     0.3000    0.0445
##      2        1.1381             nan     0.3000    0.0300
##      3        1.1002             nan     0.3000    0.0145
##      4        1.0661             nan     0.3000    0.0122
##      5        1.0402             nan     0.3000    0.0091
##      6        1.0088             nan     0.3000    0.0147
##      7        0.9897             nan     0.3000    0.0063
##      8        0.9747             nan     0.3000    0.0068
##      9        0.9628             nan     0.3000   -0.0008
##     10        0.9498             nan     0.3000   -0.0013
##     20        0.8786             nan     0.3000   -0.0008
##     40        0.8267             nan     0.3000   -0.0010
##     60        0.7979             nan     0.3000   -0.0026
##     80        0.7741             nan     0.3000   -0.0037
##    100        0.7568             nan     0.3000   -0.0023
##    120        0.7332             nan     0.3000   -0.0015
##    140        0.7134             nan     0.3000   -0.0058
##    160        0.6985             nan     0.3000   -0.0029
##    180        0.6884             nan     0.3000   -0.0042
##    200        0.6766             nan     0.3000   -0.0017
##    220        0.6652             nan     0.3000   -0.0028
##    240        0.6544             nan     0.3000   -0.0020
##    260        0.6450             nan     0.3000   -0.0029
##    280        0.6337             nan     0.3000   -0.0039
##    300        0.6247             nan     0.3000   -0.0016
##    320        0.6183             nan     0.3000   -0.0049
##    340        0.6104             nan     0.3000   -0.0020
##    360        0.6048             nan     0.3000   -0.0032
##    380        0.5975             nan     0.3000   -0.0003
##    400        0.5904             nan     0.3000   -0.0039
##    420        0.5812             nan     0.3000   -0.0021
##    440        0.5752             nan     0.3000   -0.0033
##    460        0.5692             nan     0.3000   -0.0023
##    480        0.5644             nan     0.3000   -0.0034
##    500        0.5559             nan     0.3000   -0.0015
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1925             nan     0.3000    0.0519
##      2        1.1076             nan     0.3000    0.0335
##      3        1.0397             nan     0.3000    0.0257
##      4        1.0068             nan     0.3000    0.0055
##      5        0.9863             nan     0.3000    0.0061
##      6        0.9615             nan     0.3000    0.0078
##      7        0.9354             nan     0.3000    0.0099
##      8        0.9145             nan     0.3000    0.0045
##      9        0.9020             nan     0.3000   -0.0007
##     10        0.8886             nan     0.3000   -0.0029
##     20        0.8161             nan     0.3000   -0.0042
##     40        0.7235             nan     0.3000    0.0003
##     60        0.6612             nan     0.3000   -0.0046
##     80        0.6120             nan     0.3000   -0.0060
##    100        0.5730             nan     0.3000   -0.0030
##    120        0.5181             nan     0.3000   -0.0049
##    140        0.4842             nan     0.3000   -0.0034
##    160        0.4582             nan     0.3000   -0.0082
##    180        0.4243             nan     0.3000   -0.0031
##    200        0.3906             nan     0.3000   -0.0029
##    220        0.3652             nan     0.3000   -0.0030
##    240        0.3411             nan     0.3000   -0.0009
##    260        0.3138             nan     0.3000   -0.0010
##    280        0.2995             nan     0.3000   -0.0015
##    300        0.2809             nan     0.3000   -0.0018
##    320        0.2645             nan     0.3000   -0.0014
##    340        0.2543             nan     0.3000   -0.0024
##    360        0.2341             nan     0.3000   -0.0007
##    380        0.2212             nan     0.3000   -0.0014
##    400        0.2082             nan     0.3000   -0.0007
##    420        0.1952             nan     0.3000   -0.0011
##    440        0.1851             nan     0.3000   -0.0010
##    460        0.1804             nan     0.3000   -0.0019
##    480        0.1708             nan     0.3000   -0.0011
##    500        0.1601             nan     0.3000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1784             nan     0.3000    0.0560
##      2        1.1007             nan     0.3000    0.0311
##      3        1.0620             nan     0.3000    0.0145
##      4        1.0232             nan     0.3000    0.0123
##      5        0.9859             nan     0.3000    0.0133
##      6        0.9686             nan     0.3000   -0.0019
##      7        0.9421             nan     0.3000    0.0041
##      8        0.9249             nan     0.3000    0.0043
##      9        0.9099             nan     0.3000   -0.0033
##     10        0.8981             nan     0.3000   -0.0003
##     20        0.8229             nan     0.3000   -0.0031
##     40        0.7314             nan     0.3000   -0.0007
##     60        0.6636             nan     0.3000   -0.0024
##     80        0.6142             nan     0.3000   -0.0034
##    100        0.5691             nan     0.3000   -0.0045
##    120        0.5304             nan     0.3000   -0.0035
##    140        0.4956             nan     0.3000   -0.0014
##    160        0.4733             nan     0.3000   -0.0046
##    180        0.4403             nan     0.3000   -0.0057
##    200        0.4093             nan     0.3000   -0.0064
##    220        0.3885             nan     0.3000   -0.0045
##    240        0.3586             nan     0.3000   -0.0013
##    260        0.3330             nan     0.3000   -0.0009
##    280        0.3137             nan     0.3000   -0.0037
##    300        0.2946             nan     0.3000   -0.0027
##    320        0.2778             nan     0.3000   -0.0025
##    340        0.2638             nan     0.3000   -0.0014
##    360        0.2457             nan     0.3000   -0.0015
##    380        0.2294             nan     0.3000   -0.0011
##    400        0.2176             nan     0.3000   -0.0012
##    420        0.2086             nan     0.3000   -0.0013
##    440        0.1934             nan     0.3000   -0.0013
##    460        0.1806             nan     0.3000   -0.0017
##    480        0.1736             nan     0.3000   -0.0012
##    500        0.1630             nan     0.3000   -0.0020
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1756             nan     0.3000    0.0472
##      2        1.0841             nan     0.3000    0.0474
##      3        1.0349             nan     0.3000    0.0157
##      4        1.0011             nan     0.3000    0.0105
##      5        0.9628             nan     0.3000    0.0094
##      6        0.9441             nan     0.3000   -0.0015
##      7        0.9243             nan     0.3000    0.0061
##      8        0.9100             nan     0.3000   -0.0016
##      9        0.8942             nan     0.3000    0.0046
##     10        0.8795             nan     0.3000    0.0030
##     20        0.8022             nan     0.3000   -0.0002
##     40        0.7296             nan     0.3000   -0.0048
##     60        0.6693             nan     0.3000   -0.0040
##     80        0.6075             nan     0.3000   -0.0027
##    100        0.5622             nan     0.3000   -0.0026
##    120        0.5185             nan     0.3000   -0.0029
##    140        0.4888             nan     0.3000   -0.0037
##    160        0.4561             nan     0.3000   -0.0061
##    180        0.4184             nan     0.3000   -0.0016
##    200        0.3929             nan     0.3000   -0.0032
##    220        0.3659             nan     0.3000   -0.0038
##    240        0.3389             nan     0.3000   -0.0027
##    260        0.3176             nan     0.3000   -0.0028
##    280        0.2968             nan     0.3000   -0.0007
##    300        0.2811             nan     0.3000   -0.0032
##    320        0.2684             nan     0.3000   -0.0010
##    340        0.2414             nan     0.3000   -0.0022
##    360        0.2253             nan     0.3000   -0.0012
##    380        0.2154             nan     0.3000   -0.0014
##    400        0.2009             nan     0.3000   -0.0022
##    420        0.1898             nan     0.3000   -0.0017
##    440        0.1775             nan     0.3000   -0.0007
##    460        0.1661             nan     0.3000   -0.0014
##    480        0.1559             nan     0.3000   -0.0009
##    500        0.1485             nan     0.3000   -0.0014
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1489             nan     0.3000    0.0575
##      2        1.0810             nan     0.3000    0.0150
##      3        1.0215             nan     0.3000    0.0221
##      4        0.9880             nan     0.3000    0.0085
##      5        0.9505             nan     0.3000    0.0080
##      6        0.9192             nan     0.3000    0.0047
##      7        0.8989             nan     0.3000    0.0020
##      8        0.8734             nan     0.3000    0.0072
##      9        0.8593             nan     0.3000    0.0020
##     10        0.8492             nan     0.3000   -0.0029
##     20        0.7518             nan     0.3000   -0.0026
##     40        0.6283             nan     0.3000   -0.0027
##     60        0.5543             nan     0.3000   -0.0044
##     80        0.4885             nan     0.3000   -0.0026
##    100        0.4353             nan     0.3000   -0.0027
##    120        0.4278             nan     0.3000   -0.0530
##    140           inf             nan     0.3000       nan
##    160           inf             nan     0.3000       nan
##    180           inf             nan     0.3000       nan
##    200           inf             nan     0.3000       nan
##    220           inf             nan     0.3000       nan
##    240           inf             nan     0.3000       nan
##    260           inf             nan     0.3000       nan
##    280           inf             nan     0.3000       nan
##    300           inf             nan     0.3000       nan
##    320           inf             nan     0.3000       nan
##    340           inf             nan     0.3000       nan
##    360           inf             nan     0.3000       nan
##    380           inf             nan     0.3000       nan
##    400           inf             nan     0.3000       nan
##    420           inf             nan     0.3000       nan
##    440           inf             nan     0.3000       nan
##    460           inf             nan     0.3000       nan
##    480           inf             nan     0.3000       nan
##    500           inf             nan     0.3000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1480             nan     0.3000    0.0449
##      2        1.0752             nan     0.3000    0.0341
##      3        1.0123             nan     0.3000    0.0295
##      4        0.9646             nan     0.3000    0.0154
##      5        0.9343             nan     0.3000    0.0064
##      6        0.9154             nan     0.3000    0.0012
##      7        0.8873             nan     0.3000    0.0031
##      8        0.8673             nan     0.3000   -0.0017
##      9        0.8517             nan     0.3000    0.0018
##     10        0.8324             nan     0.3000    0.0015
##     20        0.7484             nan     0.3000   -0.0057
##     40        0.6361             nan     0.3000   -0.0021
##     60        0.5412             nan     0.3000   -0.0047
##     80        0.4589             nan     0.3000   -0.0036
##    100        0.3912             nan     0.3000   -0.0015
##    120        0.3591             nan     0.3000   -0.0003
##    140        0.3153             nan     0.3000   -0.0010
##    160        0.2847             nan     0.3000   -0.0024
##    180        0.2522             nan     0.3000   -0.0030
##    200        0.2239             nan     0.3000   -0.0036
##    220        0.2033             nan     0.3000   -0.0014
##    240        0.1802             nan     0.3000   -0.0020
##    260        0.1620             nan     0.3000   -0.0028
##    280        0.1452             nan     0.3000   -0.0013
##    300        0.1339             nan     0.3000   -0.0017
##    320        0.1206             nan     0.3000   -0.0007
##    340        0.1084             nan     0.3000   -0.0008
##    360        0.0997             nan     0.3000   -0.0009
##    380        0.0920             nan     0.3000   -0.0007
##    400        0.0842             nan     0.3000   -0.0010
##    420        0.0756             nan     0.3000   -0.0003
##    440        0.0695             nan     0.3000   -0.0006
##    460        0.0626             nan     0.3000   -0.0006
##    480        0.0566             nan     0.3000   -0.0002
##    500        0.0521             nan     0.3000   -0.0007
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1639             nan     0.3000    0.0597
##      2        1.0858             nan     0.3000    0.0312
##      3        1.0292             nan     0.3000    0.0208
##      4        0.9839             nan     0.3000    0.0132
##      5        0.9534             nan     0.3000    0.0047
##      6        0.9225             nan     0.3000    0.0072
##      7        0.8959             nan     0.3000    0.0079
##      8        0.8830             nan     0.3000   -0.0067
##      9        0.8630             nan     0.3000   -0.0006
##     10        0.8449             nan     0.3000    0.0008
##     20        0.7561             nan     0.3000   -0.0088
##     40        0.6544             nan     0.3000   -0.0013
##     60        0.5660             nan     0.3000   -0.0031
##     80        0.4940             nan     0.3000   -0.0027
##    100        0.4311             nan     0.3000   -0.0019
##    120        0.3782             nan     0.3000   -0.0008
##    140        0.3432             nan     0.3000   -0.0035
##    160        0.3074             nan     0.3000   -0.0033
##    180        0.2691             nan     0.3000    0.0005
##    200        0.2425             nan     0.3000   -0.0016
##    220        0.2128             nan     0.3000   -0.0014
##    240        0.1894             nan     0.3000   -0.0013
##    260        0.1706             nan     0.3000   -0.0014
##    280        0.1544             nan     0.3000   -0.0018
##    300        0.1410             nan     0.3000   -0.0002
##    320        0.1275             nan     0.3000   -0.0017
##    340        0.1133             nan     0.3000   -0.0007
##    360        0.1022             nan     0.3000   -0.0010
##    380        0.0952             nan     0.3000   -0.0003
##    400        0.0851             nan     0.3000   -0.0010
##    420        0.0781             nan     0.3000   -0.0003
##    440        0.0717             nan     0.3000   -0.0005
##    460        0.0664             nan     0.3000   -0.0005
##    480        0.0618             nan     0.3000   -0.0004
##    500        0.0568             nan     0.3000   -0.0003
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1621             nan     0.5000    0.0526
##      2        1.0872             nan     0.5000    0.0250
##      3        1.0406             nan     0.5000    0.0138
##      4        1.0009             nan     0.5000    0.0161
##      5        0.9907             nan     0.5000   -0.0048
##      6        0.9686             nan     0.5000    0.0084
##      7        0.9571             nan     0.5000    0.0023
##      8        0.9432             nan     0.5000    0.0049
##      9        0.9251             nan     0.5000    0.0064
##     10        0.9151             nan     0.5000   -0.0014
##     20        0.8568             nan     0.5000   -0.0007
##     40        0.7981             nan     0.5000   -0.0000
##     60        0.7566             nan     0.5000   -0.0074
##     80        0.7211             nan     0.5000   -0.0033
##    100        0.7023             nan     0.5000   -0.0105
##    120        0.6826             nan     0.5000   -0.0068
##    140        0.6627             nan     0.5000   -0.0018
##    160        0.6358             nan     0.5000   -0.0005
##    180        0.6163             nan     0.5000    0.0014
##    200        0.6066             nan     0.5000   -0.0045
##    220        0.5973             nan     0.5000   -0.0039
##    240        0.5887             nan     0.5000   -0.0062
##    260        0.5762             nan     0.5000   -0.0065
##    280        0.5610             nan     0.5000   -0.0051
##    300        0.5380             nan     0.5000   -0.0040
##    320        0.5270             nan     0.5000   -0.0018
##    340        0.5166             nan     0.5000   -0.0039
##    360        0.5123             nan     0.5000   -0.0012
##    380        0.5128             nan     0.5000   -0.0061
##    400        0.4949             nan     0.5000   -0.0028
##    420        0.4869             nan     0.5000   -0.0027
##    440        0.4840             nan     0.5000   -0.0072
##    460        0.4714             nan     0.5000   -0.0017
##    480        0.4727             nan     0.5000   -0.0077
##    500        0.4588             nan     0.5000   -0.0028
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1616             nan     0.5000    0.0617
##      2        1.1096             nan     0.5000    0.0198
##      3        1.0570             nan     0.5000    0.0232
##      4        1.0099             nan     0.5000    0.0197
##      5        0.9853             nan     0.5000    0.0073
##      6        0.9590             nan     0.5000    0.0041
##      7        0.9422             nan     0.5000    0.0014
##      8        0.9308             nan     0.5000   -0.0061
##      9        0.9204             nan     0.5000   -0.0049
##     10        0.9079             nan     0.5000   -0.0007
##     20        0.8445             nan     0.5000   -0.0001
##     40        0.7878             nan     0.5000   -0.0052
##     60        0.7579             nan     0.5000   -0.0064
##     80        0.7323             nan     0.5000   -0.0114
##    100        0.7047             nan     0.5000   -0.0037
##    120        0.6922             nan     0.5000   -0.0059
##    140        0.6768             nan     0.5000   -0.0049
##    160        0.6687             nan     0.5000   -0.0100
##    180        0.6421             nan     0.5000   -0.0051
##    200        0.6374             nan     0.5000   -0.0108
##    220        0.6152             nan     0.5000   -0.0097
##    240        0.5998             nan     0.5000   -0.0012
##    260        0.5839             nan     0.5000   -0.0030
##    280        0.5871             nan     0.5000   -0.0092
##    300        0.5650             nan     0.5000   -0.0061
##    320        0.5595             nan     0.5000   -0.0043
##    340        0.5522             nan     0.5000   -0.0098
##    360        0.5388             nan     0.5000   -0.0041
##    380        0.5279             nan     0.5000   -0.0015
##    400        0.5244             nan     0.5000   -0.0036
##    420        0.5140             nan     0.5000   -0.0047
##    440        0.5125             nan     0.5000   -0.0027
##    460        0.5028             nan     0.5000   -0.0066
##    480        0.4993             nan     0.5000   -0.0070
##    500        0.4811             nan     0.5000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1491             nan     0.5000    0.0674
##      2        1.0855             nan     0.5000    0.0276
##      3        1.0345             nan     0.5000    0.0131
##      4        0.9923             nan     0.5000    0.0189
##      5        0.9727             nan     0.5000   -0.0011
##      6        0.9558             nan     0.5000   -0.0027
##      7        0.9397             nan     0.5000    0.0036
##      8        0.9265             nan     0.5000   -0.0002
##      9        0.9153             nan     0.5000    0.0017
##     10        0.9081             nan     0.5000   -0.0042
##     20        0.8416             nan     0.5000   -0.0062
##     40        0.7958             nan     0.5000   -0.0057
##     60        0.7734             nan     0.5000   -0.0047
##     80        0.7368             nan     0.5000   -0.0014
##    100        0.7091             nan     0.5000   -0.0083
##    120        0.6922             nan     0.5000   -0.0092
##    140        0.6800             nan     0.5000   -0.0074
##    160        0.6556             nan     0.5000   -0.0039
##    180        0.6438             nan     0.5000   -0.0033
##    200        0.6192             nan     0.5000   -0.0048
##    220        0.6020             nan     0.5000   -0.0044
##    240        0.5837             nan     0.5000   -0.0015
##    260        0.5667             nan     0.5000   -0.0087
##    280        0.5639             nan     0.5000   -0.0053
##    300        0.5547             nan     0.5000   -0.0076
##    320        0.5412             nan     0.5000   -0.0014
##    340        0.5339             nan     0.5000   -0.0051
##    360        0.5188             nan     0.5000   -0.0022
##    380        0.5123             nan     0.5000   -0.0053
##    400        0.5062             nan     0.5000   -0.0057
##    420        0.4992             nan     0.5000   -0.0016
##    440        0.4960             nan     0.5000   -0.0033
##    460        0.4879             nan     0.5000   -0.0040
##    480        0.4822             nan     0.5000   -0.0080
##    500        0.4745             nan     0.5000   -0.0009
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1221             nan     0.5000    0.0846
##      2        1.0428             nan     0.5000    0.0314
##      3        0.9910             nan     0.5000    0.0195
##      4        0.9434             nan     0.5000    0.0151
##      5        0.9178             nan     0.5000    0.0067
##      6        0.9030             nan     0.5000   -0.0016
##      7        0.8784             nan     0.5000   -0.0025
##      8        0.8639             nan     0.5000   -0.0023
##      9        0.8535             nan     0.5000   -0.0052
##     10        0.8464             nan     0.5000   -0.0068
##     20        0.7804             nan     0.5000   -0.0043
##     40        0.7210             nan     0.5000   -0.0092
##     60        0.6204             nan     0.5000   -0.0157
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000       nan
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000       nan
##    280           inf             nan     0.5000       nan
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1377             nan     0.5000    0.0864
##      2        1.0525             nan     0.5000    0.0347
##      3        0.9951             nan     0.5000    0.0167
##      4        0.9563             nan     0.5000    0.0003
##      5        0.9181             nan     0.5000    0.0090
##      6        0.8996             nan     0.5000   -0.0018
##      7        0.8749             nan     0.5000    0.0015
##      8        0.8616             nan     0.5000   -0.0022
##      9        0.8531             nan     0.5000   -0.0054
##     10        0.8339             nan     0.5000    0.0048
##     20        0.7750             nan     0.5000    0.0035
##     40        0.6588             nan     0.5000   -0.0092
##     60        0.5606             nan     0.5000   -0.0032
##     80        0.5075             nan     0.5000   -0.0099
##    100        0.4678             nan     0.5000   -0.0051
##    120        0.4165             nan     0.5000   -0.0051
##    140        0.3823             nan     0.5000   -0.0061
##    160        0.3345             nan     0.5000   -0.0066
##    180        0.3057             nan     0.5000   -0.0052
##    200        0.2787             nan     0.5000   -0.0030
##    220        0.2563             nan     0.5000   -0.0027
##    240        0.2221             nan     0.5000   -0.0045
##    260        0.1988             nan     0.5000   -0.0026
##    280        0.1825             nan     0.5000   -0.0016
##    300        0.1622             nan     0.5000   -0.0028
##    320        0.1489             nan     0.5000   -0.0016
##    340        0.1387             nan     0.5000   -0.0009
##    360        0.1273             nan     0.5000   -0.0035
##    380        0.1144             nan     0.5000   -0.0003
##    400        0.1072             nan     0.5000   -0.0022
##    420        0.0965             nan     0.5000   -0.0012
##    440        0.0903             nan     0.5000   -0.0024
##    460        0.0853             nan     0.5000   -0.0023
##    480        0.0791             nan     0.5000   -0.0020
##    500        0.0720             nan     0.5000   -0.0006
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1348             nan     0.5000    0.0780
##      2        1.0455             nan     0.5000    0.0160
##      3        0.9734             nan     0.5000    0.0275
##      4        0.9419             nan     0.5000   -0.0042
##      5        0.9274             nan     0.5000   -0.0024
##      6        0.9188             nan     0.5000   -0.0077
##      7        0.9051             nan     0.5000   -0.0066
##      8        0.8895             nan     0.5000   -0.0072
##      9        0.8791             nan     0.5000   -0.0085
##     10        0.8651             nan     0.5000   -0.0034
##     20        0.8122             nan     0.5000   -0.0113
##     40        0.7186             nan     0.5000   -0.0083
##     60        0.6103             nan     0.5000   -0.0097
##     80        0.4924             nan     0.5000   -0.0026
##    100        0.4337             nan     0.5000   -0.0003
##    120        0.3949             nan     0.5000   -0.0068
##    140        0.3549             nan     0.5000   -0.0022
##    160        0.3242             nan     0.5000   -0.0019
##    180        0.3026             nan     0.5000   -0.0063
##    200        0.2727             nan     0.5000   -0.0029
##    220        0.2470             nan     0.5000    0.0003
##    240        0.2219             nan     0.5000   -0.0012
##    260        0.1986             nan     0.5000   -0.0020
##    280        0.1876             nan     0.5000   -0.0056
##    300        0.1672             nan     0.5000   -0.0027
##    320        0.1573             nan     0.5000   -0.0025
##    340        0.1418             nan     0.5000   -0.0002
##    360        0.1330             nan     0.5000   -0.0019
##    380        0.1250             nan     0.5000   -0.0023
##    400        0.1152             nan     0.5000   -0.0015
##    420        0.1057             nan     0.5000   -0.0017
##    440        0.1009             nan     0.5000   -0.0005
##    460        0.0962             nan     0.5000   -0.0011
##    480        0.0847             nan     0.5000   -0.0010
##    500        0.0789             nan     0.5000   -0.0004
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0929             nan     0.5000    0.1011
##      2        0.9986             nan     0.5000    0.0369
##      3        0.9478             nan     0.5000    0.0165
##      4        0.9031             nan     0.5000    0.0165
##      5        0.8731             nan     0.5000    0.0028
##      6        0.8623             nan     0.5000   -0.0130
##      7        0.8497             nan     0.5000    0.0004
##      8        0.8490             nan     0.5000   -0.0217
##      9        0.8344             nan     0.5000   -0.0066
##     10        0.8277             nan     0.5000   -0.0217
##     20        2.0521             nan     0.5000   -0.0061
##     40        1.9703             nan     0.5000   -0.0084
##     60        1.9966             nan     0.5000   -0.0035
##     80           inf             nan     0.5000       nan
##    100           inf             nan     0.5000       nan
##    120           inf             nan     0.5000       nan
##    140           inf             nan     0.5000   -0.0003
##    160           inf             nan     0.5000       nan
##    180           inf             nan     0.5000       nan
##    200           inf             nan     0.5000       nan
##    220           inf             nan     0.5000       nan
##    240           inf             nan     0.5000       nan
##    260           inf             nan     0.5000   -0.0018
##    280           inf             nan     0.5000   -0.0021
##    300           inf             nan     0.5000       nan
##    320           inf             nan     0.5000       nan
##    340           inf             nan     0.5000       nan
##    360           inf             nan     0.5000       nan
##    380           inf             nan     0.5000       nan
##    400           inf             nan     0.5000       nan
##    420           inf             nan     0.5000       nan
##    440           inf             nan     0.5000       nan
##    460           inf             nan     0.5000       nan
##    480           inf             nan     0.5000       nan
##    500           inf             nan     0.5000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0900             nan     0.5000    0.0780
##      2        0.9992             nan     0.5000    0.0272
##      3        0.9566             nan     0.5000   -0.0037
##      4        0.9180             nan     0.5000   -0.0033
##      5        0.8913             nan     0.5000   -0.0120
##      6        0.8562             nan     0.5000    0.0030
##      7        0.8299             nan     0.5000    0.0056
##      8        0.8093             nan     0.5000   -0.0042
##      9        0.8086             nan     0.5000   -0.0201
##     10        0.7909             nan     0.5000   -0.0148
##     20        0.6908             nan     0.5000   -0.0152
##     40        0.5607             nan     0.5000   -0.0031
##     60        0.4558             nan     0.5000   -0.0098
##     80        0.3762             nan     0.5000   -0.0005
##    100        0.2808             nan     0.5000   -0.0052
##    120        0.2458             nan     0.5000   -0.0036
##    140        0.2021             nan     0.5000   -0.0053
##    160        0.1688             nan     0.5000   -0.0017
##    180        0.1388             nan     0.5000   -0.0041
##    200        0.1198             nan     0.5000   -0.0023
##    220        0.1080             nan     0.5000   -0.0016
##    240        0.0955             nan     0.5000   -0.0017
##    260        0.0848             nan     0.5000   -0.0014
##    280        0.0722             nan     0.5000   -0.0009
##    300        0.0625             nan     0.5000   -0.0006
##    320        0.0534             nan     0.5000   -0.0006
##    340        0.0463             nan     0.5000   -0.0010
##    360        0.0426             nan     0.5000   -0.0010
##    380        0.0384             nan     0.5000   -0.0011
##    400        0.0342             nan     0.5000   -0.0005
##    420        0.0297             nan     0.5000   -0.0005
##    440        0.0262             nan     0.5000   -0.0005
##    460        0.0237             nan     0.5000   -0.0002
##    480        0.0205             nan     0.5000   -0.0001
##    500        0.0181             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0956             nan     0.5000    0.0917
##      2        1.0129             nan     0.5000    0.0306
##      3        0.9560             nan     0.5000   -0.0006
##      4        0.9266             nan     0.5000   -0.0025
##      5        0.8937             nan     0.5000    0.0052
##      6        0.8676             nan     0.5000    0.0036
##      7        0.8592             nan     0.5000   -0.0140
##      8        0.8471             nan     0.5000   -0.0153
##      9        0.8305             nan     0.5000    0.0028
##     10        0.8211             nan     0.5000   -0.0107
##     20        0.7027             nan     0.5000   -0.0106
##     40        0.5544             nan     0.5000   -0.0136
##     60        0.4612             nan     0.5000   -0.0154
##     80        0.3854             nan     0.5000   -0.0065
##    100        0.3151             nan     0.5000   -0.0082
##    120        0.2629             nan     0.5000   -0.0042
##    140        0.2168             nan     0.5000   -0.0034
##    160        0.1790             nan     0.5000   -0.0026
##    180        0.1593             nan     0.5000   -0.0086
##    200        0.1214             nan     0.5000   -0.0007
##    220        0.1071             nan     0.5000   -0.0004
##    240        0.0919             nan     0.5000   -0.0017
##    260        0.0801             nan     0.5000   -0.0011
##    280        0.0704             nan     0.5000   -0.0014
##    300        0.0617             nan     0.5000   -0.0014
##    320        0.0541             nan     0.5000   -0.0011
##    340        0.0464             nan     0.5000   -0.0004
##    360        0.0407             nan     0.5000   -0.0013
##    380        0.0355             nan     0.5000   -0.0008
##    400        0.0325             nan     0.5000   -0.0007
##    420        0.0278             nan     0.5000   -0.0002
##    440        0.0242             nan     0.5000   -0.0001
##    460        0.0218             nan     0.5000   -0.0003
##    480        0.0189             nan     0.5000   -0.0001
##    500        0.0165             nan     0.5000   -0.0002
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1274             nan     1.0000    0.0668
##      2        1.0444             nan     1.0000    0.0243
##      3        0.9864             nan     1.0000    0.0314
##      4        0.9906             nan     1.0000   -0.0257
##      5        1.0041             nan     1.0000   -0.0395
##      6        0.9816             nan     1.0000   -0.0009
##      7        0.9592             nan     1.0000   -0.0018
##      8        0.9448             nan     1.0000   -0.0004
##      9        0.9162             nan     1.0000   -0.0065
##     10        0.9088             nan     1.0000   -0.0030
##     20        0.8341             nan     1.0000   -0.0115
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1225             nan     1.0000    0.0700
##      2        1.0639             nan     1.0000    0.0229
##      3        1.0269             nan     1.0000    0.0026
##      4        0.9816             nan     1.0000    0.0175
##      5        0.9747             nan     1.0000   -0.0151
##      6        0.9827             nan     1.0000   -0.0326
##      7        0.9457             nan     1.0000    0.0177
##      8        0.9485             nan     1.0000   -0.0239
##      9        0.9390             nan     1.0000   -0.0054
##     10        0.9301             nan     1.0000   -0.0039
##     20        0.9235             nan     1.0000   -0.0056
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120  3522651.0612             nan     1.0000   -0.0300
##    140  3522651.0713             nan     1.0000   -0.0061
##    160  3522651.0424             nan     1.0000   -0.0043
##    180  3522651.0536             nan     1.0000   -0.0514
##    200  3522651.1591             nan     1.0000    0.0018
##    220  3522652.6901             nan     1.0000    0.0010
##    240  3522652.7094             nan     1.0000   -0.0003
##    260  3522674.2908             nan     1.0000   -0.0074
##    280  3522674.2368             nan     1.0000   -0.0145
##    300  3522674.2328             nan     1.0000   -0.0209
##    320  3522674.2388             nan     1.0000   -0.0187
##    340  3522674.2313             nan     1.0000   -0.0020
##    360           inf             nan     1.0000      -inf
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.1284             nan     1.0000    0.0728
##      2        1.0595             nan     1.0000    0.0334
##      3        1.0313             nan     1.0000   -0.0043
##      4        0.9954             nan     1.0000    0.0063
##      5        0.9947             nan     1.0000   -0.0326
##      6        0.9895             nan     1.0000   -0.0098
##      7        0.9634             nan     1.0000   -0.0007
##      8        0.9561             nan     1.0000   -0.0199
##      9        0.9311             nan     1.0000   -0.0128
##     10        0.9263             nan     1.0000   -0.0143
##     20        0.8819             nan     1.0000   -0.0076
##     40        0.9553             nan     1.0000   -0.0296
##     60       24.4869             nan     1.0000    0.0017
##     80       24.4567             nan     1.0000   -0.0179
##    100       24.4407             nan     1.0000   -0.0007
##    120       24.4387             nan     1.0000   -0.0011
##    140       24.3075             nan     1.0000    0.0022
##    160       24.2928             nan     1.0000   -0.0046
##    180       24.2947             nan     1.0000   -0.0000
##    200       24.2603             nan     1.0000    0.0001
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0918             nan     1.0000    0.0904
##      2        0.9769             nan     1.0000    0.0383
##      3        0.9534             nan     1.0000   -0.0172
##      4        0.9738             nan     1.0000   -0.0464
##      5        0.9840             nan     1.0000   -0.0400
##      6        0.9531             nan     1.0000   -0.0196
##      7        0.9638             nan     1.0000   -0.0274
##      8        0.9717             nan     1.0000   -0.0551
##      9        0.9424             nan     1.0000   -0.0063
##     10        0.9516             nan     1.0000   -0.0389
##     20        0.9084             nan     1.0000   -0.0360
##     40        1.2785             nan     1.0000   -0.0399
##     60        1.4842             nan     1.0000   -0.0362
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0604             nan     1.0000    0.0983
##      2        0.9714             nan     1.0000    0.0148
##      3        0.9722             nan     1.0000   -0.0366
##      4        0.9438             nan     1.0000   -0.0062
##      5        0.9718             nan     1.0000   -0.0620
##      6        0.9398             nan     1.0000    0.0081
##      7        0.9712             nan     1.0000   -0.0351
##      8           inf             nan     1.0000      -inf
##      9           inf             nan     1.0000       nan
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0810             nan     1.0000    0.0672
##      2        1.0184             nan     1.0000   -0.0102
##      3        0.9766             nan     1.0000   -0.0008
##      4        0.9792             nan     1.0000   -0.0383
##      5        0.9711             nan     1.0000   -0.0246
##      6        0.9772             nan     1.0000   -0.0400
##      7        0.9821             nan     1.0000   -0.0452
##      8        0.9332             nan     1.0000    0.0075
##      9        0.9356             nan     1.0000   -0.0366
##     10        0.9420             nan     1.0000   -0.0347
##     20        1.3683             nan     1.0000   -0.0343
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000      -inf
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           nan             nan     1.0000       nan
##    160           nan             nan     1.0000       nan
##    180           nan             nan     1.0000       nan
##    200           nan             nan     1.0000       nan
##    220           nan             nan     1.0000       nan
##    240           nan             nan     1.0000       nan
##    260           nan             nan     1.0000       nan
##    280           nan             nan     1.0000       nan
##    300           nan             nan     1.0000       nan
##    320           nan             nan     1.0000       nan
##    340           nan             nan     1.0000       nan
##    360           nan             nan     1.0000       nan
##    380           nan             nan     1.0000       nan
##    400           nan             nan     1.0000       nan
##    420           nan             nan     1.0000       nan
##    440           nan             nan     1.0000       nan
##    460           nan             nan     1.0000       nan
##    480           nan             nan     1.0000       nan
##    500           nan             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0321             nan     1.0000    0.1085
##      2        0.9560             nan     1.0000    0.0085
##      3        0.9344             nan     1.0000   -0.0382
##      4        0.9312             nan     1.0000   -0.0421
##      5        0.9199             nan     1.0000   -0.0307
##      6        0.9506             nan     1.0000   -0.0686
##      7        0.9248             nan     1.0000   -0.0100
##      8        0.8766             nan     1.0000    0.0134
##      9        0.8583             nan     1.0000   -0.0122
##     10        0.8461             nan     1.0000   -0.0315
##     20      530.1265             nan     1.0000   -0.0271
##     40      844.6416             nan     1.0000   -0.0488
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0721             nan     1.0000    0.0754
##      2        0.9964             nan     1.0000   -0.0152
##      3        0.9344             nan     1.0000    0.0141
##      4        0.9175             nan     1.0000   -0.0228
##      5        0.9431             nan     1.0000   -0.0613
##      6        0.9363             nan     1.0000   -0.0245
##      7        0.8731             nan     1.0000    0.0139
##      8        0.8417             nan     1.0000    0.0025
##      9        0.8310             nan     1.0000   -0.0265
##     10        0.8105             nan     1.0000   -0.0161
##     20        0.7402             nan     1.0000   -0.0042
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.0655             nan     1.0000    0.0576
##      2        1.0120             nan     1.0000   -0.0149
##      3        0.9668             nan     1.0000   -0.0104
##      4        0.9670             nan     1.0000   -0.0357
##      5        0.9118             nan     1.0000   -0.0219
##      6        0.8692             nan     1.0000    0.0016
##      7        1.1822             nan     1.0000   -0.3038
##      8           inf             nan     1.0000      -inf
##      9           inf             nan     1.0000       nan
##     10           inf             nan     1.0000       nan
##     20           inf             nan     1.0000       nan
##     40           inf             nan     1.0000       nan
##     60           inf             nan     1.0000       nan
##     80           inf             nan     1.0000       nan
##    100           inf             nan     1.0000       nan
##    120           inf             nan     1.0000       nan
##    140           inf             nan     1.0000       nan
##    160           inf             nan     1.0000       nan
##    180           inf             nan     1.0000       nan
##    200           inf             nan     1.0000       nan
##    220           inf             nan     1.0000       nan
##    240           inf             nan     1.0000       nan
##    260           inf             nan     1.0000       nan
##    280           inf             nan     1.0000       nan
##    300           inf             nan     1.0000       nan
##    320           inf             nan     1.0000       nan
##    340           inf             nan     1.0000       nan
##    360           inf             nan     1.0000       nan
##    380           inf             nan     1.0000       nan
##    400           inf             nan     1.0000       nan
##    420           inf             nan     1.0000       nan
##    440           inf             nan     1.0000       nan
##    460           inf             nan     1.0000       nan
##    480           inf             nan     1.0000       nan
##    500           inf             nan     1.0000       nan
## 
## Iter   TrainDeviance   ValidDeviance   StepSize   Improve
##      1        1.2568             nan     0.1000    0.0167
##      2        1.2269             nan     0.1000    0.0135
##      3        1.2008             nan     0.1000    0.0092
##      4        1.1803             nan     0.1000    0.0097
##      5        1.1633             nan     0.1000    0.0069
##      6        1.1467             nan     0.1000    0.0074
##      7        1.1288             nan     0.1000    0.0064
##      8        1.1172             nan     0.1000    0.0038
##      9        1.1032             nan     0.1000    0.0062
##     10        1.0917             nan     0.1000    0.0055
##     20        1.0039             nan     0.1000    0.0014
##     40        0.9210             nan     0.1000    0.0007
##     60        0.8850             nan     0.1000   -0.0016
##     80        0.8592             nan     0.1000   -0.0002
##    100        0.8412             nan     0.1000   -0.0003
plot(fit.final3)

print(fit.final3)
## Stochastic Gradient Boosting 
## 
## 768 samples
##   8 predictor
##   2 classes: 'neg', 'pos' 
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times) 
## Summary of sample sizes: 692, 691, 691, 691, 691, 692, ... 
## Resampling results across tuning parameters:
## 
##   shrinkage  interaction.depth  n.minobsinnode  n.trees  Accuracy 
##   0.000      1                  1               100      0.6510595
##   0.000      1                  1               250      0.6510595
##   0.000      1                  1               500      0.6510595
##   0.000      1                  2               100      0.6510595
##   0.000      1                  2               250      0.6510595
##   0.000      1                  2               500      0.6510595
##   0.000      1                  3               100      0.6510595
##   0.000      1                  3               250      0.6510595
##   0.000      1                  3               500      0.6510595
##   0.000      2                  1               100      0.6510595
##   0.000      2                  1               250      0.6510595
##   0.000      2                  1               500      0.6510595
##   0.000      2                  2               100      0.6510595
##   0.000      2                  2               250      0.6510595
##   0.000      2                  2               500      0.6510595
##   0.000      2                  3               100      0.6510595
##   0.000      2                  3               250      0.6510595
##   0.000      2                  3               500      0.6510595
##   0.000      3                  1               100      0.6510595
##   0.000      3                  1               250      0.6510595
##   0.000      3                  1               500      0.6510595
##   0.000      3                  2               100      0.6510595
##   0.000      3                  2               250      0.6510595
##   0.000      3                  2               500      0.6510595
##   0.000      3                  3               100      0.6510595
##   0.000      3                  3               250      0.6510595
##   0.000      3                  3               500      0.6510595
##   0.001      1                  1               100      0.6510595
##   0.001      1                  1               250      0.6510595
##   0.001      1                  1               500      0.6519253
##   0.001      1                  2               100      0.6510595
##   0.001      1                  2               250      0.6510595
##   0.001      1                  2               500      0.6519253
##   0.001      1                  3               100      0.6510595
##   0.001      1                  3               250      0.6510595
##   0.001      1                  3               500      0.6514924
##   0.001      2                  1               100      0.6510595
##   0.001      2                  1               250      0.6510595
##   0.001      2                  1               500      0.7174356
##   0.001      2                  2               100      0.6510595
##   0.001      2                  2               250      0.6510595
##   0.001      2                  2               500      0.7196115
##   0.001      2                  3               100      0.6510595
##   0.001      2                  3               250      0.6510595
##   0.001      2                  3               500      0.7178913
##   0.001      3                  1               100      0.6510595
##   0.001      3                  1               250      0.6510595
##   0.001      3                  1               500      0.7317783
##   0.001      3                  2               100      0.6510595
##   0.001      3                  2               250      0.6510595
##   0.001      3                  2               500      0.7330884
##   0.001      3                  3               100      0.6510595
##   0.001      3                  3               250      0.6510595
##   0.001      3                  3               500      0.7317897
##   0.100      1                  1               100      0.7682388
##   0.100      1                  1               250      0.7613124
##   0.100      1                  1               500      0.7587378
##   0.100      1                  2               100      0.7630554
##   0.100      1                  2               250      0.7565334
##   0.100      1                  2               500      0.7600478
##   0.100      1                  3               100      0.7643541
##   0.100      1                  3               250      0.7652313
##   0.100      1                  3               500      0.7591650
##   0.100      2                  1               100      0.7638984
##   0.100      2                  1               250      0.7600080
##   0.100      2                  1               500      0.7526601
##   0.100      2                  2               100      0.7652313
##   0.100      2                  2               250      0.7674072
##   0.100      2                  2               500      0.7595865
##   0.100      2                  3               100      0.7587207
##   0.100      2                  3               250      0.7539417
##   0.100      2                  3               500      0.7526487
##   0.100      3                  1               100      0.7513500
##   0.100      3                  1               250      0.7474653
##   0.100      3                  1               500      0.7539645
##   0.100      3                  2               100      0.7652654
##   0.100      3                  2               250      0.7492196
##   0.100      3                  2               500      0.7461552
##   0.100      3                  3               100      0.7621839
##   0.100      3                  3               250      0.7543803
##   0.100      3                  3               500      0.7439508
##   0.200      1                  1               100      0.7617738
##   0.200      1                  1               250      0.7583049
##   0.200      1                  1               500      0.7570175
##   0.200      1                  2               100      0.7582764
##   0.200      1                  2               250      0.7626396
##   0.200      1                  2               500      0.7535373
##   0.200      1                  3               100      0.7586865
##   0.200      1                  3               250      0.7600194
##   0.200      1                  3               500      0.7491798
##   0.200      2                  1               100      0.7578663
##   0.200      2                  1               250      0.7435236
##   0.200      2                  1               500      0.7370187
##   0.200      2                  2               100      0.7526373
##   0.200      2                  2               250      0.7518057
##   0.200      2                  2               500      0.7431533
##   0.200      2                  3               100      0.7574220
##   0.200      2                  3               250      0.7531157
##   0.200      2                  3               500      0.7396389
##   0.200      3                  1               100      0.7574391
##   0.200      3                  1               250      0.7435578
##   0.200      3                  1               500      0.7461552
##   0.200      3                  2               100      0.7535429
##   0.200      3                  2               250      0.7426920
##   0.200      3                  2               500      0.7404933
##   0.200      3                  3               100      0.7530531
##   0.200      3                  3               250      0.7535543
##   0.200      3                  3               500      0.7474766
##   0.300      1                  1               100      0.7552461
##   0.300      1                  1               250      0.7561688
##   0.300      1                  1               500      0.7413762
##   0.300      1                  2               100      0.7608681
##   0.300      1                  2               250      0.7505069
##   0.300      1                  2               500      0.7392458
##   0.300      1                  3               100      0.7538961
##   0.300      1                  3               250      0.7478811
##   0.300      1                  3               500      0.7409376
##   0.300      2                  1               100      0.7496070
##   0.300      2                  1               250      0.7335669
##   0.300      2                  1               500      0.7374687
##   0.300      2                  2               100      0.7465710
##   0.300      2                  2               250      0.7431476
##   0.300      2                  2               500      0.7275120
##   0.300      2                  3               100      0.7491171
##   0.300      2                  3               250      0.7434666
##   0.300      2                  3               500      0.7257006
##   0.300      3                  1               100      0.7426293
##   0.300      3                  1               250      0.7270506
##   0.300      3                  1               500      0.7227102
##   0.300      3                  2               100      0.7464969
##   0.300      3                  2               250      0.7352586
##   0.300      3                  2               500      0.7300752
##   0.300      3                  3               100      0.7465539
##   0.300      3                  3               250      0.7283778
##   0.300      3                  3               500      0.7357883
##   0.500      1                  1               100      0.7495842
##   0.500      1                  1               250      0.7439565
##   0.500      1                  1               500      0.7266120
##   0.500      1                  2               100      0.7470153
##   0.500      1                  2               250      0.7392458
##   0.500      1                  2               500      0.7313568
##   0.500      1                  3               100      0.7491684
##   0.500      1                  3               250      0.7366200
##   0.500      1                  3               500      0.7231146
##   0.500      2                  1               100      0.7366029
##   0.500      2                  1               250      0.7222488
##   0.500      2                  1               500      0.7253304
##   0.500      2                  2               100      0.7318410
##   0.500      2                  2               250      0.7122921
##   0.500      2                  2               500      0.7083846
##   0.500      2                  3               100      0.7348713
##   0.500      2                  3               250      0.7309410
##   0.500      2                  3               500      0.7183641
##   0.500      3                  1               100      0.7274778
##   0.500      3                  1               250      0.7253133
##   0.500      3                  1               500      0.7205058
##   0.500      3                  2               100      0.7248291
##   0.500      3                  2               250      0.7322739
##   0.500      3                  2               500      0.7235931
##   0.500      3                  3               100      0.7235589
##   0.500      3                  3               250      0.7353042
##   0.500      3                  3               500      0.7365915
##   1.000      1                  1               100      0.7283607
##   1.000      1                  1               250      0.7252563
##   1.000      1                  1               500      0.7048644
##   1.000      1                  2               100      0.7210014
##   1.000      1                  2               250      0.6988950
##   1.000      1                  2               500      0.7122750
##   1.000      1                  3               100      0.7348143
##   1.000      1                  3               250      0.7278879
##   1.000      1                  3               500      0.7205400
##   1.000      2                  1               100      0.7009171
##   1.000      2                  1               250      0.6814308
##   1.000      2                  1               500      0.6810207
##   1.000      2                  2               100      0.7061859
##   1.000      2                  2               250      0.6866655
##   1.000      2                  2               500      0.6650319
##   1.000      2                  3               100      0.6931590
##   1.000      2                  3               250      0.6713545
##   1.000      2                  3               500      0.6401059
##   1.000      3                  1               100      0.6870529
##   1.000      3                  1               250      0.6302119
##   1.000      3                  1               500      0.5989633
##   1.000      3                  2               100      0.6797505
##   1.000      3                  2               250      0.6223684
##   1.000      3                  2               500      0.5586238
##   1.000      3                  3               100      0.6953976
##   1.000      3                  3               250      0.6454545
##   1.000      3                  3               500      0.6144851
##   Kappa      
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.000000000
##   0.004390317
##   0.000000000
##   0.000000000
##   0.004978642
##   0.000000000
##   0.000000000
##   0.002917046
##   0.000000000
##   0.000000000
##   0.252321850
##   0.000000000
##   0.000000000
##   0.259578129
##   0.000000000
##   0.000000000
##   0.253945259
##   0.000000000
##   0.000000000
##   0.298769389
##   0.000000000
##   0.000000000
##   0.303930320
##   0.000000000
##   0.000000000
##   0.299331950
##   0.464360321
##   0.454745945
##   0.452141053
##   0.450092366
##   0.441678101
##   0.453812047
##   0.454786591
##   0.462739389
##   0.450789646
##   0.457166002
##   0.456687378
##   0.443288814
##   0.462619174
##   0.473775037
##   0.460954847
##   0.447015450
##   0.442777807
##   0.443257863
##   0.433250519
##   0.429626995
##   0.451450779
##   0.463344822
##   0.437025142
##   0.431338943
##   0.455951346
##   0.445797906
##   0.427159253
##   0.452072994
##   0.448267786
##   0.447277667
##   0.444753899
##   0.459462146
##   0.442820365
##   0.447341473
##   0.453977096
##   0.429263440
##   0.449160984
##   0.420734882
##   0.411697929
##   0.436905272
##   0.443001321
##   0.426431832
##   0.444679268
##   0.447809295
##   0.419444694
##   0.453687513
##   0.428878951
##   0.434882942
##   0.441584422
##   0.427116043
##   0.422852320
##   0.446400014
##   0.446262518
##   0.434894552
##   0.442555631
##   0.447901777
##   0.414274682
##   0.454343398
##   0.435488084
##   0.415785006
##   0.439227340
##   0.427344073
##   0.414843577
##   0.435570092
##   0.405110975
##   0.416055791
##   0.427139739
##   0.423569663
##   0.395293831
##   0.433302599
##   0.422428343
##   0.390076392
##   0.422988507
##   0.391125182
##   0.381730704
##   0.428721177
##   0.409240018
##   0.397521940
##   0.433454187
##   0.399000541
##   0.413708441
##   0.431597841
##   0.421064257
##   0.387124460
##   0.421206989
##   0.410982003
##   0.399048009
##   0.430993272
##   0.404826108
##   0.379864330
##   0.408068705
##   0.381646949
##   0.389810503
##   0.399556764
##   0.352537007
##   0.342796110
##   0.407429731
##   0.401992243
##   0.376600816
##   0.389859809
##   0.390010824
##   0.383709225
##   0.386357169
##   0.400844865
##   0.384741090
##   0.387685764
##   0.410572575
##   0.413865225
##   0.389312332
##   0.380412123
##   0.338448580
##   0.372647201
##   0.327124724
##   0.355411420
##   0.400172364
##   0.385065375
##   0.372467612
##   0.335446860
##   0.299474290
##   0.295327902
##   0.350465617
##   0.301533773
##   0.278394261
##   0.320298557
##   0.284257487
##   0.222276054
##   0.305742036
##   0.235065021
##   0.196306555
##   0.292246106
##   0.190793734
##   0.107683185
##   0.327450994
##   0.234090613
##   0.185768299
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 100,
##  interaction.depth = 1, shrinkage = 0.1 and n.minobsinnode = 1.

5.d) Compare Algorithms After Tuning

results <- resamples(list(LogReg=fit.final1, LDA=fit.final2, GBM=fit.final3))
summary(results)
## 
## Call:
## summary.resamples(object = results)
## 
## Models: LogReg, LDA, GBM 
## Number of resamples: 30 
## 
## Accuracy 
##             Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
## LogReg 0.6883117 0.7532468 0.7843472 0.7773354 0.8000256 0.8441558    0
## LDA    0.6883117 0.7662338 0.7792208 0.7760367 0.8000256 0.8311688    0
## GBM    0.6883117 0.7508117 0.7712748 0.7682388 0.7922078 0.8441558    0
## 
## Kappa 
##             Min.   1st Qu.    Median      Mean   3rd Qu.      Max. NA's
## LogReg 0.2655008 0.4401342 0.4943168 0.4843861 0.5346588 0.6393443    0
## LDA    0.2786885 0.4425230 0.4897297 0.4802587 0.5346588 0.6057503    0
## GBM    0.2376238 0.4143031 0.4685583 0.4643603 0.5344289 0.6393443    0
dotplot(results)

6. Finalize Model and Present Results

Once we have narrow down to a model that we believe can make accurate predictions on unseen data, we are ready to finalize it. Finalizing a model may involve sub-tasks such as:

For this particular dataset, we will settle on using the SVM algorithm with a sigma value of 0.01.

6.a) Predictions on validation dataset

# Not applicable for this iteration of the project.

6.b) Create standalone model on entire training dataset

# Not applicable for this iteration of the project.

6.c) Save model for later use

# Not applicable for this iteration of the project.